text:
\section{Introduction} \label{sec:introduction}

With the explosion in availability of satellite and aerial imagery over the past decades, there has been increasing interest in the use of imagery in remote sensing (RS) applications. These applications range from precision agriculture~\citep{MULLA2013358} and forestry~\citep{lu2016survey}, to natural and man-made disaster monitoring~\citep{van2013remote}, to weather and climate change~\citep{DBLP:journals/corr/abs-1906-05433}. At the same time, advancements in machine learning (ML), larger curated benchmark datasets, and increased compute power, have led to great successes in domains like computer vision (CV), natural language processing (NLP), and audio processing. However, the wide-spread success and popularity of machine learning---particularly of deep learning methods---in these domains has not fully transferred to the RS domain, despite the existence of petabytes of freely available satellite imagery and a variety of benchmark datasets for different RS tasks. This is not to say that there are not successful applications of ML in RS, but that the full potential of the intersection of these fields has not been reached. Indeed, a recent book by \citet{camps2021deep} thoroughly details work at the intersection of deep learning, geospatial data, and the Earth sciences. Increasing amounts of research on self-supervised and unsupervised learning methods specific to remotely sensed geospatial imagery~\citep{manas2021seasonal,ayush2020geography,jean2019tile2vec} bring the promise of developing generic models that can be tuned to various downstream RS tasks. Recent large scale efforts, such as the creation of a global 10~m resolution land cover map~\citep{karra2021global} or the creation of global 30~m forest maps~\citep{rogan2008mapping}, pair the huge amount of available remotely sensed imagery with modern GPU accelerated models. To reach the full joint potential of these fields, we believe that we need tools for facilitating research and managing the complexities of both geospatial data and modern machine learning pipelines. We describe the challenges of this below, and detail our proposed solution, TorchGeo.

\begin{figure}[t]
    \centering
    \includegraphics[width=0.8\linewidth]{figures/fig1.png}
    \caption{An illustration of the challenges in sampling from heterogeneous geospatial data layers. (\textbf{A} and \textbf{B}) show example geospatial data layers that a user may want to sample pixel-aligned data from. As these layers have differing coordinate reference systems, patches of imagery (\textbf{C} and \textbf{D}) sampled from these layers that cover the same area will not be pixel-aligned. TorchGeo transparently performs the appropriate alignment steps (reprojecting and resampling) during data loading such that users can train deep learning models without having to manually align the data layers.}
    \label{fig:main}
\end{figure}

One major challenge in many RS tasks is the large amount of diversity in the content of geospatial imagery datasets compared to datasets collected for traditional vision applications. For example, most conventional cameras capture 3-channel RGB imagery, however most satellite platforms capture different sets of spectral bands. The Landsat 8 satellite~\citep{roy2014landsat} collects 11 bands, the Sentinel-2 satellites~\citep{drusch2012sentinel} collect 12 bands, and the Hyperion satellite~\citep{pearlman2003hyperion} collects 242 (hyperspectral) bands all measuring different regions of the electromagnetic spectrum. The exact wavelengths of the electromagnetic spectrum captured by each band can range from 400~nm to 15~$\mu$m. In addition, different sensors capture imagery at different spatial resolutions: satellite imagery can range from 4~km/px (GOES) to 30~cm/px (Maxar WorldView satellites), while imagery captured from drones can go as low as 7~mm/px. Depending on the type of orbit a satellite is in, imagery can be continuous (for geostationary orbits) or daily to biweekly (for polar, sun-synchronous orbits). Machine learning models or algorithms developed for one of these platforms will not generalize across inputs collected by the others, and, as a consequence of this, it is not possible to publish a single set of pre-trained model weights that span imaging platforms. In contrast, ImageNet~\citep{deng2009imagenet} pretrained models have been proven to be useful in a large number of transfer learning tasks~\citep{yosinski2014transferable}. Researchers and practitioners can often start with ImageNet pre-trained models in a transfer learning setup when presented with vision problems to reduce the overall amount of effort needed to solve the problem. Further, it isn't clear whether the inductive biases built into common modeling approaches for vision problems are immediately applicable to remotely sensed imagery. Large neural architecture search efforts~\citep{liu2018progressive,zoph2016neural} produce models that are optimized for, and indeed, outperform hand designed architectures on vision tasks, but it is an open question whether these transfer to remotely sensed imagery.

Most machine learning libraries have not been designed to work with geospatial data. For example, the Python Imaging Library (PIL)~\citep{pillow}, used by many libraries to load images and perform data augmentation, does not support multispectral imagery. Similarly, deep learning models implemented by the torchvision library only support 3 channel (RGB) inputs, and must be adapted, or re-implemented to support multispectral data. Datasets of geospatial data can be made up of a heterogenous mix of files with differing file formats, spatial resolutions, projections, and coordinate reference systems (CRS). Libraries such as rasterio~\citep{gillies2013rasterio} and fiona~\citep{fiona} can interface with most types of geospatial data, however further abstractions for using such data in arbitrary deep learning pipelines are limited. Indeed, the gap between loading geospatial data from disk, and using it in a modeling pipeline, is large for all of the reasons mentioned above. As illustrated in Figure~\ref{fig:main}, users will often need pixel-aligned crops from multiple layers of data: imagery from different points in time over the same space, imagery and corresponding label masks, high-resolution and low resolution imagery from the same space, etc.
In contrast, there are a wide variety of software libraries at the intersection of machine learning and other domains. The PyTorch ecosystem alone has torchvision~\citep{torchvision}, torchtext~\citep{torchtext}, torchaudio~\citep{torchaudio}, Hugging Face's Transformers~\citep{wolf2019huggingface}, PyTorch Geometric~\citep{fey2019fast}, PyTorch Meta~\citep{deleu2019torchmeta}, and PyTorch Video~\citep{fan2021pytorchvideo}, that provide the tools necessary for abstracting the peculiarities of domain-specific data away from the details of deep learning training pipelines.

\begin{figure}[t]
    \centering
    \includegraphics[width=1\linewidth]{figures/warping-example.png}
    \caption{Different layers of geospatial data often have differing coordinate reference systems and spatial resolutions. (\textbf{Top row}) The same physical area cropped from four raster layers with different coordinate reference systems and spatial resolutions---this data is not pixel-aligned and cannot yet be used in modelling pipelines. (\textbf{Bottom row}) The same data as after reprojecting into the same coordinate system and resampling to the highest spatial resolution---this data is pixel aligned and can serve as inputs or masks to deep neural networks.}
    \label{fig:warping-example}
\end{figure}

To address these challenges we propose TorchGeo, a Python package that allows users to transparently use heterogenous geospatial data in PyTorch-based deep learning pipelines. Specifically, TorchGeo provides:
\begin{enumerate}
    \item data loaders for common geospatial datasets from the literature
    \item data loaders for combining arbitrary geospatial raster and vector data layers with the ability to sample pixel-aligned patches on-the-fly
    \item augmentations that are appropriate for multispectral imagery and geospatial data
    \item data samplers appropriate for geospatial data
    \item pre-trained models for many common remotely sensed imagery sources
\end{enumerate}
We formally describe TorchGeo, propose and test methods for sampling from large geospatial datasets, and test the effect of ImageNet pretraining versus random weight initialization on several benchmark datasets. We achieve close to state of the art results on all experimental datasets, despite focusing only on creating simple and reproducible results to serve as baselines for future work to build on. We further find that ImageNet pre-training significantly improves spatial generalization performance in a land cover mapping task. We believe that these results are interesting in their own right, and that they highlight the importance of TorchGeo to the larger machine learning community. TorchGeo is open-source and available on GitHub: \url{https://github.com/microsoft/torchgeo}.

\section{Design} \label{sec:design}

Remotely sensed imagery datasets are usually formatted as \textit{scenes}, i.e. tensors $X \in \mathbb{R}^{H \times W \times C}$, where $H$ is height, $W$ is width, and $C$ is the number of spectral channels, with corresponding spatial and temporal metadata. This metadata includes a \textit{coordinate reference system} (CRS) that maps pixel coordinates to the surface of the Earth, a \textit{spatial resolution} (the size of each pixel when mapped onto the surface of the Earth), \textit{spatial bounds} (a bounding box representing the area on Earth that the data covers), and a \textit{timestamp} or time range to indicate when the data was collected. We say that two datasets, $X^1$ and $X^2$ are \textit{pixel-aligned} if $X^1_{i,j}$ and $X^2_{i,j}$ represent data from the same positions on Earth for all $i,j$. Most pairs of datasets are not aligned by default. For example, $X^1$ and $X^2$ can be captured by two satellites in different orbits and will only have partially overlapping spatial bounds, or $X^1$ will be satellite imagery while $X^2$ will be from a dataset of labels with a different CRS (see Figure \ref{fig:warping-example}). However, deep learning model training requires pixel-aligned patches of imagery---i.e. smaller crops from large scenes. Most models are trained with mini-batch gradient descent and require input tensors in the format $B \times H \times W \times C$ where $B$ is the number of samples in a mini-batch and $W$ and $H$ are constant over all samples in the batch. At a higher level, training semantic segmentation models requires pairs of pixel-aligned imagery and masks.

