text:
\section{Introduction}
\label{sec:intro}
The ocean provides a livelihood for more than 3 billion people whilst being the habitat for billions of species and generates a more than \$3 trillion each year to the global economy \cite{worldEF}. Despite this importance, especially in the last decade, plastic pollution and marine debris have become one of the most important reasons endangering this valuable and consumable resource due to the large needs of the human population and the unconscious use of plastic.

\begin{figure}[t]
    \centering
    \includegraphics[width=\linewidth]{figures/debris.png}
    \caption[Great Pacific Garbage Patches]{Great Pacific Garbage Patches\footnotemark[1].}
    \label{fig:debris}
\end{figure}

Particularly, large plastics entering the ocean either sinks or floats since they are low-density materials \cite{farre2020remote}. Floating plastics in macro-scale (say macro-plastics, $>$ 5mm) is the most crucial sub-group of floating plastics that cause entangling and ingestion for marine wildlife. Thus, detecting and tracking macro-scale floating plastics before their fragmentation into micro and nano scales, and/or threatening the wild-life trough entanglement and ingestion becomes the primary action to protect the marine environment. One example area of focus is the Great Pacific Garbage Patch (GPGP), which can be described as a gyre within a gyre. GPGP covers an area of ocean approximately 1.6 million square km (roughly 8, 3, 2 times greater than the UK, France, and Turkey, respectively) in size. However, the density of ocean plastic can vary greatly and change quickly within any location \cite{lebreton2018evidence}. Figure \ref{fig:debris} shows garbage patches and ocean currents in the Pacific Ocean.

Since the means of plastic usage, specifically in the last decade, has shifted from a positive tool to potential danger to the environment, various efforts have been performed to help authorities and the academic community in reducing and preventing marine pollution. Marine debris and floating plastics can cause various problems, but mostly affects human health and marine wildlife. Particularly, plastic is polluting not only the water, but also the air we breathe, and the food we eat. According to the WWF's report in 2019, an average person could be ingesting approximately 5 grams of plastic every week, which is the equivalent of a credit card's worth of microplastics. Moreover, an average person potentially consumes as much as 1769 particles of plastic every week just from water \cite{twoOceansAqu2020}. Apart from the risks it causes to human health, marine litter and debris also endanger wildlife crucially, from tiny coral reefs to huge whales. To date, plastic ingestion and/or entanglement has caused the killing of more than 700 marine species \cite{wwf2019plastic}. According to the research, more than 50\% of sea turtles eat plastic whilst plastic materials have been found inside more than half of the dead sea turtles. Additionally, more than 50\% of the sea mammals such as dolphins, whales and seals are impacted by plastic pollution whilst 100 thousand of them are killed by plastic each year (National Oceanic and Atmospheric Administration (NOAA) via \cite{wwf2019plastic2}). Figure \ref{fig:animals} depicts several examples of plastic pollution threat on sea animals.

In addition to the problems caused for human health and marine wildlife, the marine debris problem causes some non-native organisms to travel to areas where they would not be found in normal circumstances. These organisms attach themselves to marine debris and are transported all around the world. Last but unfortunately not least, the ocean pollution problem can heavily affect economies, especially for countries whose economy relies on summer tourism. Considering the summer tourism sector heavily relies on healthy coastal and ocean resources along with environmental and aesthetic quality, beaches where floating plastics and garbage patches are drifted, will not provide healthy conditions and this sector will economically be affected by this pollution problem \cite{noaadebris}.
\footnotetext[1]{\url{https://marinedebris.noaa.gov/info/patch.html}}

\begin{figure}[t]
    \centering
    \includegraphics[width=\linewidth]{figures/animals.png}
    \caption[Plastic pollution threat on sea animals. (a) Firepaw, Inc., (b) Hakai Magazine, (c) Lauren Packard, (d) World Animal Protection, Marine Animals of Maine, and David Burdick-Marine Photobank, and (e) Planet Love Life.]{Plastic pollution threat on sea animals. (a) Firepaw, Inc.\footnotemark[2], (b) Hakai Magazine\footnotemark[3], (c) Lauren Packard\footnotemark[4], (d) World Animal Protection, Marine Animals of Maine, and David Burdick-Marine Photobank\footnotemark[5], and (e) Planet Love Life\footnotemark[6].}
    \label{fig:animals}
\end{figure}

For environmental applications, exploiting earth observation systems and their products such as active and passive imagery is a long-established approach, whilst for marine debris and floating plastic monitoring the efforts are still in the early stages. Airborne and space-borne remote sensing modalities such as the optical, hyperspectral, and synthetic aperture radar (SAR) have shown an important potential up to date in detecting, tracking, and mapping marine pollutants, such as oil spills. Moreover, thanks to their (optical, hyper-spectral) rich visible band and spectral choices, remote sensing imagery has had applications in assessing various metrics based on algal blooms whilst SAR has shown to be the gold standard in vessel-tracking and recognition applications. For all the marine applications above, the main imaging modality limitation was their spectral and temporal resolutions. Thanks to recent technological developments, new generations of satellites have been launched and spatial resolutions which were previously unavailable are now provided from space-borne remote sensing. In the last decade, initiatives such as NASA, UK Space Agency (UKSA), and European Space Agency (ESA) pay attention to saving the ocean by using recent technological advances. For this purpose, some effort has been made to track/detect marine pollution and illegal fishing. Satellite remote sensing imagery (optical, SAR, hyper-spectral, etc.) has been the leading technique for marine monitoring since its capability to capture wide-angle area imagery under challenging weather conditions and for various sea states, etc.

\footnotetext[2]{\url{https://firepaw.org/2019/07/05/sharks-other-marine-life-strangled-by-plastic-study/}}
\footnotetext[3]{\url{https://www.hakaimagazine.com/news/plastic-pollutions-rapidly-mounting-toll/}}
\footnotetext[4]{\url{https://www.flickr.com/photos/110485367@N08/11180067935}}
\footnotetext[5]{\url{https://www.worldanimalprotection.org/our-work/animals-wild/sea-change/our-work/rescue}}
\footnotetext[6]{\url{https://www.planetlovelife.com/pages/wildlife-entanglement}}
\footnotetext[7]{\url{https://www.plastic-i.com/}}
\footnotetext[8]{\url{https://theoceancleanup.com/}}
\footnotetext[9]{\url{https://theoceancleanup.com/plastic-tracker/}}
\footnotetext[10]{\url{https://debristracker.org/}}
\footnotetext[11]{\url{https://www.oceanscan.org/}}
\footnotetext[12]{\url{https://messages-in-bottles.netlify.app/}}

Apart from the governmental initiatives mentioned above, various efforts have also been specifically spent on marine debris and floating plastics monitoring in both academia and industrial companies. Currently, creators of Plastic-i\footnotemark[7] have developed a powerful tool (and also the world's first) for mapping ocean plastics via satellites, and won the first-ever Hack The Planet Competition 2021. Moreover. A non-profit organisation, the Ocean Cleanup\footnotemark[8], is developing and scaling technologies to rid the oceans of plastic. Their software - The Plastic Tracker\footnotemark[9] - is an educative tool to predict where the floating plastic patches are heading to. Debris Tracker\footnotemark[10] is designed to make community members contribute data on plastic pollution. Passionate people from all around the world record data on inland and marine debris by using the Debris Tracking App. Ocean Scan\footnotemark[11] is a global database and aims to promote important international collaborations for the marine debris cleaning efforts. They offer access to various in-situ observations and matching remote sensing images of marine litter and debris. Messages in Bottles (MiBs)\footnotemark[12] provides a non-real time interactive tool to visualise information on coastal plastic waste using Sentinel-2 optical imagery (inspired by \cite{biermann2020finding}) in order to connect research and public interests for the purposes of the marine pollution efforts.

Within this developing application area, the machine/deep learning-based remote sensing computational imaging approaches are still in the early stages one of the most important reasons for which is the lack of approved marine debris data sets. Especially in the last couple of years, there has been a good amount of academic outcomes published promoting advanced computational imaging approaches and publishing data sets thanks to the efforts from academic institutions for marine debris and floating plastics detection/tracking purposes.

This paper, via contributing to the literature with a thorough review, highlights current advances based on remote sensing computational imaging for marine debris and floating plastics detection and tracking applications. The rest of the paper is organised as follows: Academic outcomes serving the efforts of marine debris problem are discussed in Section \ref{sec:pollution}. The literature statistics describing the number of publications, etc. is presented in Section \ref{sec:stats} whilst research challenges and future research directions are discussed in Sections \ref{sec:chal}, and \ref{sec:future}, respectively. Section \ref{sec:conc} concludes the paper with a general discussion.

\section{Marine Debris Monitoring Applications}\label{sec:pollution}
Initial efforts for the purposes of detecting, analysing the marine debris problem mostly rely on in-situ approaches via (1) visual surveys \cite{thiel2011spatio,lavers2017exceptional}, and/or (2) airborne data owing to their potential to monitor by the help of high-resolution and real-time information \cite{garaba2018airborne, moy2018mapping, themistocleous2020investigating}. In a recent study \cite{de2021quantifying}, de Vries et. al. present the utilisation of footage of floating plastic debris recorded offshore with GPS-enabled action cameras aboard vessels. Their work provides consistent results of predicted macroplastic compared to the global plastic dispersal models.

Due to its capability in covering large and inaccessible areas, satellite-based remote sensing imagery is considered a useful tool for monitoring the marine environment and detecting/tracking macro-scale marine pollution. An initial study promoting utilisation of satellite imagery in order to detect floating plastics/debris is by Aoyama \cite{aoyama2014monitoring,aoyama2016extraction}. This study firstly concludes that the small size of most marine debris means that it cannot be confirmed directly, even when using high spatial resolution satellite imagery. In order to solve these problems, the author proposed using several candidate debris pixels and making a spectral analysis on these whether there is a discrimination between debris and surrounding ocean pixels. Their approach works effectively in cases where the size or area of marine debris is large enough to affect a change in the captured spectra. Multi-spectral satellite imagery has also been used to validate various important post-events after the devastating tsunami triggered by the Tohoku-Oki earthquake of 11 March 2011. In \cite{matthews2017dynamics}, it has been shown that high spatial resolution satellite tracking reveals faster-than-expected floating debris motions.

A study by Goddijn-Murphy et al. \cite{goddijn2018concept} has shown that plastic litter and seawater develop a reflectance model coming from their spectral signatures and optical geometry. The work in \cite{goddijn2018concept} considers only one type of macro plastic and proposes that the fraction of a plastic surface can be estimated from the surface reflectance, since specifically knowing the clear water reflectance. It has also been reported in \cite{goddijn2018concept} that there are some limitations to this approach such as shading of surface plastic, and the key approach is to select a spectral wavelength in which water leaving light is minor and plastic reflectance is high (approx. 750 $\eta$m). Even though it leads future studies on plastic detection applications, the work in \cite{goddijn2018concept} hence constitutes limitations in natural environments since plastic debris appears in a magnitude of shapes, colours, and sizes. Nevertheless, determining the appropriate reflectance for the plastic makes it detectable in remote sensing imagery.

\begin{figure}[ht]
    \centering
    \subfigure[]{\includegraphics[width=0.54\linewidth,keepaspectratio=true]{figures/spec.png}}
    \subfigure[]{\includegraphics[width=0.45\linewidth,keepaspectratio=true]{figures/bands.png}}
    \subfigure[]{\includegraphics[width=0.7\linewidth,keepaspectratio=true]{figures/pix.png}}
    \caption{(a) Spectral signatures of different materials \cite{biermann2020finding}. (b) Sentinel-2 band characteristics. (c) Example plastic coverage in pixels \cite{topouzelis2020remote}}
    \label{fig:sen2_1}
\end{figure}

Reflectance analysis in \cite{goddijn2018concept} motivated \cite{topouzelis2019detection,topouzelis2020remote} for a further detailed analysis via an approach consisting of both satellite and unmanned aerial imagery for the detection of plastic patches. Topouzelis et. al. \cite{topouzelis2019detection} have created a measurement setup at Tsamakia beach, Greece, which includes a set of three artificial floating plastic targets (PET bottles, plastic bags and fishing ghost nets). It has been shown that plastic litter such as bottles, bags, and fishing nets, reflect light in the near-infrared (NIR) band where clear water absorbs the light. Moreover, it has been reported in \cite{topouzelis2019detection} that the reflectance intensity is also directly related to the amount of plastic in a single pixel of the utilised image (10m x 10m for Sentinel-2). Thus, if water composes more than 50-70\% for a given pixel, the reflection from the plastic pollutant is highly poor in the NIR band. Consequently, remote sensing data is required to have pixels that are filled with more than 30-50\% plastics (an area of at least 30-50 m2). This case can be achieved for the regions of such as coastal areas, where an eddy or a similar reason might combine plastics into larger patches.  However, especially for the ocean, individual plastic patches will potentially be less than the detectable limit \cite{biermann2020finding}. This can be seen as the main limiting factor of the plastic detection research via the optical remote sensing modalities since the Sentinel-2 imagery has a spatial resolution of 10x10 meters. It leads to the detectable floating plastic area to be at least 50m$^2$ since plastic pollutants become spectrally visible if the water composes less than 50\% of the pixel area. In \cite{topouzelis2019detection}, a Sentinel-1 SAR imagery has also been analysed for the same measurement setup, however, it has not been concluded notable results based on the detection of plastics via SAR imagery. The potential main reason behind this is the spatial resolution of 10m and the relatively less penetrable frequency band of the Sentinel-1 satellites (C band compared to L or S bands).

Another work \cite{martinez2019measuring} has deeply discussed the requirements of a specifically-designed remote sensing monitoring system of plastic pollution. It has been reported in \cite{martinez2019measuring} that an ideal remote sensing system would compromise both passive (optical) and active (SAR) satellite modalities, as well as support from UAV based systems. Passive sensors that offer short-wave infrared (SWIR) band measurements are noted as the high potential modality, whilst SAR also specified as a potential for future developments, especially thanks to new SAR platforms (such as the ICEYE) capability to offer a higher spatial resolution, and lower revisiting times.

The very first work on detecting and classifying plastic patches using \emph{solely} optical satellite data has been studied by Biermann et. al in \cite{biermann2020finding}. The work in \cite{biermann2020finding} has two joint objectives: (1)  demonstrating the capability of Sentinel-2 data on detecting floating macroplastics, and (2) classifying macroplastics and other natural materials which are most likely to be aggregated within mixed patches of floating debris. The authors proposed a novel parameter, which is the floating debris index (FDI), in order to analyse sub-pixel interactions of macroplastics with the sea surface to increase the chance of detection of patches floating on the ocean surface. The FDI index is inspired by a previously proposed index of floating algae index (FAI). By replacing the red band measurements with the red edge band (approx. at 740 nm), the authors leverage the difference between the NIR band and its baseline reflectance. The FDI can be represented as
\begin{align}
    FDI &= R_{NIR} - R'_{NIR}\\
    R'_{NIR} &= R_{RE2} + (R_{SWIR1} - R_{RE2})\times \dfrac{\lambda_{NIR} - \lambda_{RED}}{\lambda_{SWIR1} - \lambda_{RED}}\times 10
\end{align}
where $R_{NIR}, R_{RE2}$, and $R_{SWIR1}$ refers to NIR, red edge 2 and SWIR1 bands, respectively whilst similarly $\lambda$ values are wavelength values (in $\eta$m). Sentinel-2 band characteristics are provided in Figure \ref{fig:sen2_1}-(b). The FDI dramatically highlights the plastic and has been found useful in the identification of floating plastics in waterbodies using spectral remote sensing imagery. An example for the visibility of the FDI is depicted in Figure \ref{fig:fdi}.

\begin{figure}[htbp]
    \centering
    \includegraphics[width=0.75\linewidth,keepaspectratio=true]{figures/fdi.png}
    \caption{Representation of FDI index in optical imagery \cite{biermann2020finding}}
    \label{fig:fdi}
\end{figure}

It has also been shown in \cite{biermann2020finding} that using FDI in conjunction with the Normalised Vegetation Difference Index (NDVI) \begin{align}
    NDVI = \dfrac{R_{NIR} - R_{RED}}{R_{NIR} + R_{RED}}
\end{align}
makes it possible to detect differences between plastics, vegetation, driftwood, and seafoam.

Themistocleous et al. \cite{themistocleous2020investigating} set up a pilot study to investigate whether plastic targets on the sea surface can be detected from Sentinel-2 only data. Their setup consists of a target of plastic water bottles with size of 3m$\times$10m in the sea near the Old Port in Limassol, Cyprus. They gathered UAV multi-spectral images during the same time as the Sentinel-2 satellite passes. They have used various important spectral indices such as NDVI, but not using FDI of \cite{biermann2020finding} since the work has been published during the same time interval in 2020. Instead, they proposed a new index called Plastic Index (PI)
\begin{align}
    PI = \dfrac{R_{NIR}}{R_{NIR} + R_{RED}}.
\end{align}
Their spectral analysis on the visibility and detectability of the floating plastics in Sentinel-2 imagery showed that the newly developed PI has been able to identify floating plastics and was selected as the most effective index.

Kikaki et. al. \cite{kikaki2020remotely} has conducted a research over a specific target location - Bay Islands in the Caribbean Sea where remarkable amounts of plastic debris have been reported. Satellite imagery for the time interval of 2014-2019 has been investigated and in-situ data collected with vessel and diving expeditions has been verified via exploiting the spectral characteristics of the floating plastics. This work, even though it is not proposing a technical method to detect and track marine debris, utilises classical spectral analysis approaches, but this time to analyse the source of pollution. According to their findings, the main source of pollution in the target area has been found to be the river discharges from the basins of Honduras and Guatemala. It has also been reported that dynamic sea currents affects the pollution and plastic patches can travel more than 200km. Figure \ref{fig:kikaki} shows an example figure highlighting the source of plastic pollution in Bay Islands in the Caribbean Sea.

\begin{figure}[ht]
    \centering
    \includegraphics[width=\linewidth]{figures/kikaki.png}
    \caption{An example figure from \cite{kikaki2020remotely} shows the source of plastic pollution in Bay Islands in the Caribbean Sea.}
    \label{fig:kikaki}
\end{figure}

Up to this point, all the works were based on Sentinel-2 imagery for the purpose of promoting satellite images in floating plastic detection. Park et. al. \cite{park2021detecting}, instead of Sentinel-2 multi-spectral imagery, utilises very high geo-spatial resolution 8-waveband WorldView-3 imagery in order to observe floating plastic litter in the Great Pacific Garbage Patch (GPGP). They apply various spectral analysis approaches and investigate anomalies to infer presence of
suspected plastic litter.

Salgado-Hernanz et. al., in their bibliographic review \cite{salgado2021assessment}, reveal one more time that the optical sensors seem to be the most optimum to target marine litter. Interestingly, in their work, they also promote SAR utilisation and specifically mention that SAR sensors may detect sea-slicks (areas containing high concentration of microplastics). Furthermore, Hu \cite{hu2021remote} also suggests that it is impossible to detect floating material by all the optical spectra and analysis should be performed over the difference spectra to minimize the impact of variable subpixel
coverage. It is certain that some spectral bands are more important than others for the remote detection of marine debris. Ciappa \cite{ciappa2022marine} proposes a marine litter detection work for target location of North Adriatic during the summer 2020. This work similar with the ones mentioned above uses spectra arithmetic for spectral anomalies of the Red Edge bands, assuming changes of the red edge in pixels where marine litter was mixed with vegetal materials. Table \ref{tab:indices} highlights the most common spectral indices utilised by the works discussed above.