Aligning two datasets requires \textit{reprojecting} the data from one in order to match the CRS of the other, \textit{cropping} the data to the same spatial bounds, and \textit{resampling} the data to correct for differences in resolution or to establish the same underlying pixel grid. Typically, these are performed as pre-processing steps using GIS software such as QGIS, ArcGIS, or tools provided by GDAL---see Section~\ref{subsec:gdal_example} for an example of a GDAL command to align two data layers. This requires some level of domain knowledge to perform correctly and does not scale to large datasets as it requires creating duplicates of the layer to be aligned. Further, this approach still requires an implementation of a dataset or data loader that can sample patches from the pre-processed imagery.

In TorchGeo, we facilitate this process by performing the alignment logic on-the-fly to create pixel-aligned patches of data sampled from larger scenes. Specifically, we implement the alignment logic in custom PyTorch Dataset classes that are indexed in terms of spatial coordinates. Given a query in spatial coordinates, a desired destination CRS, and a desired spatial resolution, the custom dataset is responsible for returning the corresponding reprojected and resampled data for the query. We further implement geospatial data \textit{samplers} that generate queries according to different criteria (e.g. randomly or in a regular grid pattern). See Section~\ref{sec:implementation} for a discussion on the implementation of these.

As our datasets are indexed by spatial coordinates we can easily compose datasets that represent different data layers by specifying a valid area to sample from. For example, if we have two datasets, $D^1$ and $D^2$, we may want to sample data from the intersection of the layers, $D^1 \cap D^2$, if one layer is imagery and the other layer is labels, or the union of the layers, $D^1 \cup D^2$, if both layers are imagery. The latter is particularly powerful for use in applications like multimodal learning~\citep{8269806} and data fusion~\citep{doi:10.1080/19479830903561035}. As we describe in the following section, we implement generic dataset classes for a variety of common remotely sensed datasets (e.g. Landsat imagery) that can be composed in this way. This allows users of the library to create their own multimodal datasets without having to write custom code.

Most importantly, these abstractions that TorchGeo creates---geospatial datasets and samplers---can be combined with a standard PyTorch data loader class to produce fixed size batches of data to be transferred to the GPU and used in training or inference. See Listing~\ref{listing:example} for a working example of TorchGeo code for setting up a functional data loader that uses Landsat and Cropland Data Layer (CDL) dataset implementations. Our approach trades off required storage space for data loading time compared to pre-processing all data layers, but, crucially, does not require knowledge of GIS tooling. We benchmark our implementations in Section~\ref{subsec:dataset_benchmarks}.

\input{tables/benchmark_datasets}

\section{Implementation} \label{sec:implementation}

The implementation of TorchGeo follows the design of other PyTorch domain libraries to reduce the amount of new concepts that a user must learn to integrate it with their existing workflow. We split TorchGeo up into the following parts:
\begin{description}
    \item[Datasets] Our dataset implementations consist of both \textit{benchmark datasets} that allow users to interface with common datasets used in RS literature and \textit{generic datasets} that allow users to interface with common geospatial data layers such as Landsat or Sentinel 2 imagery. Either of these types of datasets can also be \textit{geospatial datasets}, i.e. datasets that contain geospatial metadata and can be sampled as such. These are part of the core contribution of TorchGeo and we describe them further in the following section.
    \item[Samplers] We implement samplers for indexing into any of our \textit{geospatial datasets}. Our geospatial datasets are indexed by bounding boxes in geospatial coordinates (as opposed to standard fixed length datasets of images which are usually indexed by an integer). The samplers generate bounding boxes according to specific patterns: randomly across all scenes in a dataset, random batches from single scenes at a time, or in grid patterns over scenes. Different sampling patterns can be useful for different model training strategies, or for running model inference over datasets.
    \item[Models] Most existing model implementations (e.g. in torchvision) are fixed to accept 3 channel inputs which are not compatible with multispectral imagery. We provide implementations (or wrappers around well established implementations) of common deep learning model architectures with variable sized inputs and pre-trained weights. For example, models that use all of the Sentinel 2 multispectral bands as inputs. We also implement architectures from recent geospatial ML work such as the Fully Convolutional Siamese Network~\citep{DBLP:journals/corr/abs-1810-08462}.
    \item[Transforms] Similar to existing model implementations, some existing deep learning packages do not support data augmentation methods for multi-spectral imagery. We provide wrappers for augmentations in the Kornia~\citep{eriba2019kornia} library (which does support augmentations over arbitrary channels).
    \item[Trainers] Finally, we implement model training recipes using the PyTorch Lightning library~\citep{William_PyTorch_Lightning_2019}. These include both dataset specific training code, and general training routines---for example, an implementation of the BYOL self-supervision method~\cite{grill2020bootstrap}.
\end{description}

\subsection{Datasets}

We organize datasets based on whether they are \textit{generic datasets} or \textit{benchmark datasets} and based on whether or not they contain geospatial metadata---i.e. are a \textit{geospatial dataset}.

Benchmark datasets are datasets released by the community that consist of both inputs and target labels for a specific type of task (scene classification, semantic segmentation, instance segmentation, etc.). These may or may not also contain geospatial metadata that allows them to be joined with other sources of data. In our opinion, one of the strongest components of existing deep learning domain libraries is the way that they make the use of existing datasets trivial. We aim to replicate this and, for example, include options that let users automatically download the data for a corresponding dataset. Table~\ref{tab:benchmark-datasets} lists the set of benchmark datasets that TorchGeo currently supports.

\begin{minipage}{\linewidth}

\input{tables/generic_datasets}

Generic datasets are not created with a specific task in mind, but instead represent layers of geospatial data that can be used for any purpose. For example, we implement datasets for representing collections of scenes of Landsat imagery that let users index into the imagery and use it in arbitrary PyTorch-based pipelines. These are not limited to imagery, for example we also implement a dataset representing the Cropland Data Layer labels, an annual US wide raster layer that gives the estimated crop type or land cover at a 30~m/px resolution (see Table~\ref{tab:generic-datasets}).
\end{minipage}

\subsection{Samplers}

As our \textit{geospatial datasets} are indexed with bounding boxes using geospatial coordinates and do not have a concept of a dataset ``length'', they cannot be sampled from by choosing a random integer. We provide three types of samplers for different situations: a ``RandomGeoSampler'' that returns a fixed-sized bounding box from the valid spatial extent of a dataset uniformly at random, a ``RandomBatchGeoSampler'' that returns a set of randomly positioned fixed-sized bounding boxes from a random scene within a dataset, and a ``GridGeoSampler'' that returns bounding-boxes in a grid pattern over subsequent scenes within a dataset. These samplers are benchmarked in Section~\ref{subsec:dataloader_benchmarks}. This abstraction also allows for methods that rely on specific data sampling patterns. For example, Tile2Vec~\cite{jean2019tile2vec} relies on sampling triplets of imagery where two of the images are close to each other in space while the third is distant. This logic can be implemented in several lines of code as a custom sampler class, that would then operate over any of the generic imagery datasets. Finally, all TorchGeo samplers are compatible with PyTorch data loader objects so can be fit into any PyTorch based pipeline.

\section{Experiments and Results}

\subsection{Datasets} \label{subsec:datasets}

We use the following datasets in our experiments:

\begin{description}
\item[Landsat and Cropland Data Layer (CDL)] The Landsat and CDL dataset consists of multispectral imagery from 114 Landsat 8~\citep{roy2014landsat} collection 2 level 2 scenes taken in 2019 that intersect with the continental United States and the 2019 Cropland Data Layer (CDL) dataset~\citep{boryan2011monitoring}. This data is 151~GB on disk and is stored in cloud optimized GeoTIFF (COG) format. We use this dataset to benchmark our proposed GeoDataset and sampler implementations.
\item[So2Sat] The So2Sat-LCZ42 dataset~\citep{zhu2019so2sat} is a \textit{classification} dataset that consists of 400,673 image patches classified with one of 42 different local climate zone labels. The patches are $30 \times 30$ pixels in size with 18 channels consisting of Sentinel 1 and Sentinel 2 bands. They are sampled from different urban areas around the globe. We use the second version of the dataset as described on the project's GitHub page\footnote{\url{https://github.com/zhu-xlab/So2Sat-LCZ42}} in which the training split consists of data from 42 cities around the world, the validation split consists of the western half of 10 other cities, and the testing split covers the eastern half of the 10 remaining cities.
\item[LandCover.ai] The LandCover.ai dataset is a \textit{semantic segmentation} dataset~\citep{boguszewski2021landcover} that consists of high-resolution (0.5~m/px and 0.25~m/px) RGB aerial imagery from 41 \textit{tiles} over Poland where each pixel has been classified as one of five classes (four land cover classes and a ``background'' class). The scenes are divided into 10,674 $512 \times 512$ pixel patches and partitioned into pre-defined training, validation, and test splits according to the script on the dataset webpage\footnote{\url{https://landcover.ai}}.
\item[Chesapeake Land Cover] The Chesapeake Land Cover dataset~\citep{robinson2019large} is a \textit{semantic segmentation} dataset that consists of high-resolution (1~m/px) imagery from the US Department of Agriculture's National Agriculture Imagery Program (NAIP) and high-resolution (1~m/px) 6-class land cover labels from the Chesapeake Conservancy. Specifically, the dataset contains imagery and land cover masks for parts of six states in the Northeastern US: Maryland, Delaware, Virginia, West Virginia, Pensylvania, and New York. The data for each state is split into $\sim7\times6$ km tiles and then divided into pre-defined training, validation, and test splits\footnote{\url{https://lila.science/datasets/chesapeakelandcover}}.
\item[RESISC45] The RESISC45 dataset is a \textit{classification} dataset~\citep{zhang2019remote} that consists of 31,500 $256 \times 256$ pixel RGB image patches of varying spatial resolutions where each patch is classified into one of 45 classes. As the dataset does not have official splits, we use the train/val/test splits defined in~\cite{neumann2019domain}.
\item[ETCI 2021] The ETCI 2021 dataset~\citep{etci2021} is a \textit{semantic segmentation} dataset used in a flood detection competition. It consists of 66,810 256x256 pixel Sentinel-1 SAR images. We use the official train/test splits, and we further randomly subdivide the train split 80/20 into train/val splits.
\item[EuroSAT] The EuroSAT dataset~\citep{helber2019eurosat} is a \textit{classification} dataset consisting of 27,000 64x64 pixel Sentinel-2 images and 10 target classes. As the dataset does not have official splits, we use the train/val/test splits defined in~\cite{neumann2019domain}.
\item[UC Merced] The UC Merced dataset~\citep{yang2010bag} is a \textit{classification} dataset consisting of 21,000 256x256 pixel RGB images from the USGS National Map and 21 target classes. As the dataset does not have official splits, we use the train/val/test splits defined in~\cite{neumann2019domain}.
\end{description}

\subsection{Data loader benchmarks} \label{subsec:dataloader_benchmarks}

\begin{figure}[b]
    \centering
    \begin{subfigure}[b]{0.45\textwidth}
        \caption{Sampler performance vs. batch size}
        \includegraphics[width=\textwidth]{figures/samplingrate_vs_batchsize}
        \label{fig:load}
    \end{subfigure}
    ~
    \begin{subfigure}[b]{0.45\textwidth}
        \caption{Effect of preprocessing and caching}
        \includegraphics[width=\textwidth]{figures/bar_comparison}
        \label{fig:warp}
    \end{subfigure}
    \caption{Sampling performance of various ``GeoSampler'' implementations. (a) Solid lines represent average sampling rate, while shaded region represents minimum and maximum performance across random seeds. (b) Average sampling rate under different data loading conditions.}\label{fig:dataloader}
\end{figure}

We first benchmark the speed at which TorchGeo can sample patches of imagery and masks from the Landsat and CDL dataset. We believe this dataset is typical of a large class of geospatial machine learning problems---where users have access to a large amount of satellite imagery scenes covering a broad spatial extent and, separately, per-pixel label masks where each scene is not necessarily projected in the same coordinate reference system. The end goal of such a problem is to train a model with \textit{pixel-aligned} patches of imagery and label masks as described in Section~\ref{sec:design}. As such, we measure the rate at which our dataset and sampler implementations can provide patches to a GPU for training and inference.

In Figure~\ref{fig:load}, we calculate the rate at which samples of varying batch size can be drawn from a ``GeoDataset'' using various ``GeoSampler'' implementations. Compared to the other samplers, ``GridGeoSampler'' is significantly faster due to the repeated access of samples that are already in GDAL's least recently used (LRU) cache. For small batch sizes, ``RandomGeoSampler'' and ``RandomBatchGeoSampler'' are almost identical, since overlap between patches is uncommon. However, for larger batch sizes, ``RandomBatchGeoSampler'' starts to outperform ``RandomGeoSampler'' as the cache is used more effectively.

In Figure~\ref{fig:warp}, we demonstrate the difference that preprocessing and caching data makes. This is most easily demonstrated by ``GridGeoSampler'' and to a lesser extent the other samplers. GDAL's LRU cache only saves raw data loading times, so warping must always be done on-the-fly if the dataset CRSs or resolutions don't match. When the necessary storage is available, preprocessing the data ahead of time can lead to significantly faster sampling rates. Although ``RandomGeoSampler'' and ``RandomBatchGeoSampler'' are much slower than ``GridGeoSampler'', most users will only need to use ``GridGeoSampler'' for inference due to our pre-trained model weights.

\subsection{Dataset benchmarks} \label{subsec:dataset_benchmarks}

\input{tables/benchmark_results}

We also use TorchGeo to create simple, reproducible benchmark results on 7 of the datasets described in Section \ref{subsec:datasets}. We report the uncertainty in the results such that future work can evaluate whether a proposed improvement is due to methodological innovation or variance in results due to the stochastic nature of training deep learning models. To ensure reproducibility, we include a model training and evaluation framework in TorchGeo based around the PyTorch Lightning library and release all of our results. To quantify uncertainty we report the mean and standard deviation metrics calculated over 10 training runs with different random seeds. The torchmetrics library~\citep{torchmetrics} was used to compute all performance values (overall top-1 accuracy for the classification datasets, and mean intersection over union for the semantic segmentation datasets).

Our main results are shown in Table \ref{tab:main-results}. We find that our simple training setup achieves competitive results on several datasets. The in-domain pre-training method in~\citep{neumann2019domain} trains models, starting with ImageNet weights, further on remote sensing (in domain) datasets before actually training on the target dataset and finds that this performs better than simply starting from ImageNet weights. In contrast, our best result across the RESISC45, EuroSAT, and UC Merced datasets come from simply using ImageNet pre-trained models and training on the target dataset with low learning rate. The difference between these approaches is likely entirely due to different learning rate selection in the hyperparameter search, and data augmentation in the in-domain pre-training setup. On the So2Sat dataset we find the ImageNet pre-trained models are able to achieve similar results with in-domain pretraining, but only when using all S2 bands vs. RGB only. The previously reported baseline methods on the LandCover.ai dataset all use a DeepLabV3+ segmentation model with a Xception71 + Dense Prediction Cell (DPC) encoder that has been pre-trained on Cityscapes. We are able to achieve a result within 0.75~mIoU of this setup using a simple U-Net and ResNet50 encoder pre-trained on ImageNet. Finally, we report the first set of basic benchmark results on the Chesapeake Land Cover dataset.

\subsection{Effect of ImageNet pre-training on generalization performance}

Two of the datasets we test with, So2Sat and Chesapeake Land Cover, contain splits that are designed to measure the generalization performance of a model. The validation and test splits from So2Sat include data from urban areas that are not included in the training split while the Chesapeake Land Cover dataset contains separate splits for six different states. In these setting we observe a large performance boost when training models from ImageNet weights versus from a random initialization, however we do not observe the boost on in-domain data. Table~\ref{tab:generalization} shows the performance of models that are trained on the Delaware split and evaluated on the test splits from every state. The in-domain performance (i.e. in Delaware) of the ImageNet pre-trained model and model trained from scratch are the same, however in every other setting the ImageNet pre-trained model performs better. In all cases but Maryland, this difference is greater than 6 points of mIoU with the most extreme difference being 12 points in Virginia. In the So2Sat case we find that most models are not able to significantly reduce validation loss (where in this case validation data is out-of-domain), while, unsurprisingly, achieving near perfect results on the training data. Despite this overfitting, there remains a large gap between the best and worst models, with ImageNet pre-trained models achieving +7\% and +10\% accuracy over randomly initialized models. These results extend existing lines of research in computer vision that show how pre-training can improve out-of-domain performance~\cite{hendrycks2019using}, and how, specifically, ImageNet pretraining is useful for transfer learning tasks~\cite{huh2016makes}.

\input{tables/chesapeake_results}

\section{Discussion}

We introduce TorchGeo, a Python package for enabling deep learning with geospatial data. TorchGeo provides data loaders for common geospatial datasets, composable data loaders for arbitrary geospatial raster and vector data, samplers appropriate for geospatial data, models, transforms, and model trainers. Importantly, TorchGeo allows users to bypass the common pre-processing steps necessary to align geospatial data with imagery and performs this processing on-the-fly. We benchmark TorchGeo dataloader speed, and demonstrate how TorchGeo can be used to create reproducible benchmark results in several geospatial datasets.

Finally, TorchGeo serves as a platform for performing geospatial machine learning research. Existing works in self-supervised learning with geospatial data rely on spatiotemporal metadata and can be naturally implemented in TorchGeo and scaled over large amounts of geospatial imagery without the need for pre-processing steps. Similarly, augmentation methods appropriate for training with geospatial imagery are under-explored, however can be integrated easily with TorchGeo. Other interesting directions include building inductive biases appropriate for geospatial imagery into deep learning models (similar to the work done on rotation equivariant networks~\cite{marcos2018land}), data fusion techniques (e.g. how to incorporate spatial information into models, or appropriately use multi-modal layers), and learning shape based models. Finally, TorchGeo exposes a catalog of benchmark geospatial datasets (Table \ref{tab:benchmark-datasets}) through a common interface, and, with the results in this paper, has begun to include corresponding benchmark results. This makes it easy for researchers to compare new ideas to existing work without having to repeat expensive computations. We hope TorchGeo can help drive advances at the intersection of machine learning and remote sensing.

\section*{Ethics Statement}
Machine learning with remotely sensed data has the potential to impact many aspects of society in both positive and negative ways. For example, satellite imagery can be used to model and track progress towards roughly 20\% of the indicators of the United Nation's sustainable development goals~\cite{arnold2019global}, as well as inform more effective responses to natural disasters. At the same time, remotely sensed imagery can be used for unwarranted tracking of individuals. For example, academic papers and datasets about human and vehicle re-identification from high-resolution imagery exist and pose serious ethical concerns. Further, the biases of models that are run over satellite imagery are not well understood, and, if used in decision making, could systematically exacerbate existing disparities. TorchGeo makes it easier for researchers to develop machine learning models with remotely sensed data. Users of TorchGeo, and the wider machine learning and remote sensing communities, should consider the ethical consequences of their research when publishing work.