\subsection{Methodological Framework}
Even though all the works published in this area can be somehow considered to be in computational imaging, in this chapter, we mostly focus on works that promote machine/deep learning-based computational imaging techniques such as feature extraction, segmentation, classification for the purpose of marine debris and floating plastics detection.

\begin{table}[ht!]
    \centering
        \caption{Frequently Utilised Spectral Indices}
    \label{tab:indices}
    \begin{tabular}{p{1.5cm}p{5.5cm}p{8cm}}
    \toprule
        & \textbf{Index}		&\textbf{Expression}\\\toprule
\textbf{NDVI} &	Normalized Difference Vegetation Index&	 $\dfrac{R_{NIR} - R_{RED}}{R_{NIR} + R_{RED}}$\\\midrule
\textbf{FDI}& 	Floating Debris Index&	 $R_{NIR} - \left[R_{RE2} + (R_{SWIR1} - R_{RE2})\times \dfrac{\lambda_{NIR} - \lambda_{RED}}{\lambda_{SWIR1} - \lambda_{RED}}\times 10\right]$\\\midrule
\textbf{PI}& 	Plastic Index&	 $\dfrac{R_{NIR}}{R_{NIR} + R_{RED}}$\\\midrule
\textbf{WRI}& 	Water Ratio Index&	 $\dfrac{R_{GREEN} + R_{RED}}{R_{NIR} + R_{SWIR2}}$\\\midrule
\textbf{RNDVI}& 	Reversed Normalized Difference Vegetation Index&	 $\dfrac{R_{RED} - R_{NIR}}{R_{RED} + R_{NIR}}$\\\midrule
\textbf{AWEI} &	Automated Water Extraction Index&	 $4 \times (R_{GREEN} - R_{SWIR2}) - (0.25 \times R_{NIR} + 2.75 \times R_{SWIR1})$\\\midrule
\textbf{MNDWI}& 	Modified Normalization Difference Water Index&	 $\dfrac{R_{GREEN} - R_{SWIR2})}{R_{RED} + R_{SWIR2}}$\\\midrule
\textbf{NDMI}& 	Normalization Difference Moisture Index&	 $\dfrac{R_{NIR} - R_{SWIR}}{R_{NIR} + R_{SWIR}}$\\\midrule
\textbf{NDWI} &	Normalized Difference Water Index&	 $\dfrac{R_{GREEN} - R_{NIR}}{R_{GREEN} + R_{NIR}}$\\\midrule
\textbf{SAVI}&	Soil adjusted vegetation index&	$(1 + L) \times \dfrac{R_{NIR} - R_{RED}}{R_{NIR} + R_{RED} + L}$\\\midrule
\textbf{NDWI2}&	The second normalized difference water index&	 $\dfrac{R_{GREEN} - R_{RED}}{R_{GREEN} + R_{RED}}$\\\midrule
\textbf{NDBI}&	Normalised Difference Build-Up Index&	$\dfrac{R_{SWIR} - R_{NIR}}{R_{SWIR} + R_{NIR}}$\\\midrule
\textbf{FAI}&	Floating Algae Index&	 $R_{NIR} - \left[R_{RED} + (R_{SWIR1} - R_{RED})\times \dfrac{\lambda_{NIR} - \lambda_{RED}}{\lambda_{SWIR1} - \lambda_{RED}}\right]$\\\midrule
\textbf{S2}-\textbf{based}&	Sentinel 2 based index&	$\dfrac{ (R_{RE3} - R_{NIR}) + (R_{NNIR} - R_{NIR})}{R_{RE3} + R_{NNIR}}$\\
\bottomrule
    \end{tabular}
\end{table}

Acuna-Ruz et. al. \cite{acuna2018anthropogenic} propose an approach with the objective to improve the identification of macroplastics through remote sensing over beaches. The authors promote the utilisation of 8-band very-high resolution WorldView-3 satellite products. Five important ML approaches of Random Forests (RF), Support Vector Machines (SVM) with three kernel functions of linear, polynomial, and radial basis function, and Linear Discriminant Analysis (LDA) have been tested in order to classify the beach debris. The results show that the SVM approaches were mostly the best with detection accuracy around 85-90\%.
Fallati et. al. \cite{fallati2019anthropogenic} propose an ad-hoc methodology for beach litter analysis based on the combined use of a UAV and the deep-learning software of PlasticFinder. The utilised software PlasticFinder is a commercial software of DeepTrace Technologies. It consists of several Convolutional Neural Networks (CNN) which are constructed to detect and quantify Anthropogenic Marine Debris (AMD). The approach in \cite{fallati2019anthropogenic} reaches a sensitivity value of 67\% whilst the positive predictive value of 94\%. Despite the considerable performance of the PlasticFinder, this software highly relies on the sunlight conditions of the scene and has serious problems with targets such as footprints or shadows.

The first satellite-only computational imaging approach has been proposed by Biermann et al. \cite{biermann2020finding} and they have utilised a Näive Bayes classification approach for the floating plastic classification. In fact, this approach has two main steps: (1) manual detection of marine debris using FDI and NDVI values, (2) using Näive Bayes to classify plastics among water, seaweed, timber, and foam. Across all five test sites, the proposed approach reaches 86\% detection accuracy of plastics of the pixels detected as marine debris in the first manual step. Kako et. al. \cite{KAKO2020111127} propose a novel approach which is a combination of UAV surveys and deep learning approaches. Via performing edge detection they estimate plastic marine debris on the beaches of Sato and Fukiage in Kagoshima, Japan. The computational imaging approach in \cite{KAKO2020111127} exploits a binary classification on each pixel (either plastic or not) based on 3 layers deep artificial neural network with a performance of less than 5\% error.

Furthermore, Jakovljevic et. al. \cite{jakovljevic2020deep} aim to investigate the applicability of various important deep learning algorithms for automatic floating plastic via utilising UAV orthophotos. They also test to discriminate plastic types by exploring various characteristics such as spatial resolution and detectable plastic size. Utilised deep neural network approaches are semantic segmentation algorithms based on U-Net architecture. Results show that the ResUNet50 architecture achieved the best performance via detecting plastics with more than 85\% of precision.
An automated plastic pollution monitoring approach for the river surfaces using bridge‐mounted camera imagery is presented by van Lieshout et. al. in \cite{van2020automated}. The authors have performed an experimental analysis of five different rivers in Jakarta, Indonesia. The proposed deep learning-based approach consists of two cascaded CNNs for the purposes of segmentation and object detection. For the segmentation stage, the so‐called Faster R‐CNN (trained on the river image data set) was used whilst for the detection stage Inception v2 network (pre-trained on the COCO data set) was used. With the highest 69\% precision of plastic detection, their results demonstrate a promising way of monitoring plastic pollution.

Freitas et. al. \cite{freitas2021remote} propose a hyperspectral imaging-based ML procedure to detect floating plastics. Data set collected with manned/unmanned airborne hyper-spectral sensors have been tested with two supervised methods of RF and SVM. Their results show an automated detection capability of marine litter with up to 80\% precision (50\% recall).
Tasseron et. al. \cite{tasseron2021advancing} present a hyper-spectral laboratory setup to collect spectral signatures of 40 virgin macroplastic items and vegetation.  The system covers a spectral range from visible to shortwave infrared (VIS-SWIR) from 400 to 1700 nm. The experiment setup has given around 2 million pixels each of which has been processed via linear discriminant analyses (LDA). Their results return absorption peaks of plastics (1215 nm, 1410 nm) and vegetation (710 nm, 1450 nm), and provide evidence of the gold spectral indices of NDVI and FDI.

Basu et. al. \cite{basu2021development} utilise Sentinel-2 multi-spectral imagery to detect floating plastic debris in coastal water-bodies in specific test locations in Limassol, Cyprus, and Mytilene, Greece. The utilised Sentinel-2 images are of the date on which the real-world plastic pollution data were collected through experiments at the two study area locations. The authors test four machine learning classification approaches K-means, fuzzy c-means (FCM), support vector regression (SVR), and semi-supervised fuzzy c-means (SFCM). In addition to the 6 Sentinel-2 bands of blue, green, red, red edge 2, near-infrared, and short wave infrared 1, two indices important spectral indices of NDVI and FDI are utilised to develop the ML models. Their results suggest that SVR outperforms all other ML models with around 97\% of plastic detection accuracy.

Kremezi et. al. \cite{kremezi2021pansharpening} leverage the potential of satellite hyper-spectra remote sensing imagery in marine plastic litter detection purposes for the first time in the literature. The authors use PRISMA satellite data with fine spectra but low spatial resolutions. In order to increase the spatial resolution, \cite{kremezi2021pansharpening} proposes exploiting pansharpening with the panchromatic data that enhance spatial resolution. In the experimental analysis, thirteen different pansharpening methods have been tested and the PCA-based substitution method managed to efficiently discriminate plastic targets from water-bodies.