\subsubsection*{Acknowledgments}
This research is part of the Blue Waters sustained-petascale computing project, which is supported by the National Science Foundation (awards OCI-0725070 and ACI-1238993), the State of Illinois, and as of December, 2019, the National Geospatial-Intelligence Agency. Blue Waters is a joint effort of the University of Illinois at Urbana-Champaign and its National Center for Supercomputing Applications.


Wait, you need to change disks on this diskless console? What?
To some people, the emulation scene is seen as a den of pirates, while to others it acts a vital role when it comes to video game conservation, allowing games to be played long after disks/cartridges stopped being manufactured and the consoles themselves slowly make their way to silicon heaven.
Even today's console manufacturers are in on the emulation game, be it Microsoft's Xbox/Xbox 360 emulation on Xbox One or recent retro console releases like the NES/SNES Classic Mini consoles or Sony's new PlayStation Classic console. While some of these emulators are in-house developments, others are ripped straight from the Open Source community, with Sony's PlayStation Classic using code from the PCSX ReARMed PlayStation Emulator.
Kotaku pointed out that the PlayStation Classic's menu contains lists the game's use of the emulator, confirming that Sony took the easy approach when developing the PlayStation Classic. Beyond that, Sony's Classic console also requires disk switching in titles like Final Fantasy VII, which shipped across several disks on the original PS1. This is done by pressing the eject button on the console, despite the fact that every game on the console sits on flash memory. While this can be seen as a novel way to emulate disk switching on this classic console, it is doubtful that modern gamers appreciate being forced to stand up, walk to the mini console and manually switch disks on a console that relies on solid state storage.
Opinions on Sony's Classic console have been mixed so far, with many gamers feeling disappointed by the Sony's included games while others are disappointed by the system's use of 1.5m controller cables, which are not much longer than the cabled used by the SNES Classic. That being said, the PlayStation Classis uses USB controllers, making them compatible with standard USB extension cables, unlike the SNES Classic which relies on a proprietary connector.
You can join the discussion on the Sony PlayStation Classic's use of an Open-Source Emulator on the OC3D Forums.
Honestly. I can see this working well for Sony.


text:
Shot in the Frontier (br.: No meio do tiroteio) é um filme estadunidense curta metragem de 1954, dirigido por Jules White. É o 157º de um total de 190 filmes da série com os Três Patetas produzida pela Columbia Pictures entre 1934 e 1959.

Enredo

Os Três Patetas retornam a uma velha cidade do Velho Oeste, onde suas três noivas (Ella, Bella e Stella) esperam ansiosas para se casarem com eles. Após a cerimônia, as noivas avisam que enquanto estiveram fora, os Irmãos Noonan quiseram namorar com elas e ameaçaram os Patetas de morte caso eles as desposassem. O trio tenta fugir mas os vilões já esperavam por eles no meio da rua. Sem alternativa, eles começam o tiroteio, se escondendo em pedras de tumba expostas na frente da funerária. Quando a munição acaba, Larry tenta conseguir mais mas se engana e coloca bexigas num rifle. Shemp usa seus suspensórios como arma e arremessa pedras contra os bandidos. Quando resta apenas um dos irmãos em pé, os Patetas o desafiam para uma luta corpo-a-corpo. Shemp e Larry são rapidamente derrubados e Moe consegue segurar o bandido e rola com ele pelo chão. Shemp se recobra e consegue nocautear o bandido, sem antes atingir por engano Moe. No final, os Patetas vencem e suas noivas voltam a ficar com eles. Moe quebra o violão na cabeça do trovador (que cantava desde o início do filme),contudo,ele continua a música com um instrumento de menor tamanho que escondera nos bolsos. E Shemp pede um beijo para sua noiva e, quando recebe, fica contente (emitindo um "mi-mi-mi" com a boca).

Notas
Shot in the Frontier foi filmado entre 26–28 de outubro de 1953, quase um ano antes de seu lançamento. Esse é um dos dois filmes da série produzidos em 1954 que contém apenas cenas inéditas. O outro é  Income Tax Sappy. É uma paródia de Stanley Kramer Ocidental High Noon de 1952.

Ver também
 Filmografia d'Os Três Patetas

Ligações externas

Filmes de curta-metragem
Filmes de comédia dos Estados Unidos
Filmes de comédia da década de 1950
Filmes dos Estados Unidos de 1954
Filmes em língua inglesa
Filmes em preto e branco
Filmes de faroeste dos Estados Unidos
Filmes de faroeste da década de 1950
Filmes da Columbia Pictures
Filmes de The Three Stooges

redpajama_set_name: RedPajamaWikipedia


Love and energy aren't always enough to provide what Allensworth, a historic African-American town, needs most: clean water, accessible to all.
Removing arsenic costs money, and money is something a small, rural water system never has.
Colonel Allen Allensworth, a former slave who rose to become a Union officer during the Civil War, had founded the eponymous town in 1908, when he bought up 2,700 acres of alkali flats to establish a black utopia in a part of the San Joaquin Valley known as the Tulare Basin. By 1913, some 1,200 people from across the country had responded to Allensworth's call — sent out via newspaper advertisements — to build the "Tuskegee of the West." Back then, abundant clear water flowed from artesian wells, enough to drink and to irrigate crops of alfalfa, sugar beets and corn, along with feed for livestock.
But when Morrison arrived, all that remained of Allensworth's vision was a nostalgic new state park, established in 1976 to commemorate the fallen town, and a tumbledown village of mostly Latino migrant workers and a few African-American families, grinding out a spare existence on the now-parched land. They cooked, when they could afford it, with expensive propane brought in by the tank. If they had toilets to flush, the sewage went into faulty septic systems; many of them used outhouses instead. Their wells were determined to be contaminated with arsenic, at levels too high for human consumption. A remedial treatment system never proved quite adequate: Residents still drove miles to fill tanks with clean water from other jurisdictions.
Flowers has a long history of working within rural communities to improve living standards, and water quality in particular. She grew up in Lowndes County, Alabama, where in 2001 black families living in poverty were blamed for a raw sewage spill because they couldn't afford to build a treatment system. After the U.S. Environmental Protection Agency conducted a house-to-house survey that traced the problem back to failing septic and municipal systems, she persuaded both the state and federal officials to stop prosecuting Lowndes' poorest residents for sewage spills they had no power to prevent.
That pattern started early. By the time Colonel Allensworth arrived in the Tulare Basin, it had been irrevocably altered by agriculture. Farming had so drawn down the water table that an entire lake — the largest west of the Mississippi — had disappeared, one that once ran so heavy with fish that early farmers were said to have harvested them with buckets. Arsenic, which is naturally occurring in the basin, was already in the soil and, to some extent, the water. The continuous overdraft by industrial agriculture has only made the problem worse.
Judith Lewis Mernit writes about energy, the environment, economic justice and public health from Los Angeles, California. Her work has appeared in High Country News, Sierra Magazine, Yale Environment 360, TakePart, The Atlantic and the LA Weekly.
Thank you for this ! .
I've lived in rural setting where clean water was always a problem .


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Parent, two teachers in court over North West school violence
North West / 24 June 2019, 3:59pm / Molaole Montsho and ANA Reporter
National Professional Teachers Organisation of South Africa (Naptosa) members protest at the Mankwe Magistrate's Court in Mogwase during the court appearance of Itumeleng Letsepe accused of assaulting a teacher. Picture: ANA
Rustenburg - A parent and two North West primary school teachers appeared in separate courts in the province on charges of assault committed at two different schools.
Itumeleng Lizbeth Letsepe, 40, is appearing in the Mankwe Magistrate's Court in Mogwase for the alleged assault on a primary school teacher Mpuseng Sepotokele, 53. Letsepe is facing a charge of assault with the intention to do grievous bodily harm.
The Grade 3 teacher told the court she was beaten with an umbrella by Letsepe on February 22 at the school gate when she arrived for work at Leema Primary School in Tlhatlhaganyane, about 60km north west of Rustenburg.
The incident took place in full view of learners arriving for school.
She sustained wounds to the head, right arm and right hand. She had a two centimetre cut at the back of her head and a stitch behind the right ear. Sepotokele also sustained a 1.5 centimetre laceration to the elbow, while her middle finger was fractured. She told he court she has frequent headaches following the attack
An enraged Letsepe had accused Sepotokele of being abusive towards her son. Her son had come come crying, alleging that Sepotokele had slapped him.
Letsepe claims she acted as a concerned parent and wanted to stop the teacher from abusing her son.
Sepotokele maintains that she could never abuse a child.
She was considering taking early retirement due to the injury on her hand.
"I am injured on my right hand, I use it for writing and marking as a teacher. It is still painful to use now. I still go to physiotherapy for this hand," she told the court.
She had also moved out of Leema Primary School.
"I felt unsafe in the environment I was in. I did not want the trauma of teaching the child and having to deal with the woman that almost killed me," she said.
The case was postponed to July 25.
National Professional Teachers Organisation of South Africa (Naptosa) North West provincial chairman, Ephraim Ditlopo, told African News Agency (ANA) that the teachers' union was concerned about the state of safety in schools.
"We are here [in Mogwase] with the basic intention of supporting our member who was assaulted by one of the parents in school.
"We are greatly concerned by the fact that there are no policies that safeguard all people and persons who are going to schools, members of staff even learners who are assaulted almost everyday," he said.
In Tlhabane, near Rustenburg, two teachers from Motladi Kgoadigoadi Primary School are appearing in the Bafokeng Magistrae's Court for allegedly assaulting two minor siblings - a boy and his sister.
A nine-year-old boy sustained a wound to his knee after his teacher allegedly kicked him with a high-heeled shoe in July 2018.
The 55-year-old teacher appeared in the regional court on June 5 on a charge of assault with the intention to cause grievous bodily harm. Her case was postponed to July 5 for the State to consult witnesses and to give her lawyer time to prepare a representation to court.
Her 53-year-old colleague also appeared in the regional court on June 20 for the alleged assault of a ten-year-old girl.
He allegedly used a broom stick to beat the child for playing in the rain in January 2018. The girl suffered a swollen hand.
The beating, according to their father, was an attempt by the school to force him to take his children to another school.
The father claimed that prior to the beating, his children told him they were told at school that he should transfer them to another school.
He alleged this was a result of an acrimonious relationship between him and the school management after he alleged the school's finances were clumsily handled. He approached the public protector to launch an investigation after which he claimed he was removed as a member of the school governing body.
On May 6, his children were sent back home. He said when he went to the district office, he was told to go back to the school the following day.
He was met by a group of angry parents when he arrived on May 7. They allegedly accused him of causing trouble at their school and kicked his children out of school.
The children had not been at school since May 7 and missed out writing their mid-year examinations.
"All I want is for children to go back to school by the next school term," he said.