A very recent study by Kikaki et. al. \cite{kikaki2022marida} introduce a benchmark Sentinel-2 dataset for developing and evaluating ML algorithms, called Marine Debris Archive (MARIDA). Publishing the MARIDA data set will surely become a game-changer in marine debris and floating plastic detection research since it is the first dataset based on the multispectral Sentinel-2 satellite data. MARIDA offers the capability to classify various different marine targets, such as marine debris (MD), sargassum macroalgae, ships, wakes, foam, various dissimilar water types, and clouds. The authors have gathered plastic pollution information for the time interval between 2015-2021 from more than 11 different countries all around the world. Based on these ground truth data, Sentinel-2 images collected and annotations have been done via using high-resolution information from Planet and Google Earth imagery. MARIDA data consists of 17/63 Sentinel-2 tiles/scenes, 2597 patches, and 837377 annotated pixels. Among those pixels, MARIDA contains 3339 Marine Debris (MD) pixels in total. In the MARIDA paper, MD was defined as "floating plastic and polymers, mixed anthropogenic debris". Of these pixels, 1625 pixels were digitized and annotated with high confidence, 1235 pixels were labeled with moderate, and 539 pixels with low confidence. Confidence levels were determined by the collection of reports about mass-plastic events in coastal regions. All satellite images were also investigated with very high-resolution satellite imagery where possible. Overall, this increases the validity and makes MARIDA the most comprehensive classification of marine debris to date. From the technical point of view, MARIDA \cite{kikaki2022marida} also presents two different baseline approaches based on RF (has three versions for (1) spectra, (2) spectra \& indices, and (3) spectra \& indices \& land cover map) and U-Net architecture. The results indicate that RF forest approaches outperform U-Net results with $F_1$ scores higher than 0.7 whilst U-Net achieves 0.5. MARIDA data class distributions and an example performance result for the benchmark models are depicted in Figure \ref{fig:marida}.

\begin{figure}[htbp]
    \centering
    \subfigure[]{\includegraphics[width=\linewidth]{figures/marida1.png}}
    \subfigure[]{\includegraphics[width=\linewidth]{figures/marida2.png}}
    \caption{MARIDA data set content statistics and example results for the benchmarking. (a) MARIDA data set class distributions. (b) An example performance result for the MARIDA benchmark models}
    \label{fig:marida}
\end{figure}

Kremezi et. al. in their recently published paper \cite{kremezi2022increasing} propose to enhance the capabilities of Sentinel-2 multi-spectral imagery via performing image fusion approaches with very high-resolution WorldView-2/3 (WV-2/3) images. Their approach has the primary aim to present solutions to the main limitation in the existing approaches that limited spatial resolution of Sentinel-2 hindering the detection of relatively small marine debris and floating plastics. Various image fusion techniques have been tested in \cite{kremezi2022increasing} in terms of preserving spectral and spatial information. Among those, Coupled non-negative matrix factorization (CNMF) has shown to be the best via producing a fused image with clear edges, no blurring, and favorable spectral characteristics. Utilised Fusion-GAN and Fusion-ResNet approaches have also shown great performance for fusion purposes regarding spectral similarity. The superior performance throughout the paper can be listed as the reduction in the required pixel coverage. The smallest detectable target via the best approach in the fused image was noted as 0.6 × 0.6 m2 in size, which is equivalent to 3\% pixel coverage of the original Sentinel-2 imagery with 20m resolution. Despite this notable impact and potential future research direction, their plastic detection approach consists of a high number of false detections. This is actually not special to this specific paper, but nearly all the existing approaches are suffering from false detections which reduces the accuracy and reliability of the detection approach. Considering proposing a detection approach is not the aim of \cite{kremezi2022increasing}, this paper can be noted as a breakthrough for this research area for future works.

Lavender \cite{lavender2022detection} proposes a detection approach using satellite imagery, but this time not only for marine, also for terrestrial environments. This work also produces a data set by manually digitising some of the classes in the land cover maps into plastics, greenhouses, tyres, and waste sites. This data consists of around 1.3 million pixels only around 1\% of which is labeled as plastics. The proposed detection algorithm is fed by two different modalities of SAR (Sentinel-1) and multi-spectral (Sentinel-2) imagery and consists of an artificial neural network (ANN). An RF-based algorithm has also been used to compare the ANN performance. Except for the proposed data set, maybe the most important contribution of this paper in terms of the computational image processing approaches is to propose a post-ANN decision tree. This adds a second classification step to the proposed method in order to fix errors caused by the ANN's incapability to fully capture the radiometric interpretation of the surfaces and to process categorical layers. The post-ANN decision tree and an example before and after this has been shown in Figure \ref{fig:kremezi}. Regarding the detection performance, despite having around 95\% of aggregate average precision, their plastic detection precision is 91\%.

\begin{figure}[ht]
    \centering
    \subfigure[]{\includegraphics[width=0.8\linewidth]{figures/decTree.png}}
    \subfigure[]{\includegraphics[width=\linewidth]{figures/dtPerf.png}}
    \caption{(a) Post-ANN decision tree structure of \cite{lavender2022detection}. (b) Comparison of the classification results before and after the post-network decision tree for the subset of the Solo River.}
    \label{fig:kremezi}
\end{figure}

Booth et. al. in their work \cite{booth2022high} propose a high-precision marine debris mapping algorithm by utilising Sentinel-2 imagery and a semantic segmentation algorithm. Their approach is constructed to detect marine debris and floating plastics from a high-precision point of view. Maybe, in the literature, it is the first time, this approach focuses on improving the precision of the marine debris detection algorithm by keeping recall values high enough to produce a reliable algorithm. The authors in \cite{booth2022high} facilitate semi-automated monitoring of suspected marine debris and floating plastics via a data pipeline named MAP-Mapper that enables the user to provide coordinates for a region of the ocean along with a date time range so that Sentinel-2 data could be downloaded, pre-processed via atmospheric corrections and individual pixels could then be classified using the proposed ML algorithm. Afterwards, as of the outcome of this tool, a plastic density map is produced and enables users to easily interpret model predictions and identify areas of high plastic density in the area being investigated. Their approach promotes utilising the MARIDA data within a U-Net-inspired machine learning approach. Their approach MAP-Mapper-HP reach 95\% precision of marine debris class in MARIDA data set whilst the original U-net based baseline in \cite{kikaki2022marida} was only 30\%. In order to test their approach's applicability, they set three out-of-distribution test locations of the Bay of Honduras, Cornwall, and Lesvos via the data pipeline. Their generated marine debris maps for these test locations are aligned with the existing marine debris gathering information and ground truths. In Figure \ref{fig:henry}, an example marine density map for the Bay of Honduras is depicted.

\begin{figure}[ht]
    \centering
    \includegraphics[width=\linewidth]{figures/oursHond.png}
    \caption{Marine Debris Density Map from \cite{booth2022high} for the Bay of Honduras. The density map shows results for a five-month time interval from 01/03/2022 to 01/08/2022. Coloured dots show detected marine debris pixels, whilst the colour of each hexagonal bin refers to the detected number of cases within that bin. }
    \label{fig:henry}
\end{figure}

\section{Literature Statistics}\label{sec:stats}
This Chapter is going to highlight the evolution of marine debris research with numbers in terms of literature statistics. Our analysis will follow a gradual point of view - from general to special - in three cases that titles, abstracts, and/or keywords include the words:
\begin{enumerate}
    \item ("Marine Debris"),
    \item  ("Marine Debris") AND ("Remote Sensing" OR "Satellite" OR "Sentinel"),
    \item ("Marine Debris" OR "Plastic") AND ("Remote Sensing" OR "Satellite" OR "Sentinel") AND ("Machine Learning" OR "Deep Learning" OR "Artificial Intelligence")
\end{enumerate}

All the statistical analyses are performed via the document search option in Scopus (\url{www.scopus.com}). This means literature pieces that are not indexed via Scopus are out-of-the-scope of this analysis.

\subsection{Marine Debris Research}\label{sec:case1}
\begin{figure}[ht!]
    \centering
    \subfigure[Country based percentages]{\includegraphics[width=0.69\linewidth]{figures/countryAll.png}}
    \subfigure[Yearly trend]{\includegraphics[width=0.49\linewidth]{figures/yearAll.png}}

    \subfigure[Subject area percentages]{\includegraphics[width=0.49\linewidth]{figures/subareaAll.png}}
    \subfigure[Source title based percentages]{\includegraphics[width=0.9\linewidth]{figures/journalAll.png}}
    \caption{Data visualisation for the literature statistical analysis.}
    \label{fig:cases}
\end{figure}

Marine debris research dates back to the 80s according to our analysis which can be seen in Figure \ref{fig:cases}-(1). Even though the number of papers is quite low, it can be taken as evidence of how old marine debris problems and attracted attention in academia. This slow trend continues still for nearly 30 years until the 2010s. 2010 is a critical point since 90\% of the papers published after 2010 in around 12 years. 2019+ is another critical year that 50\% of the works have been published during the last 4 years of time. The main important reason for this is the improved technology in both remote sensing satellite products and artificial intelligence research.

In terms of the subject area, three important areas appear to lead this research, that is Environmental Science, Agricultural and Biological Sciences, and Earth and Planetary Sciences. United States of America (USA) appears to be the leading country in this research via populating approximately 30\% of the papers whilst Marine Pollution Bulletin becomes the leading academic source title via accommodating more than 35\% of the published papers.

\subsection{Marine Debris Research via Remote Sensing}\label{sec:case2}
When we tighten our search keys by adding remote sensing and some other related terms, Scopus returns 109 academic outcomes. Compared to Case 1, there is a dramatic decrease in the number of papers. The main reason is we now specifically look at marine debris research that promotes utilisation of remotely sensed data, such as RGB, optical, and radar imagery. Along with the remote sensing research, Case 1 also includes various important marine debris research in areas of biology, material sciences, chemistry, etc. Now, most of these research are excluded unless they are promoted using remote sensing imagery. It looks like Scopus only indexed 5 works within these keywords before 2010, which makes it again an important milestone date for marine debris research via remote sensing imagery. Similarly, around 60\% of the papers that are highlighted in this case was published in 2019 and thereafter.

The three important subject areas which become prominent for Case 1 are still the best three subjects. However, this time, adding remote sensing details into the play made Earth and Planetary Sciences research area the leader naturally. The USA is still the leading country in this research with more than 40\% papers published by US institutions. Similarly, the Marine Pollution Bulletin (~30\%) journal is a far more widespread academic venue high-likely to see marine debris research via remote sensing imagery.

\subsection{Marine Debris Research via Remote Sensing Computational Imaging}\label{sec:case3}
Case 3 aims to highlight research at the intersection of oceanology, earth sciences, and computer science. We can also mention that the research highlighted under Case 3 is the target research of this review paper. Adding deep/machine learning keywords into the search criteria basically returns us to remote sensing computational imaging approaches that aim to provide solutions to marine debris and floating plastics problems. Our Scopus search returns 56 academic outcomes for Case 3 where the first of which was published in 2016. Computer vision research and the AI revolution can be dominantly felt in the marine debris research area, especially during the last 2 years in which around 65\% of the works were published.