redpajama_set_name: RedPajamaCommonCrawl


Whole House Fans Cool Homes Very Efficiently, But!
Whole house fans cool homes very efficiently in most areas of the country. But older style fans have come under fire for creating leaks into the attic, causing potential depressurization problems and being very noisy.
The Whole House Fan Man explains the features and the differences between the CentricAir and Quiet Cool. The Whole House Fan Man explains the features and the differences between the CentricAir and Quiet Cool whole house fan damper inlet boxes.
Should You Consider a Traditional or Modern Whole House Fan?
It doesn't make much sense to use an air conditioner to cool a home when the outside air is cooler than the inside. So in areas with hot days and cool nights, people often use whole-house fans to exhaust the hot indoor air once the outside temperature drops below 78°F.
I have to admit that my experience with whole-house fans has not always been positive. I remember almost 20 years ago as an HVAC contractor when homeowners would ask me to quote a whole house fan installation, I'd quickly steer them away.
Other ducted whole house fan companies use an unbalanced (off the self) fan blade like the one below.
By Michael Goodbrand -, (1 Comment).


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Q: State considerations when converting a python desktop application into a web app? I'm confused as to what considerations should be taken into account when converting a desktop application into a web app. We have a desktop app written in python using the wxPython library for the GUI and its a very traditional application which sets up the main window and calls the app.Mainloop() method to sustain the GUI and respond to events. The application itself is a configuration utility that simply accepts file(s) and allows the user to configure it. Naturally, the program keeps track of all the changes made and responds to events in light of those changes.
I intend to serve this utility as part of a larger application using the Django framework hosted on an Apache server and expect many users to use it simultaneously. Once I remove the app.MainLoop() directive, as expected, running the app simply goes through the code once and exits. This is obviously not what I need, the application needs to remember the state.
So far, I've started to identify and decouple all GUI code from the core of the application so I can effectively reuse the core and decided to write the UI in JavaScript using frameworks such as jQuery to handle GUI events and such. Two obvious options of storing the state would be sessions and databases but I'm somewhat stuck while forming a big picture of how this will all work. What will happen between requests in terms of Django views? I would really appreciate it if someone could shed some light on the overall workflow.
Thank you.

A: This question is a bit vague: some specifics, or even some code, would have helped.
There are two separate differences between running this as a desktop app and running it on the web. Lack of state is one issue, but it seems like the much more significant difference is the per-user configuration. You need some way of storing that configuration for each user.
Where you put that depends on how persistent you want it to be: the session is great for things that need to be persisted for an actual session, ie the time the user is actively using the app at one go, but don't necessarily need to be persisted beyond that or if the user logs in from a new machine. Otherwise, storing it explicitly in the database attached to the user record is a good way to go.
In terms of "what happens between requests", the answer as Bruno points out is "nothing". Each request is really a blank state: the only way to keep state is with a cookie, which is abstracted by the session framework. What you definitely don't want to do is try to keep any kind of global or module-level per-user state in your app, as that can't work: any state really is global, so applies to all users, and therefore is clearly not what you want here.

redpajama_set_name: RedPajamaStackExchange


Dec. 19, 2018 6:30 p.m. to 9:30 p.m.
Blues phenoms Billy Flynn and Jim Schwarz light up the cheel this Wednesday with an amazing duo of the finest blues you can find! Join us for this amazing evening.
Date & Time Dec. 19, 2018 6:30 p.m. to 9:30 p.m.


text:
You are here: Atlantic Council Di Giovanni, Gianni
Di Giovanni, Gianni - from Italy
Journalist, Head of Eni's international relations team in Washington, D.C., Chairman of Eni Petroleum and Chairman of Eni USA R&M. He worked for the IRI for six years, before completing graduate studies in Journalism at the Catholic University of Milan.
In 1994, he was appointed Head of Public Relations at Stet International and Head of Institutional Affairs at SMH and Stet International Netherlands, roles in which he served until 1998, when he became Head of Communications for Telecom Italia's TIM.
From November 2000 to December 2006, he served as Head of Public Relations for the Italian mobile carrier Wind.
Since October 2015, Di Giovanni has served as Chairman of the Board of Directors of Eni Co Inc. He is also Chairman of the Board of Directors of Eni USA R&M Co. Inc., a company which produces and distributes lubricants in North America.
Since October 2015, he has served as Executive Vice Chairman of Eni's Washington D.C. branch office, a role through which he works to safeguard and promote Eni's interests in the US, interfacing with local public institutions and authorities, assessing markets and advising the company on growth opportunities.
https://en.wikipedia.org/wiki/Gianni_Di_Giovanni