Earth and Planetary Sciences research area is still the leading area whilst Computer science and Environmental Sciences follow it with a high amount of work. There are two important breaking points different from the statistical analyses for Cases 1 and 2. In this case of research, China has become the new leading country whereas USA institutions still publish around 20\% of the papers. It is also important to note that European institutions in Italy, Germany, Spain, and Greece have started to direct this research area by publishing a total of 37.5\% of the papers.
Taking AI into play made The MDPI Remote Sensing journal (a peer-reviewed, open-access journal about the science and application of remote sensing technology) the leading academic venue for this research whilst Marine Pollution Bulletin and the International Geoscience And Remote Sensing Symposium (IGARSS) follows with a similar number of publications.

\subsection{Overall Analysis}
Finally, despite all the efforts statistically mentioned above, marine debris research via remote sensing computational imaging is still in the early stages. In order to understand this, we share two important Scopus searches and the numbers will say it all!

When we remove ("Marine Debris" OR "Plastic") keys from Case 3, Scopus returns 23258 academic outcomes. This clearly means that the marine debris research that promotes remote sensing computational imaging approaches is only 0.24\% within the whole research area. When we go one step down by excluding some non-marine related research from this analysis and replacing ("Marine Debris" OR "Plastic") key in Case 3 with ("Marine" OR "Maritime" OR "Ocean" OR "Water"), Scopus returns 3574 academic outcome leads only to 1.57\% of the research in this area promotes remote sensing and computer vision approaches for marine debris monitoring purposes.

\section{Challenges} \label{sec:chal}
This chapter draws a picture regarding the potential challenges in marine debris and floating plastics detection research area from the point of view of the authors. We are going to reflect on our understanding and experience within this chapter and please note that the challenges reported below are not exhaustive.

\subsection{Optical Imagery Limitations}
As we explained in the previous literature review that the works highlighted mostly focus on optical multi-hyper spectral imagery, which provides a great amount of information whilst having some serious disadvantages at the same time. There are several reasons that can be listed as potential challenges for passive remote sensing systems in marine plastic pollution monitoring.
\begin{enumerate}[1.)]
    \item As mentioned in the above discussion, even though providing data with 13 different spectral bands, the current gold modality Sentinel-2 imagery has up to 10 meters of spatial resolution. This significantly influences the detection capability of methods since the amount of plastic in a single pixel determines the light intensity and defines a lower size limit for floating plastics, which are around 30-50 m2. This lower limit of plastic is quite high considering that the ocean generally has various pollutants combined in a single patch. The sub-pixel indexes (the FDI of \cite{biermann2020finding}) can be thought of as a solution to this problem, but it is still open to improvements for higher performance. Image fusion approaches that exploit high-resolution data along with the Sentinel-2 information will be highly important future research direction as Kremezi et. al. \cite{kremezi2022increasing} have given an example for this via utilising a similar approach which leads to a reduced required pixel coverage of 0.6 m by 0.6 m.
    \item In addition to the previously mentioned fact that optical images are prone to cloud cover and incapable of night-time data generation, optical remote sensing also suffers from lower sampling rates (say high revisiting times). All these reasons limit their capability to collect continuous data, specifically for plastic patches tracking applications. To the best of our knowledge, in the literature, there is a limited number of works that have studied the problem of tracking plastic patches in the oceans. There are several but non-exhaustive reasons behind this, which are required to be addressed.
    \begin{enumerate}[a.)]
        \item In addition to the incapability to obtain continuous optical imagery for tracking purposes, due to atmospheric limitations of the optical sensors, the low spatial resolution of optical sensors also causes a risk in tracking individuals of plastic bits.
        \item Considering the floating plastics are drifting due to winds, sea waves, and currents, developing a robust tracking approach requires modeling these hydrological variables. Since optical sensor wavelengths are not capable of covering the Bragg scattering mechanism, imaging hydrological variables such as gravity waves, swell waves, and ocean currents cannot be possible.
    \end{enumerate}
    \item The limitations of the optical satellite remote sensing data have led the plastic detection research somehow to unmanned air vehicle (UAV) based approaches. Although these works suggest important outcomes for the understanding of the remote sensing imaging of plastic patches \cite{moy2018mapping,garaba2018airborne,topouzelis2019detection}, they are operator and location dependant whilst having high costs and lack of standardisation.
\end{enumerate}

\subsection{Data Related Problems}
In addition to the sensor-related challenges mentioned above, there is also another important problem causing this research area to move slower compared to other computational imaging areas; that is \textit{the data availability and other data-related problems}.

The very first one is the limited number of open-access marine debris and plastic detection data sets. To the best of our knowledge, before 2022, there was only one available data set from Topouzelis et. al. \cite{topouzelis2020remote} under several repetitions of plastic detection contests. Despite providing a great source of information for future studies as is mentioned in the above chapters, the shared data set consists of a couple of Sentinel-2 imagery that includes only a handful of annotated plastic pixels. Thus, its applicability in advanced remote sensing computational imaging approaches naturally remained limited. Thanks to the efforts in this area, during 2022 (very recently) two important data sets have been published \cite{kikaki2022marida,lavender2022detection}. These data sets, even though still suffering from the number of annotated plastic pixels, provide a basis for researchers around the world and we believe that the applicability of the advanced deep-machine learning approaches will be dramatically increased thanks to these two data sets.

As of now, despite having several data sets, we still face an important problem, which is the limitation of annotated plastic and marine debris pixels. Due to their locations in the ocean, and accessibility issues, having ground truth information with a high level of confidence is a great challenge for this research area. The MARIDA data set \cite{kikaki2022marida}, despite having around 0.9 million annotated pixels, only 0.4\% of those belong to the marine debris class. Among those, only half of these pixels (1625 pixels reported in \cite{kikaki2022marida}) are annotated as high confidence which makes the reliable information in the data set even smaller. This limited information causes a lack of learning for the advanced algorithms and we believe this is the reason that nearly all the proposed approaches suffer from low precision, in other words, lots of false plastic/marine debris detections.

Lastly, both having a limited number of data sets and a small amount of high-confidence annotated pixels, advanced computer vision algorithms have not been utilised for the purpose of detecting floating plastics and marine debris. Instead, most of the referenced works above rely on classical approaches such as Random Forests, Naive Bayes, or simple machine learning architectures based CNNs.

\section{Future Research Directions} \label{sec:future}
After highlighting several important challenges in this area in the above section, now, we share potential future research directions.

\subsection{Utilisation of SAR Imagery}
Considering the limitations and challenges mentioned above, it should also be admitted that the detection and monitoring of plastic pollution via remote sensing imagery are still in their early stages, especially in terms of multi-modality and promoting other sensor information. All the efforts in the last decade, lead to the development of pioneering works mentioned above. Optical imagery, though being the modality that displays the earth as the human eye sees it, will, unfortunately, be at the mercy of the clouds and the amount of sunshine. However, SAR system constitutes an active sensor, which emits microwaves towards the Earth and receives the back-scattered signals from the surface. Since SAR utilises larger wavelengths (1cm to 1m) compared to the optical sensors (near-visible light or 1 micron), it is able to image at both day and night, and in almost all-weather conditions through clouds and storms (while optical sensors are not). SAR imagery provides us with useful information about forest biomass, and crop cover types since their ability to sensitively discriminate against the target surface. Furthermore, SAR images also provide useful information on ocean state, including wind speed and direction, gravity waves, swells, currents, sea-ice structures, and meteorological and environmental conditions in bodies of water. Thanks to its aforementioned capabilities, SAR imagery recognised as the gold standard and superior to the optical imagery where the literature abounds various applications which utilise SAR imagery as the main source of information, such as detecting/tracking oil spills \cite{brekke2005oil, karantzalos2008automatic, kostianoy2006operational, li2010oil, shirvany2012ship, zhao2019detecting}, assessment of sea surface signatures \cite{karakucs2020rici, karakucs2019ship2, karakucs2019sim, graziano2016wake}, ship detection and tracking \cite{gao2020autonomous, kartal2019ship, liang2019destination, mazzarella2013data, pappas}, and ice sheets tracking.

SAR imagery has various advantages compared to the passive remote sensing modalities, which can be listed below:
\begin{enumerate}[1.)]
    \item Several European missions (e.g., the Italian COSMO/SkyMed, the German TerraSAR-X, the UK's NovaSAR-1, or the Finnish ICEYE) have developed a new generation of satellites exploiting SAR to provide spatial resolutions previously unavailable from space-borne remote sensing (up to 0.25 meters). Investigating cm-scale pixel sizes via remote sensing modalities is crucially important to detect and classify the plastic pollutant since these spatial resolutions are not suitable for optical remote sensing, and operating UAV-based approaches with cm-scale resolutions are extremely expensive, operator and location dependent. Conversely, high-resolution SAR data is continuously obtained for some other monitoring applications regardless of our intention to use it. Taking these high-potential, yet less-demanded, sources into play for plastic detection yields crucial importance for the future role of remote sensing technologies in plastic pollution monitoring.
    \item New SAR technology provides larger swath-ranges compared to optical imagery. This is one of the reasons that makes SAR preferable to optical imagery, specifically for monitoring applications.
    \item Thanks to its day-night imaging capability, SAR provides up to 12 hours of temporal resolution whilst the Sentinel-2 optical imagery has a revisiting time of only 2 to 5 days. High-sampling rates, high temporal resolution, or smaller revisiting intervals can be listed as the primary need for monitoring/tracking plastic patches in the ocean. Utilising this source of information would help us to develop robust tracking algorithms. On the other hand, this also will serve the community with the understanding regarding how the plastic patches behave in the open ocean, and why places such as the remote islands at the Pacific Ocean like Henderson Island becomes a gathering point for ocean plastics \cite{lavers2017exceptional}.
    \item SAR sensors operate with various frequency bands (P-S-L-C-X), and each of these bands has various advantages compared to the others. As we mentioned above, SAR has been analyzed in a limited number of works for plastic detection and recognition in the literature to date. However, these analyses are limited only to Sentinel-1 SAR imagery, which images C-band data with 10-m spatial resolution. Since (i) different frequency bands will therefore develop different interactions between the plastic pollutant and the sea surface waters, and (ii) different penetrable frequency bands such as S (NovaSAR-1), L (ALOS2) and X (TerraSAR-X, ICEYE, etc.) have never been utilised in these analyses, SAR imagery is yet unexplored regarding its plastic detection capability. An example demonstration of the penetration capability of different SAR frequency bands is shown in Figure 4. This demonstration shows the L-band penetration capability to e.g., determine forest floods. To date, a similar analysis has not been studied for sea surfaces including plastics and various other natural materials.
\end{enumerate}
\begin{figure}[htbp]
    \centering
    \includegraphics[width=\linewidth]{figures/sarBands.png}
    \caption{SAR frequency band penetration example \cite{ottinger2020spaceborne}.}
    \label{fig:sarFreqBands}
\end{figure}

Drawbacks: Despite the advantages compared to optical imagery, an important problem degrading statistical inference from SAR imagery is the presence of multiplicative speckle noise, which is not the case in optical imagery. The received back-scattered signals sum up coherently and then undergo nonlinear transformations. This in turn causes a granular look in the resulting images, which is referred to as speckle noise \cite{kuruoglu2004modeling}. Speckle noise may lead to the loss of crucial details in SAR images and cause problems in the processing of these images such as in feature detection, segmentation, or classification. However, speckle noise is a well-studied problem for decades and can be dealt with and removed by precisely determining the statistical characteristics of SAR images as in \cite{achim2006sar, karakucs2019cauchy2, karakucs2020GGRician, karakucs2018generalized, kuruoglu2004modeling}. Despite its well-studied nature, it is also a potential problem of SAR imagery utilisation in marine debris monitoring since the interaction between plastic reflection and speckle noise has not yet been reported.

\subsection{Applicability of New Generation Satellites}
Mentioning the drawbacks of optical multi/hyperspectral imagery above does not specifically mean that their era has already passed. Apart from the new technology satellites mentioned above for SAR imagery, in the last couple of years, new technology satellites for optical imagery have also been launched. Worldview-3 which is an imaging and environment-monitoring super-spectral, high-resolution commercial satellite sensor from Maxar, can be named among those technologies. Even though it has 8 years of history, due to being a commercial satellite, its usage has been limited to various remote sensing applications let alone marine debris monitoring. This instrument collects images at 0.31 meter panchromatic and 1.24 meter in the eight VNIR bands, 3.7m in the eight SWIR bands whilst having additional bands for enhanced multi-spectral analysis (coastal blue, yellow, red edge, NIR2) designed to improve segmentation and classification of land and aquatic features. Since Worldview-3 products started to be delivered by the ESA, their applicability has increased, and recently researchers reduced the smallest detectable marine debris target to 0.6 × 0.6 m2 in size, which is equivalent to 3\% pixel coverage of the original Sentinel-2 imagery with 20m resolution. We believe satellite products like Worldview-3 multi-spectral imagery will direct following years of research for floating plastics and marine debris monitoring via filling the gaps in low Spatio-temporal resolution of the current gold standard - the Sentinel-2 imagery products. An example of WV-3 and S-2 comparison for the same area is given in Figure \ref{fig:wv3}.

\begin{figure}[htbp]
    \centering
    \includegraphics[width=\linewidth]{figures/wv3.png}
    \caption{(left) RGB WV-3 image with 4 m spatial resolution, (right) S2 image with 10 m spatial resolution. \cite{kremezi2022increasing}.}
    \label{fig:wv3}
\end{figure}

\subsection{Computational Imaging Solutions}
As we mentioned previously, this paper aims to review and discuss current advances in computational remote sensing imagery for the proposes of marine debris and floating plastics detection. In Section \ref{sec:pollution}, we specifically discussed computational imaging approaches utilised for this purpose, the conclusion leads to an important fact that existing approaches mostly rely on classical machine learning approaches such as Random Forests, Naive Bayes, and straightforward deep neural network structures. However, one of the most active research areas of deep learning and artificial intelligence is computer vision and highly advanced approaches are being developed nearly every day. Despite this fast-changing and active nature of computer vision, remote sensing part of it still coming from several years back. Several reasons for this have been highlighted above as a lack of open access data sets, and a low amount of annotated plastic pixel existence.

The first potential computational imaging research direction for marine debris monitoring that would be highly active in the following years is multi-modal AI and multi-sensor image fusion approaches. Various environmental applications have benefited from multi-modal image fusion by exploiting complementary features provided by different types of remote sensors. Specifically, for active-passive sensor data fusion, passive-optical sensors play the role of feeding the system with a high spectral resolution of the earth's surface which are useful for image analysis whilst active remote sensing sensors usually provide sufficient textural and structural information of observed objects \cite{zhang2022multispectral}. Some example remote sensing applications that exploit multi-sensor information and obtain considerable performance increase can be listed as land use/cover mapping \cite{ma2022amm}, air pollution detection \cite{scheibenreif2022toward}, building footprint extraction \cite{shermeyer2020spacenet}, and maritime vessel detection \cite{farahnakian2020deep}.

Another computational imaging research direction will be guided by the fact that the marine debris monitoring area is suffering from a low amount of annotated data. The same existing fact has been a great driving force for the development of semi-, weakly-, and self-supervised learning (reduced supervision or minimal supervision) computational imaging approaches especially in the medical imaging area, which has the same annotated data problem as remote sensing imagery applications. Along with the advances in multi-modal fusion approaches, developing minimal supervision techniques for marine debris monitoring is a crucial step, and we believe academic literature will gradually get involved in this area in order to leverage not annotated data simultaneously with the limited amount of high-confidence marine debris pixels. The weakly supervised approach proposed in \cite{kikaki2022marida} is a good starting point for this, but of course not enough for high-precision marine debris monitoring software development tools. Ma et. al. in their land cover mapping application paper \cite{ma2022amm} have shown that without losing much performance accuracy, multi-modal sensor information usage in a parallel manner can reduce the amount of required training data up to 1/20. We believe this work, and other similar minimal supervision-promoted remote sensing computational imaging approaches in the literature might guide researchers in the following years.

\subsection{Tracking the Source of Pollution}
To the best of our knowledge, the literature for marine plastic pollution monitoring does not yet have a complete tracking method due to the incapability of continuous data flow from optical sensors. On the other hand, resolution limitations and small sizes of plastic bits can be listed as other reasons for not developing a complete set of tracking approaches. The tracking literature is mostly dominated by the Bayesian approaches, especially for the last couple of decades that are based on the sequential Monte Carlo (SMC) algorithm. There have been proposed various advanced versions of this algorithm, though the SMC-based tracking approaches still suffer from data degeneracy and high particle variance problems. Currently, optimal transport theory-based solutions are getting prominent in tracking approaches \cite{corenflos2021differentiable,wu2022ensemble,hao4129781particle}. However, there is no such advanced methodology developed for remote sensing imagery so far. Thus, the marine debris tracking literature has an open and hot topic via the improved and novel version of the sequential Monte Carlo (SMC) algorithm, whilst leveraging the optimal transport theory to mitigate the effects of high-variance particles. Additionally, exploiting the utilisation of irreversible Bayesian samplers to improve the mixing properties and convergence speed of Bayesian plastic tracker algorithms can be listed as another important research direction.

Detecting where marine debris and floating plastics are located and tracking where they are heading are of crucial importance, however, there is one more important aspect about this: \textbf{the detection of the sources of the plastic.} Detecting and acting to clean plastic pollution from the oceans is an important action but requires repetitive efforts as soon as we do not stop the source of the pollution. Hence, the effort by Lavender \cite{lavender2022detection} via proposing a date set for plastic pollutants both on the land and coastal areas can be seen as one of the starting points of this effort since analyzing the source of the pollution requires discriminating the pollutants not only on land but also in small inland waters such as rivers. Similarly, the work by Sasaki et. al. \cite{Sasaki20226391} exploiting coastal debris detection via machine learning approaches and utilising Maxar WV-3 imagery accommodates the same importance in order to clearly discriminate the debris on the sea surface and on Land.

As mentioned in the above sections, floating plastics' trajectory in the ocean has highly been affected by ocean surface signatures and currents. Combining SAR and optical imagery information with the ground-breaking statistical samplers for tracking approaches will make analysing ocean currents, and wave structures possible, which can lead to developing back-tracking approaches to predict the sources of plastic pollution. Lastly, In order to mitigate the drawbacks of small-sized plastic bits and relatively low spatial resolution remote sensing data, group (or namely cloud) tracking-based approaches \cite{mihaylova2014overview,septier2009tracking} are of great importance.

\section{Conclusions}\label{sec:conc}
The ocean and waters of the planet Earth have been facing various threads during the last couple of decades. Whilst Global warming-related problems and some power-holders ignorance about it are still there, the Earth also dealing with pollution problems in the ocean, air, etc. Along with the technological developments, especially in the machine learning and artificial intelligence area in the last decade, computer vision approaches started to play some crucial roles to guide initiatives and decision-makers to act for the aforementioned problems. Marine debris and floating plastics are one of those problems, and remote sensing computational imaging efforts can help us to hear the call of the ocean!

This paper via presenting a review of the academic efforts in the literature aims to highlight developed computational remote sensing imaging approaches for the purpose of detecting marine debris and floating plastics. After carefully discussing each highly valuable academic outcome, we also highlighted the literature statistics for marine debris monitoring purposes in the last decade. Finally, depending on the authors' experience and research on marine-related remote sensing computational imaging approaches, various challenges, and potential future research directions have been listed (non-exhaustive).