Vern Burkhardt (VB): You say you wrote The Innovation Solution to offer everyone a simple innovation solution process. Can it be a simple process?
Praveen Gupta: I wrote The Innovation Solution to share the breakthrough innovation framework with everyone. It is written for the casual reader. Innovation is not limited experts only.
Is there only one process? No, there can be many processes but I haven't found anyone who is systematically studying the knowledge, science, or systematic approach to making innovation a learned skill.
I purposefully started my innovation journey to develop a framework so it can be taught to people. People still think innovation is a fuzzy thing and perhaps even a gift from the outer world, but any field starts with subjective knowledge before becoming a repeatable and routine knowledge. I want to move the field of innovation in the direction of becoming a routine knowledge. Today it is in at an intermediate state where it's still not fully understood. My framework takes the level of knowledge a considerable stride in being much better understood.
VB: Is this an accumulation of your experience in the area of innovation?
Praveen Gupta: Well, it is an outcome of my investigations and research to find answers to the questions that people have about innovation. What is innovation? How should it work? What can I know about it? What should be done to be more innovative? Why are some people or organizations more innovative than others? What are some of the inefficiencies in the innovation process? These are simple questions, but the answers are not necessarily obvious.
New product development and growth opportunities are the least efficient and effective processes in a business. Innovation and improvement in these areas offer corporations the major opportunities for improving profit and growth. My breakthrough innovation framework and innovation solutions book will contribute to making these processes a little better understood and more successful.
VB: What is the "innovation solution?"
Praveen Gupta: A lot of people have been focusing on a strategy and on policies for innovation. So, this is not an innovator's solution. Instead, the innovation solution is about the process that makes innovation a pervasive, predictable, and profitable way of exploiting people's intellectual capabilities combined with corporate resources.
VB: You say the basic purpose of innovation is "to help people live longer and more comfortably." It all comes down to this basic purpose?
Praveen Gupta: Those are my observations about why people want to innovate, and it relates to mankind's evolution. Innovation is always about making human life easier, better, and more comfortable. These involve seven categories of human needs: safety, food, health, communication, entertainment, productivity, and comfort.
If ever we don't have the desire to become a better species there will be no need to innovate. We know this desire will never go away, so the purpose of innovation will continue to be to make our lives better, and personal, professional and financial growth.
VB: "With the Internet… the rate of innovation is changing, and large corporations cannot keep up with it." Does this mean implementing alternative approaches to innovation, such as open innovation, will increasingly be necessary for survival in the 21st century?
Praveen Gupta: Yes, alternative approaches to innovation will be one of the secrets to survival. IdeaConnection, which will be publishing our conversation, is a good example of how to use the intellectual capital of facilitated teams working online to generate solutions to difficult challenges of companies and other organizations. It's a feasible and viable process, and suggests an important way for innovation to occur in the future.
The Internet Age has brought customers and producers much closer together – they're only a few seconds apart. Physically you can be at the other side of the world but you are still only a few seconds apart from each other. You can demand and communicate what you want. As a result, companies must achieve more innovations faster and with greater efficiency because if they don't they would neither have solutions nor make any money. To remain profitable companies must learn and use alternative innovation processes to achieve speed, efficiency and success.
VB: You say, "…with access to the Internet, a networked individual is the building block of innovation." Do you have any advice about how to mobilize this network?
Praveen Gupta: Once you recognize that the building block of innovation is an individual, not a large corporation or any of its departments, you can then set out creating opportunities to form virtual clusters. In order to achieve an economic advantage from innovation in the Knowledge Age, you not only empower individuals and enable them to collaborate but inspire them to work together to create wealth around the world. They don't have to be co-located. That's the power of the Internet and recognizing that networked individuals are the building blocks of innovation and are at par with corporations for innovation.
VB: And they can create wealth virtually?
Praveen Gupta: They can create real wealth even though they are in different locations throughout the world. They are virtually co-located.
VB: And this enables the advancement of knowledge.
Praveen Gupta: Yes, it is about creating new knowledge together rather than bringing people physically together for incremental innovations.
VB: "Growth creates hope and excitement, while pursuing cost management alone leads to fear and stress." Would you talk a bit about this?
Praveen Gupta: As we have seen in the last 20 or 25 years most corporate leaders in the developed economies have focused on profit. When they tried to improve profit they focused on operational excellence implementing LEAN, Six Sigma, and other continuous improvement approaches.
Leaders must recognize that cost reduction has a limit. You cannot improve beyond a certain point when you take this approach. Their focus on cost cutting almost inevitably leads to a reduction in operations and so-called 'right-sizing', and this leads to layoffs. This stresses people and in contradicts commitment to innovation. They fear punishment and repercussions if they don't cut costs. For people cutting or no cutting cost the outcome appears the same – lay offs.
When you aim for growth it forces you to think which brings intellectual challenge but also an excitement. Work feels more fun. Fun does not mean wasting time; instead it means you can think without constraints. So when you focus on growth you're encouraging thinking rather than action. Thinking is most productive during those happy moments when people can see unlimited possibilities. Growth has no limit to growth opportunities. With a growth focus you can do as much as you want versus limited options for controlling costs. Cost reduction programs diminish human spirit, and growth initiatives inspire people.
VB: As you point out, for over two decades the main measures of continuous improvement in many businesses have been cost reduction and profit. Are you finding that more companies are now implementing measures related to innovation?
Praveen Gupta: It is human nature to want to measure for results. We want to measure innovation because we want to see the magic right away. But I have learned that without understanding innovation we will create many meaningless measures. The data may provide statistical correlations but there's unlikely to be causative relationships to help us understand the true impacts of innovation. As a result, people draw random conclusions, and sometimes even conflicting and wrong conclusions, and make wrong decisions about investments.
It may be possible to establish many measures of innovation, but without knowledge of the process you end up with too many measures and too little effectiveness.
If you understand the process as few as three measurements can give you much more return on your effort to measure effectiveness of innovation. Yes, only three measurements, but measures must follow the better knowledge of the innovation process.
VB: "Innovation should no longer be considered the result of a flash of genius; instead, it should be targeted to making everyone a genius." How do you make everyone a genius?
Praveen Gupta: Creativity is looking for a different way of doing things. It is combining two or more things differently. You can look at any solution, any creative idea – that's how it is formed. When people become good at combining things they become good at being creative. Above all else it requires a commitment to be creative. Research and invention are investment in your business proposition, and development and innovation must ensure a healthy return on investment.
A genius is the one who thinks differently as compared to anyone else around. If we make it a subconscious habit to always be looking for a different way of doing things by combining two or more things uniquely compared to what anybody else does we can be a genius. By understanding that this is in essence what a genius does we can put this way of thinking into practice, and try to be a genius every day. It's not difficult to do at all.
VB: It would likely make us much more productive.
Praveen Gupta: Oh, I think most will love it. In the absence of innovation and being creative when friends meet and exchange pleasantries such as, "How are you doing or what is new?" no longer will the almost automatic response be "same-old, same-old." If they are being creative and innovating they will never give such a reply. They will always have stories to tell because they will be having different experiences, and will be excited sharing their new experiences.
VB: "…the fundamental strategy for any business must be to sustain profitable growth." Would you talk about this?
Praveen Gupta: The question is what should businesses focus on given that the current strategy of focusing only on profit is causing all this chaos and loss of jobs and opportunities?
When you first start a business your first goal is not to make a profit. Instead it's to find the first customer to get your first project, your first engagement or the first sales order. So growth precedes profit. Whenever you want to build a business first of all you must focus on selling. Sales mean business, and zero sales mean zero business.
Similarly, corporations must always focus on revenue growth then profitable execution. Our fundamental business strategy should be to sustain profitable growth. It is the same for all businesses. For not-for-profit organizations it would be sustaining value-added growth or offering more services. We must always focus on growth, but it must be profitable growth. Growth creates hope and excitement and a sense of freedom, while cost reduction creates fear and stress, a feel of constraints.
Where is this profit to come from? Without growth, businesses cannot survive long. If businesses follow a strategy of cost cutting to the extent of reducing capability and lowering revenues they will shrink, and eventually have to close their doors.
VB: "…organizations can have excellent strategic plans, but they are rarely executed successfully." In your experience, why is this the case?
Praveen Gupta: It has been understood that business strategies are successfully executed less than 10% of the time. Mergers and acquisitions are only about 15-20% successful. Even venture capital investments have a low success rate, as do the new product launches.
The development of a business strategy tends to be more of a right-brain activity while execution of the strategy is more of a left-brain activity. If you develop a strategic plan without collaborating with the left-brain thinking execution people, it will have little chance of experiencing a successful implementation. Operations people must be involved in the development of the strategies and product development. New product designs must be reproducible profitably.
A business strategy must be executable; otherwise the best of intentions would only cost. We improve the business strategy success rate by teaming the right-brain and left-brain people, i.e., strategy and operations people.
Similarly, in the research and development functions most of the effort is directed at product development rather than research with the result that often product performance is marginal and manufacturing is dumped with 'me too' and suboptimal designs. To become a highly profitable and growing organization, we must create a culture of excellence and innovation. We must always do better than anybody else and we must innovate continually.
VB: The last time we talked we discussed your "Brinnovation™" framework. Would you refresh our memories about the rule of two?
Praveen Gupta: Brinnovation™ is an abbreviation for breakthrough innovation, and the breakthrough is defined by the rule of two. The rule of two says if less is better, if you have to innovate something to make it significantly smaller or lesser, divide by two will determine the extent of innovation. At least cut the characteristic into half to set the innovation target. If more is better, then multiply by two, or at least double it.
A breakthrough innovation must make the change or difference visible. If it is different, it better be very different. Don't tell your customers a product or service is different if it doesn't look or operate differently. Breakthrough innovations must look different, work differently and feel different. It must offer a unique enjoyable experience to the customer besides more features and value.
VB: Does it surprise you when you encounter business leaders who do not understand the basic innovation process and yet demand innovation from their employees?
Praveen Gupta: Someone recently suggested that most executives who don't truly understand innovation don't effectively pursue a path of innovation. They may cling onto the theoretical notion of innovation or even recite the words around being more innovative, but their decisions don't reflect a business strategy of promoting new business growth through innovation.
Of course, these same executives often demand innovation from their employees, "Be innovative," but they don't really mean it in terms of insisting on implementation of an actual innovation framework and set of processes. They don't understand what it takes to establish a truly innovative company.
Without sufficient understanding the innovation process, executives defer innovation decisions in the interest of cost reduction, and miss out opportunities for grand successes. For them innovation is still magic or a mystery. That is why our focus is education in innovation for making innovation more pervasive, predictable and profitable.
VB: When you are working with a business what do you look for when assessing whether they have a good innovation process?
Praveen Gupta: This is a great question. Recently we developed a Business Innovation Maturity Model that has 5 stages: Sporadic, Idea, Managed, Nurtured, and Sustained.
Once leaders have a basic understanding of innovation their first question before deciding to develop a strategy to pursue the path of innovation would be, "Where are we now when it comes to innovation? What processes do we have in place that indicate we are being innovative compared to our competition?" They will want this benchmark in order to highlight available internal resources for innovation, determine existing deficiencies, and identify opportunities for better and faster innovations.
The Business Innovation Maturity Model can tell them where they are, and how they can pursue a path toward building an innovative organization for sustaining profitable growth. Only by assessing where they presently are can they determine what to do next. Educating employees in the concepts of innovation and its benefits, and engaging them intellectually are often the first steps to build a culture of innovation.
VB: What is the best way for business leaders to learn how to create a culture of continual innovation in their organizations?
Praveen Gupta: In order for executives to create a culture of innovation rather than focus on developing only one innovative product they must assess what are their company's innovation capabilities, where the opportunities lie with customers, and which processes need minor or significant change. Only then can they develop the right strategy.
VB: You say that "the story" is an important aspect of executing an innovation strategy. Do you have any tips for explaining to employees why they should change the story being told?
Praveen Gupta: The story has to focus on the common good in which employees see their good as being the same as the company's good, and indeed as the society's good. Only then employees buy into the story.
If they only see the innovation strategy as being good for the company leading to bonuses for the executives but nothing of benefit to them, they won't buy into it. So the story has to be that the company cares for its employees, only wants to do what is best for the employees, and as a consequence it will be good for the company. Seeing employees first and company second customers would enjoy the experience working with the company.
Innovation is not for the company. Companies are made up of people and their leaders. So innovation is for the people in the company and ultimately for the benefit of society. If we have a story that connects innovation to people it will be sold. If it doesn't connect to employees it's not going to be powerful enough to drive change, and make innovation an integral part of doing business.
VB: Sometimes employees forget that the company is for them, as employees.
Praveen Gupta: Every employee is a part owner of the company because the employees' continued employment depends on the health and wellbeing or the company's continued profitable growth.
Today 'HR' is a term used to refer to Human Resources in a company. In a sense employees have been trivialized by being described by these two letters. It's like being reduced to a number. And they are discarded like numbers during economic downturns or when there is a short-term focus on profit by cost reduction.
Thomas Watson was the founder of IBM and led it to great successes. I was curious about his approach while he was building IBM to become a highly successful company. In reading one of his books I found a powerful message in the context of innovation. In Thomas Watson's time, 'ER' was used rather than 'HR' and it stood for employee relations. He knew that if the owners and leaders have a good relationship with employees, their employees will care for the company. The first priority of a leader is to listen and care for the employees and they, in turn, will care for the company and will innovate. They will think about how to improve business processes and develop new products and services. If they feel they're not cared for or not listened to, they will have no interest in applying their intellectual energy to create new solutions.
VB: Most companies have changed the name from Employee Relations to Human Resources. I guess they don't understand the distinction you've just shared.
Praveen Gupta: Yes, but I feel innovation of Human Resources must be undone to Employee Relations.