\bibliographystyle{elsarticle-num}

redpajama_set_name: RedPajamaArXiv


text:
Q: Twisted Server: How to keep track of the number of requests on the server? There is a Twisted server which gives responses to clients' requests. The "LineOnlyReceiver" is used for the protocol design. The process should go as following:
A client sends a request to the server (such as "TEMP?" asking the temperature), then the number of request on the server should increase by 1. After the server sends back the response, the number should decrease by 1. However, during the request ("TEMP?") being processed by the server, there could be other clients sending requests to the server, the number of requests on the server should vary with time.
So how to keep track of the number of requests on the server on-the-fly? Currently, the "lineReceived" method is used to receive requests from clients and send back responses (other codes omitted):
Class SimProtocol(LineOnlyReceiver):
    def lineReceived(self, request):
        self.factory.taskNumber[request.strip()] += 1
        print 'Task number: ', self.factory.taskNumber[request.strip()]
        time.sleep(10)
        resp = self.factory.getResponse(request.strip())
        self.transport.write(resp)
        self.factory.taskNumber[request.strip()] -= 1
        print 'Task number: ', self.factory.taskNumber[request.strip()]

The 10 seconds delay is added in the middle on purpose to simulate the processing time of the requests. During the 10 seconds, there are several other requests being sent to the server, but the server didn't respond to other requests until it finishes the current one. In other words, the "print" statements always show the task number is 1, 0, 1, 0..., no matter how many clients are sending requests.
So what kind of design approach (probably a totally different one, like getting the reactor queue size could do?) should be used to accomplish this functionality (keep track of the number of requests on the server on-the-fly)?

redpajama_set_name: RedPajamaStackExchange


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Bitcoin Embassy New Hampshire, which recently launched inside Route 101 Local Goods at 661 Marlboro Street in Keene, is not the first Bitcoin Embassy in the United States, but it is the second one! The first was opened earlier this year in Atlanta. There are others in various parts of the world but Bitcoin Embassy New Hampshire is the only one in the Northeast part of North America.
The Bitcoin Embassy will be a place for learning, with "Bitcoin 101" classes already available, teaching the basics of Bitcoin and cryptocurrency. Classes are pay-what-you-can-afford, which means if you can't afford to pay, you can still attend for free. Drop in to take a tour at 661 Marlboro St in Keene and sign up for a class, or visit online at BitNH.me.
Bitcoin has been very volatile this year. The cryptocurrency is now down to $6,500 A tremendous loss from it's record breaking high this year. If bitcoin remains volatile and continues to fall, I predict the business who accept bitcoin will stop taking it or increase fees.
You know Jacks, that link you klndly posted for us really doesn't help your argument in any way at all. In fact, if anything, it makes it perfectly clear right off the bat that the ONLY thing falling with bitcoin these days IS its volatility. You do understand why that's not necessarily a bad thing, now don't you my sweets?
Jacks really is a swell guy, isn't he ZX81? But I just don't see how any of that is possible. Especially now that Jacks has gone back to his old habit of not responding to my "rants, childish raves, and basically stupid statements." It's absolutely heartbreaking, isn't it?
Jacks o jacks. I'm still doing wayyyy better with my Bitcoin holdings than I ever did with dollars. Dollars only go down. Bitcoin tends to go up over time. Stupid people like you will be easily deprived of wealth and demand theft thinking you'll personally gain from other peoples hard work. The reality is you are better off NOT stealing from other people, but your too stupid to realize this. When you demand the government steal to support whatever social cause you have some private entities benefit most and what you could of done for yourself now costs more as you pay for it via taxes. It's particularly bad for the poorest in our society and yet these people stupidly demand more of it. Now I wouldn't invest in Bitcoin or crypto generally, but that's another story. Other crypto currencies like Dash are great for daily use and Bitcoin still seems optimal online. You can benefit from crypto currencies without being an "investor".
With Jack's distraction I forgot to say nice! I'm glad to hear the embassy is open. I've checked it out myself the other day and there is a lot of cool crypto merchandise available for sale too. I got myself a Bitcoin jacket, some tech books, a few t-shirts, and other swag. I'm glad to see the enthusiasm for freedom and prosperity is alive and well in Keene in spite of the authoritarian socialist scum who run our town and drive businesses and people away.
kk – bitcoin is volatile. It's worth has decreased and continued to decrease for several months. There is no proof it will climb back to what it was. User fees are going up and this type of currency is only good for those who have computers. If you want to do the crypto thing it doesn't bother me. The rest of your rants, raves and insults fall on deaf ears..
Jacks my sweets, does this sentence look familiar to you?
It should. It's the first sentence in that Coindesk article you posted for us on Friday.
I'm surprised you're not applauding this recent development, Jacks. It's giving me the sneaking suspicion that you don't know what "volatility" actually is, pookums.
Now now Drac. Don't be too hard on jacks. You know full well he is retarded and such big words he won't understand.
Yes- that is a fair statement. However keeping things in perspective the Canadian dollar fell 40% in four years to the US dollar. Numerous other countries have seen hyper inflation as well. The difference here is Bitcoin has over the long haul gone up over time so even if you lost some money here or there those who continue to use it will have gained far more than they have lost over the long haul. Can't say that for any hard currency that I'm aware of in the modern era.
It's actually been very stable for quite a long time now. You see jacks that tends to be how things go. Things tend to remain pretty stable for long periods of time and then go up or down depending on whats happening in the world.
Correct. There is also no proof that the US dollar won't collapse. Not sure what your point is with this. While history is no guarantee of future results those have placed there bets on crypto long term haven't done poorly.
Actually they aren't. Your conflating what happened a long while back over a handful of months. Things settled back down and while fees are still higher on Bitcoin they aren't significant for most transactions. Certainly less than the taxes in many places and really nobody is using Bitcoin for local purchases anyway. It's been all Dash now in NH for quite some time. Which has fees of a penny at best.
Technically you are correct. 95%+ of Americans and 65% of the worlds population today have access to a computer. It's called a cell phone. And guess what jacks. That's exactly what you need to utilize crypto currencies. Do you lack a cell phone? Is that why you are so anti crypto? How old are you jacks? Have your parents not let you get one yet? Maybe if you improve your behavior a bit they'll change there mind.
Sure seems like it does. You care enough to stand by and comment on a site that is heavily focused on it and New Hampshire when you don't live in the state and you don't like anything that gives people more freedom.
Clearly. You haven't understood a word of anything anybody here has said. It's like you aren't even able to read.
That's right Mr. Hmmm! Bitcoin farms! Now you're getting it!
Personally, kk, I get quite a kick out of watching Jacks's level of apprehension rise every time an article is posted about bitcoin. He absolutely hates the fact that his political rivals are doing well financially, you know.


Bright, juicy forest fruit aromas combine with peppery spice and a silky smooth texture in this vibrant expression of the local Lagrein grape.
At its best with rich and spicy appetizers, it enhances first course dishes with ragù sauce, lasagna, game main courses and grilled meats.
This flagship Lagrein is grown along the Adige valley. The vines are trained in the pergola style, giving optimum ripening while preventing the risk of sunburn.
After crushing and destemming, fermentation occurred at a controlled temperature of 26°C for twelve to thirteen days. The wine was aged for ten months in three-year-old French oak barrels.


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About Us » Executive Officers » Brian Delfino
Brian Delfino, Vice President of Direct Claims
Brian Delfino is Vice President of the Claims division. Brian joined Grinnell Mutual in March 2017 as director of Personal Lines Claims and was promoted to assistant vice president of that department in 2018.
Delfino has an extensive claims leadership background and held several executive positions with the Kemper Insurance Group. Delfino has a Business Management degree from Rhode Island College, and attended the Executive Development Institute at the University of North Carolina. He also holds the Associate in Claims (AIC) designation.

redpajama_set_name: RedPajamaCommonCrawl


text:
Ampermetrul este un aparat de măsurare a intensității curentului electric ce trece printr-un conductor sau un circuit electric.

Există ampermetre folosite pentru curent continuu (c.c.) și ampermetre pentru curent alternativ (c.a.). Unitatea de măsură pentru intensitatea curentului electric este, în SI Amperul. Pentru a măsura curentul ce trece printr-un element (componentă) de circuit, ampermetrul se montează în serie cu acesta. Dat fiind că în timpul operației de măsurare, prin ampermetru trece curentul de măsurat, rezistența lui electrică internă (constructivă) trebuie sa fie cât mai mică posibil (putând ajunge până la 10-3 Ω), pentru o bună precizie și pentru limitarea pierderilor de energie nejustificate.

La aparatele analogice indicatoare, deviația sistemului mobil care este legat rigid cu acul indicator al instrumentului, este cu atât mai mare cu cât valoarea numerică a curentului de măsurat este mai mare.
Această deviație este determinată de un cuplu de forțe de natură electromagnetică (la ampermetrele electromagnetice și magnetoelectrice) sau de natură electrodinamică (la ampermetrele electrodinamice).

Clasificare
După natura curentului măsurat, ampermetrele pot fi:
 ampermetre de curent continuu;
 ampermetre de curent alternativ;
 ampermetre de curent continuu și alternativ.

După principiul de măsurare, ampermetrele pot fi:
 magnetoelectrice: cu magnet permanent și bobină mobilă;
 electromagnetice: cu fier mobil;
 electrodinamice
 aparate de inducție: bazat pe acțiunea câmpului învârtitor al curentului asupra unui disc de cupru sau aluminiu parcurs de curenți turbionari induși;
 termice: bazat pe efectul dilatării sau deformării unui bimetal.

După gradul de precizie, ampermetrele pot avea următoarele clase de precizie: 0,001; 0,002; 0,005; 0,01; 0,2; 0,5; 1; 1,5; 2,5.

În funcție de valoarea intensității curentului măsurat, există: microampermetre, miliampermetre, ampermetre și kiloampermetre.