A lot of companies will say in their written materials and their leaders will repeat that "Employees are our most valuable assets," but employees are the first assets to dispose of. Many leaders will say employees are highly valuable, but their actions suggest they don't really mean it. Employees are disposed of before the companies' machines. Many companies follow the strategy of keeping the machines but letting some of their key employees who are operating them go – and with them goes all the training, knowledge, and skills.
In the Knowledge Age employees are even bigger assets than before so companies should focus on building employee relationships. The first evidence of building relationships is listening to employees' ideas and not scare them of lose of their jobs in the case of an economic downturn.
VB: The fourth step in your innovative idea generation process is to write down funny ideas about the topic under consideration for innovation. You say that in this step during training "practically everyone stumbles, with a few exceptions." Why is this?
Praveen Gupta: Our environment has been so structured and sterilized through education and subsequently through work that we end up telling people what to do, and we tell them by our actions "Don't think; be serious". Implied is we are growing older and wiser, and we shouldn't be having fun.
Most brainstorming meetings are only about 30% effective, and normally even the best ideas generated in those meetings are not so good. The typical brainstorming session is like memory jogging. Participants are dumping their experiences; they're clearing their memories. But while doing this they're not thinking.
Thinking begins when you ask people to come up with some crazy, stupid, or funny ideas. Funny ideas occur when you get the left and right hemispheres of the brain to work together for the purpose of creating an enjoyable experience. The extent of innovation increases when you move from generating good to funny ideas. Our studies have shown that if you ask people to think crazy, think stupid, and think funny they will be much more creative in coming up with new, innovative ideas.
The thinking process has four stages. The first is to think of good ideas to clear the brain, and then dump the old ideas out of your subconscious mind by writing them down. Then you can start thinking of fresh ideas that are crazy, stupid or funny. Stupid ideas take more time to think up than good ideas. Funny ideas are even harder to think up because for funny ideas we use our crazy and stupid sense for the purpose of innovating our products and services to provide our customers an enjoyable experience. This means you care for customers.
VB: Do you personally use this approach when tackling significant problems?
Praveen Gupta: Absolutely. I have done this experiment with people from all over the world, and in different countries. People love it.
In the beginning it sounds strange to be thinking stupid and funny ideas. For example, I was giving a talk to high school students and later one mother told me, "She thought her some was doing some stupid things and it was not good. She was happy to realize that he was trying to do differently and being innovative!" Recognizing your kid's potential, which according to societal standards would be called stupid behavior, is important because it's possibly innovative behavior.
We often kill people's creativity because we call their actions or thoughts stupid, and stupid is considered bad. We need to change our thought process so that if somebody labels an idea as 'stupid' we think it means innovative. It may be a highly creative idea.
As Einstein said if something you innovate does not look absurd, it must not be innovative enough. You can extend stupid to absurdity.
VB: "Ultimately, every employee has the responsibility to play and create value through innovation." How does play contribute to innovation?
Praveen Gupta: Play is an alternate word for experimentation. How do kids play? They play by doing different things and using their creative minds to act out different scenarios.
In the workplace we have to create a culture of play where people can experiment without the fear of failure. We have to recognize that if people are experimenting with new ideas and new things they're not failing.
Einstein used the term "combinatorial play" for trying different combinations in your mind. Every innovation is a unique combination of two or more things. Only by trying different combinations can you come up with the right combinations. Play implies combinatorial play – experimenting without fear of failing.
VB: Would you talk about the TEDOC methodology, and why it is so valuable as a process for innovation?
Praveen Gupta: Most innovations fail or fail to achieve their full potential. When a lot of innovative products are developed they are functionally designed and then given to manufacturing. This makes it difficult for manufacturing processes to reproduce due to design issues. As a result products are often over-specified or over-engineered and therefore expensive to manufacture, which means they are expensive for customers. I tried to address these problems in the TEDOC methodology. This methodology is based on a breakthrough innovation framework with 14 steps grouped into 5 phases: Target, Explore, Develop, Optimize, and Commercialize.
Many innovations are not developed with sufficient input or insights off the customer experience. As Steve Jobs often said in his interviews, "Nobody listens to our customers more than we do." One of his priorities was to think of all the possibilities for which customers might use Apple's products or services. These possibilities are incorporated into the products, and interestingly not all capabilities publicized. It is left for customers to discover over a period of time resulting in sustained surprises and pleasant experience using Apple products.
Target means not just knowing what we know now but, more importantly, extensively listening and paying attention to the insights of customers' needs and applications, and then deciding what to innovate. It identifies a clear need for innovation based on opportunity analysis. The need is found in complaints, indecisions, technical limitations, nagging or chronic problems, and competitors' limitations. It also involves examining the maturity of the industry, trends in the supply chain, and target markets. Then key benefits of the solution to be innovated must be documented.
Explore implies research. In a typical R&D department there is little or no research but all development. Research doesn't mean learning something new. Research is like searching what has already been researched and developed. Researching is identifying what has been developed to date so you can develop beyond that level. In the innovation process exploring is about knowing what has been done before so you can think what can be done next.
The Develop phase is to force ourselves to develop multiple alternative solutions rather than just one because the one we initially develop might not be the best solution. We want to maximize innovation, not just engage in it. Following the rule of 2, which we discussed earlier, will help ensure our final solution will be a breakthrough innovation and this, in turn, will require a different approach than the status quo. The company must try to maximize innovation rather than practice a minimalist approach to innovation.
The Optimize phase is making innovation profitable, and this is where a lot of companies fail. They don't optimize the design in pre-production stage resulting in significant waste affecting profit directly adversely. The prototype or pilot run that initially looked acceptable might result in continual rework and field failures. You want to pay attention to the affordability and the profitability before bringing a product to the marketplace.
The fifth phase is Commercialize. Businesses must begin innovating with commercialization in mind. Often commercialization is more difficult than developing an innovative product. An innovation will fail if not sufficiently marketed. Every innovator must learn how to commercialize ones innovation that means developing the ability to create and communicate value. In today's e-world businesses must extensively use social media and viral marketing tools to market and sell innovative solutions.
Without Target there will be wrong innovations, without Explore there will be me-too innovations, without Develop there will be the minimal innovations, without Optimize there may be no profit, and without commercialization there will be no innovation.
VB: When talking about the seven killers of innovation, you say, "Trust me, I know!" in reference to "expertise has an adverse impact because of associated baggage." How do you know?
Praveen Gupta: Expertise without continual exploration quickly becomes an obsolete knowledge. Effectively there are no experts because in today's age things are changing so rapidly that nobody knows everything. Experience means you are willing to share, explore, and collaborate to build something new.
VB: "…a cluster of people by itself does not become more innovative; instead, a network of thinking individuals is more innovative." Has this been your experience in your consulting work?
Praveen Gupta: In a group one or two people will dominate, and the others become quiet and do not participate to their full potential.
Studies have been done of the effects of group dynamics on the innovation process. If you form ten groups of ten people each, let them solve a problem and compare their productivity with one group of hundred people thinking individually you will discover something rather surprising. The groups that had the individuals consider the issue individually before being formed into groups were more productive in generating novel and innovative solutions. By having them think individually before brainstorming as a group you use everyone's intellectual potential. If formed into groups without this individual thinking process it will block a lot of people's intellectual curiosity because of group dynamics. The lesson is we want to make sure that everyone has contributed intellectually before joining a group or a meeting.
Brainstorming is all about building on each other's ideas. If people haven't thought of unusual or different ideas they're only building on the ideas of the few who intellectually or physically dominate the group. You want to make sure that everybody's ideas are used to build on the ultimate solution.
VB: The tendency for one or two people to dominate a group is interesting isn't it? It seems to be a universal phenomenon.
Praveen Gupta: It's not their fault. It is due to a lack of a good brainstorming process. Brainstorming will not work well without people understanding the thinking process. If people are instructed to think of some creative idea they don't know how to go about it because few are ever taught how to think of creative ideas there will be a limited creativity. Giving participants the opportunity to think of ideas first, and then being able to discuss these ideas in a brainstorming group will be much more productive.
VB: You identify 3 of the 10 measurements in your Six Sigma Business Scorecard book as relating to innovation: employee recognition by CEO, employee recommendations, and new business/sales ratio. Would you talk about this?
Praveen Gupta: Often innovation is measured in terms of new ideas, patents filed, new products introduced to the marketplace, revenues derived from new products, or the number of people or hours of work dedicated to innovation activities. At a fundamental level, however, the measurements should be related to growth in the company. It comes back to the concept of profitable growth.
The goal should always be to minimize the number of measures in an organization because the purpose of business is not measures for the sake of generating statistics for management, but to produce value for customers. Therefore we always want to minimize the number of measures to only those that are highly relevant and necessary.
An understanding of the innovation process suggests three measurements are most relevant. They relate to inputs, in-process activities, and the results of the innovation process. Input is inspiration, which is employee recognition by senior management, preferably the CEO. Recognizing employee innovations will breed more innovation. Ideas that employees generate are in-process activities, and these must be encouraged if a culture of innovation is to take hold in a company. The first goal of innovation is generating new ideas without judgment, and these must be listened to and acted upon. The third relevant measure of the innovation process is revenue growth, which is to say new business and increased sale of innovations.
These three measurements essentially represent the complete innovation process. They are correlated, manageable, and predictable. If the measures are low it can be concluded that the company is not inspiring people, encouraging people to generate innovative ideas, or producing revenue growth. Whatever is the case this must be addressed and actions taken to accelerate and improve innovation.
VB: "Human capital consisting of intellectual capability is one of the most underutilized resources….Managers love to see highly intelligent people run around, but rarely allow them time to use their brains." Does it surprise you when you encounter this in organizations?
Praveen Gupta: Sometimes it makes you wonder why leaders hire qualified people, and then get them do trivial work and keep busy running around doing low value tasks. It goes back to the question of having a strategy of focusing on profit versus focusing on growth. If we focus on growth we will encourage employees to engage intellectually and generate innovative outputs leading to increased revenue.
A leader has the responsibility to not only grow the business but to also create opportunities for employees to grow personally, professionally, and financially through incentives and recognition.
Once leaders start to think in terms of growing employees skills, developing innovation capabilities, and caring for employees they quickly realize they have to intellectually challenge their employees. There is a high risk if employees are not challenged. People will stay in a company when they're accomplishing a lot, but will leave when they aren't producing or feeling productive. So to have creative employees who accomplish a lot we have to challenge them to do something new and enable them to think creatively.
VB: "Eventually, a continual flow of employee ideas toward development of new products or services in response to demand from customers or the marketplace should become the status quo." Do you encounter this situation very often with your clients?
Praveen Gupta: I encounter it from time to time. A continual flow of ideas needs to become an ongoing process.
You want to have all of the brains in your company working all of the time. The beauty is that brain capacity is infinite, and so are the potential opportunities. So if a process of a continual flow of employee ideas becomes the routine you will have surprises every day, and identify new growth opportunities.
VB: Do you have any final advice about the innovation solution?
Praveen Gupta: To make the innovation process more pervasive, predictable and profitable leaders and executives must commit to growing their business and investing resources before they need to. If the innovation becomes critical it will diminish the innovation process because crisis will overtake a more creative approach.
Leaders must understand innovation first, identify innovation champions, ensure employees are trained and learn innovation skills, and act innovatively themselves. Once they understand the innovation process they'll be more inclined to engage their people and make the innovative process more pervasive.
VB: Have you been pleased with the response to your book, The Innovation Solution?
Praveen Gupta: So far it has been very satisfying, and I'm looking forward to seeing it becoming a widely read book for learning new rules of innovation in the 21st century.
VB: I presume you have plans for another book in the future?
Praveen Gupta: We are editing Global Innovation Science Handbook for McGraw Hill.
VB: Thank you for telling us about the innovation solution.
Praveen Gupta: It had been a pleasure. Thank you for your support and for publishing the interview on IdeaConnection.com portal.
Author Praveen Gupta says, "Innovation is continually and efficiently developing and delivering breakthrough solutions by offering higher value to customers, achieving profitable growth for businesses, and gaining competitive advantages in the marketplace."
He advises that planning for innovation at all levels will happen if corporate strategy is linked to profitable growth. It's about ensuring there is a return on investment in innovation.
Praveen Gupta is a Director at the Center for Innovation Science and Applications at IIT School of Applied Technology, and an adjunct faculty for teaching Innovation classes.
Praveen is an internationally recognized thought leader in Process Management, Corporate Performance, Six Sigma and Innovation. He is the author of many books including Business Innovation in the 21st Century (2007), The Six Sigma Performance Handbook (2005), and Six Sigma Business Scorecard (2003). He has conducted workshops and given keynotes frequently on business innovation. Praveen is the founding Editor of International Journal of Innovation Science, and co-editor of Global Innovation Science Handbook (2013).
Praveen is President of Accelper Consulting, an Operations Management consulting firm. He is leading the innovation change through education in innovation in academia and industry. Praveen has worked with over 100 small to large clients. His innovation training is being offered by AIAG (Automotive Industrial Action Group) starting 2012. He has developed Certified Business Innovator and supporting training programs offered by IIT for executives and professionals.
Praveen Gupta has a Bachelor of Science in Electronics and Communication from Indian Institute of Technology in Roorkee, and a Master of Science in Electrical and Computer Engineering from the Illinois Institute of Technology in Chicago.
Praveen Gupta is the author of The Innovation Solution; Making Innovation More Pervasive, Predictable and Profitable (2012), Business Innovation In the 21st Century: A Comprehensive Approach to Institutionalize Business Innovation (2007), ISO 9000: 2000 An Implementation Guide (2006), The Six Sigma Performance Handbook (2004), and Six Sigma Business Scorecard: A Comprehensive Corporate Performance Scorecard (2003). He is also co-author of Stat Free Six Sigma: Focusing on Intent for Quick Results (2007), The Six Sigma Green Belt Handbook (2009), A Complete and Balanced Service Scorecard: Creating Value Through Sustained Performance Improvement (2008), Improving Healthcare Quality and Cost with Six Sigma (2007), Six Sigma for Transactions and Service (2004), The Six Sigma Black Belt Handbook (2004), Six Sigma Business Scorecard: Creating a Comprehensive Corporate Performance Measurement System (2003), Six Sigma Deployment (2003), The Role of Board Members in Venture Capital Backed Companies: Rules, Responsibilities, Motivations of Board Members—From Management and VC Perspectives (2004), and The Ways of the VC (Inside the Minds) (2003).
The Innovation Solution is an abridged version of Business Innovation.
His published articles include "Innovation: The Key to a Successful Project" (Six Sigma Forum Magazine, August 2005), and "Innovation and Six Sigma" (http://www.qualitydigest.com, December 2004).