Eroarea de măsurare
Ampermetrele sunt caracterizate de eroarea de măsurare (care determină clasa aparatului) și de valoarea rezistenței interne.
Astfel, ampermetrele cu sistem magnetoelectric sunt de cea mai înaltă clasă.

Pentru calculul erorii de măsurare se consideră un circuit de curent continuu alimentat la o tensiune electromotoare E.
Dacă R este rezistența consumatorului, iar r cea internă a alimentării, atunci intensitatea curentului din circuit este:

Introducând în circuit ampermetru de rezistență internă Ra, valoarea curentului scade și va fi:

Eroare absolută datorată inserării aparatului de măsură în circuit este:

Se observă că eroarea este cu atât mai mică cu cât rezistența internă a ampermetrului este mai mică.

Extinderea domeniului de măsurare
Pentru extinderea scalei de citire, în paralel cu instrumentul se montează un șunt (în cazul curentului continuu) sau legarea acestuia se face prin intermediul unui transformator (în cazul curentului alternativ).

Rezistența șuntului se stabilește cu relația:

unde n este raportul dintre curentul de măsurat și cel maxim de pe scala aparatului.

Legături externe
  Măsurarea curentului electric cu ajutorul ampermetrului - www.circuiteelectrice.ro
  Ampermetrul, structură și principiu de funcționare - www.circuiteelectrice.ro

Aparate electrice de măsură
Curent electric

redpajama_set_name: RedPajamaWikipedia


The battle royale genre has catapulted to the forefront of the games industry thanks to Player Unknown's Battleground and Fortnite trading blows. PUBG set a record for concurrent players with 3.2 million in January, only to be topped by Fortnite less than a month later with 3.4; over the past 90 days the two games have occupied the No.2 (PUBG) and No.3 (Fortnite) slots for most watched and streamed games. There's two different PUBG esports leagues in Seoul, South Korea, and a $50,000 PUBG Invitational will feature prominently at IEM Katowice this Saturday and Sunday.
But there's a more important question than which game will reign supreme as the most popular battle royale esports title: how do you broadcast an esport where 100 players drop into a massive arena to fight and the best action takes place in a highly unpredictable and uneven fashion until the last person or team is left standing?
The answer: take a few pages from the game of golf.
To date, every competition organizer that has hosted a Battle Royale tournament is approaching it like any other esport-a live casting duo covers the whole map as observers franticly bounce from player to player to try to catch pivotal moments. In order to keep viewers up to date, casters frequently comment on the kill field to reflect on action occurring off-screen. It gets better as the player base and map shrinks simultaneously, but for a large percentage of the time the viewing experience is left wanting; there are a lot of spaces where little to nothing is happening.
This may seem like a problem endemic to the genre itself, but it isn't. Battle royale esports need to take a page out of golf's spectating playbook and re-think how the esport is broadcast, and these games have the potential to become the most popular and watchable esports in the world.
For those unaware, a golf tournament is played over the span of days on the same course. The field is cut in half at the midway point based on performance, and the number of players actually in competition for the title narrows further as play continues.
Casting pairs are stationed throughout the course to focus on the action on a particular hole or set of holes, with a main broadcasting duo weaving the overall narrative of the event. In the beginning, play focuses on the most famous players in the field, though the broadcast narrows in on leaders as the event goes on. Since action occurs throughout the course in real time, the broadcast frequently flashes back to important or impressive shots that occurred off-screen.
So how does this translate into a battle royale game?
1. At the beginning of a map, don't try to follow all of the action. Hone in on the most popular/best teams to start building storylines around the competitors about which the audience cares the most.
2. Have a casting duo that is responsible for the macro event and narrative, but employ a handful of other casters who focus on a narrow portion of the map, players or teams so they are able to fully track that action and quickly update the viewer should the broadcast cut to their area. What players have the best loot? Who has the favorable terrain? Where are conflicts likely to occur as the circle narrows and players are forced to travel?
3. Remove the kill feed (or at the very least stop having casters focus on it) and don't be afraid to flash back to important action or funny moments. The beginning of a map is chaos, and action will naturally be missed. But until you get to the end, the live action is far less important than ensuring the viewer is consistently watching compelling content.
4. Use a bottom line ticker and scoreboard that focuses on the players overall positioning for the event, not just the map itself. Standing in an individual map matters, but the results of the overall tournament should always be paramount for the broadcast-no one cares that Dustin Johnson is shooting a 65 on the final day if Tiger Woods is going to win the overall event by finishing with a 68.
5. This is not in the hands of the competition organizer, but these games would also benefit from more predictability. Knowing item drops and the sequencing of the poison circles in advance would allow competitors to plan out a strategy and help broadcasters predict where action will occur in order to weave a more compelling and comprehensive narrative as the map unfolds. Different circle patterns also create different competition patterns for the same map, much like evolving hole/tee placement on a golf course.
This analogy isn't perfect, and I'm certainly not saying anyone should create the golf of esports. But if we could take cues from the broadcast philosophy, we have a recipe for some of the most engaging esports content around.


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filippomorace
filippomorace » Electronic » David - Am I Normal?
David - Am I Normal? Album
David Hi Nrg Synth Pop 1983
Performer: David
Title: Am I Normal?
Style: Hi NRG, Synth-pop
Am I Normal? (Disco Version)
STL 11 David Am I Normal? (7", Single, Blu) Stiletto Records STL 11 UK 1983
STLT 11 David Am I Normal? (12") RCA, Stiletto Records STLT 11 UK 1983
STL 11 David Am I Normal? (7", Bla) RCA STL 11 UK 1983
Am I Normal 1979. Billy Liberty. Язык: Русский. Страна: США. With Joel Doolin, David Carr, John Perrigo, Jerry Gershman. 13-year old Jimmy has a lot of questions about his changing body. His often hilarious search for answers uncovers the facts about male sexual development, and raises important issues about peer pressure and readiness to date. Title: Am I Normal: A Film About Male Puberty 1980. Who Am I - David Haack. Лента с персональными рекомендациями и музыкальными новинками, радио, подборки на любой вкус, удобное управление своей коллекцией. Who Am I. Исполнитель: David Haack. 2009 alternative. AM I NORMAL a series of monthly nights Revolver Seattle 1514 E Olive Way every first Wednesday from 9pm till close . New article featuring the inimitable Ms Dalle. a subversive icon previously highlighted by AM I Normal. See you tonight Never afraid of controversy, the French actor talks about her love affair with Rupert Everett and all things English, her passion for punk and why Jesus is sexy. Listen to Am I Normal for free online and get recommendations for similar music. See the latest images of Am I Normal, including album covers and cover photos. Don't want to see ads Upgrade Now. Similar Albums. Whimsical Weather. Cirrus Bay. 212 listeners. Город: London, England. I am David is a 1963 novel by Anne Holm. It tells the story of a young boy who, with the help of a prison guard, escapes from a concentration camp in an unnamed Eastern European country according to geographical clues, probably Bulgaria and journeys to Denmark. Along the way he meets many people who teach him about life outside the concentration camp. The book, originally written in Danish, has been translated into several languages, including Norwegian, Swedish, Finnish and German all 1963, Dutch. David - Am I Normal. Wiki Lyrics David. The Human League. Life On Your Own. Diana Ross And The Supremes. You Keep Me Hangin' On. Find i am david from a vast selection of Records. Vintage David Hammond I Am The Wee Falorie Man Record Album Vinyl LP. or Best Offer. David - am I normal - rare 7 45 promo vinyl record w pict slv - 1983. Am I Normal is the first episode of the second season of Big Mouth. It is the eleventh episode of the series overall. It premiered on October 5, 2018. It was written by Andrew Goldberg and directed by Bob Suarez. Andrew struggles to blend in after a growth spurt, Nick worries his hormones are out of whack, and Jessi and Jay get a taste of the teen runaway life
David Rychener talks about his music style, delivery and inspiration. A Western Music Performer with the delivery of a true cowboy in the perfect setting David performing in the CTA Red Line Subway Chicago. Feb Performer - The David James Band. Лента с персональными рекомендациями и музыкальными новинками, радио, подборки на любой вкус, удобное управление своей. This page is a blatent attempt to promote the David Coffin brand. David Pearl born 1 March 1960 is a performer, author, director and public speaker. His book Will there be Donuts was included by The Times in their list of top 10 Summer Reads of 2013. He is the founder of the Pearl Group, Opera Circus, Lively Arts and Impropera companies. Pearl was first talent spotted in a North London primary school, which led to him making his singing debut with Plácido Domingo at Covent Garden, and subsequently worked at the Royal Opera House as the boy soprano soloist for 4. Listen to music from David like Wild At Heart, I Don't Care & more. Find the latest tracks, albums, and images from David. Featuring the dreamy and spacey females vocals of Deborah Kelly, their music speaks of the ideals of the youth of the time and features some fine horns, electric fuzz guitar, great orchestration, read more. The classic Canadian band that released the great Flower Power Pop LP David in 1969. Featuring the dreamy and spacey females vocals of Deborah Kelly, their music speaks of the ideals of the youth of the time and features some fine horns, electric fuzz guitar, great orchestration, and backed by outstanding keyboards. David Michael Frank, Composer: Out for Justice. David Michael Frank was born and raised in Baltimore, Maryland where he studied classical piano and composition at the Peabody Conservatory of Music. After graduating from Nothwestern University, David moved to New York, where he became Broadway's youngest conductor. David Firsts latest album can be listened to as a single piece lasting over two hours, or as discrete scenes that can be mixed up and played like a pop Dawn BottomsThe New York Times. David Firsts new album, The Consummation of Right and Wrong, attempts to reconcile ideas about melody and sustained tones. Oborin Lev, Oistrakh David. Publication year. Soderzhanie David Oistrakh, Lev Oborin. The Sonatas For Piano And Violin Sonata No. 1 In D Major, Op. 12 No. Allegro Con Brio Sonata No. 1: II. Tema Con Variazioni. Shelf ID
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