Nigeria's President Dr. Goodluck Ebele Jonathan (GCFR), on Monday, March 12, 2012 conveyed his sincerest and heartfelt commiserations to the families of the victims of the bomb attack earlier on Sunday, March 11, on the St. Finbarr's Catholic Church, Jos as well as the entire congregation of the Church and the government and people of Plateau State.
In a statement by the Special Adviser, Media and Publicity, Dr. Reuben Abati, President Jonathan reiterated his Administration's total condemnation of this and all other terrorist attacks on innocent Nigerians and reassured all Nigerians and friends of the country that the Federal Government is doing everything possible to end the spate of mindless attacks and killings in parts of the country.
He further urged Nigerians not to despair or be discouraged by the persistence of the attacks in spite of his government's efforts to deal decisively with the menace of terrorism within the country's borders because as he said, "despite seeming appearances to the contrary, government is indeed winning the war against the terrorists and will continue to progressively destroy their ability to unleash murderous attacks on peaceful, law-abiding Nigerians."
It is worthy of note that in recent weeks national security agencies have identified and arrested more of these agents of darkness than at any other time. Their hideouts and modus operandi are also being consistently exposed.
The President assured all Nigerians that his Government will continue to build on these recent successes by our security agencies until terrorism is totally defeated in the country with the support, cooperation and solidarity of all patriotic citizens.
The President therefore called on the populace to remain patient and refrain from taking matters into their own hands through actions such as reprisal attacks, as Government continues to take the battle to the merchants of terror and close in on their locations, funding and sponsors. It would be recalled that Boko Haram (BH) which translates to "Western Education is Sin" have continue to hold the Northern part of the country to ransom by planting sucide bombers and bombs in parts of the country. They already claimed responsibility for the latest attacks.


We are headed for Arizona next weekend to catch a couple of Spring Training games! I am sharing some pictures from our trip last year. This is an annual tradition for us and I can't wait to get there!
Have a great week and for more Blue Monday posts, head on over to Sally's!
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My brother in law lives in Arizona and went to a game last week. Should be great weather there. Have fun.
I like the photos! Looks like a bright sunny day 🙂 Hopefully the weather cooperates on your trip. It's been really weird and moody lately. Have fun! Happy Blue Monday!
That is a cool idea like to see that.. Happy blue Monday.
What a good idea to see some baseball and enjoy the sun.
A nice place and way to spend quality time with family and friends. Have a safe trip and be safe.
Looks like fun and those blue skies look so beautiful!
Great mosaic. I love the perfect blue skies.
what a fun day that was 🙂 lovely mosaic!
I'm not a sports fan, so I would be hitting the antique malls, flea markets or craft stores.
That sounds like so much fun to watch the games and enjoy the weather!