Datasets:
DynamicEarthNet-video: Daily PlanetFusion Image Cubes Compressed as Videos
Description
๐ฆ Dataset
DynamicEarthNet-video is a storage-efficient re-packaging of the original DynamicEarthNet collection. The archive covers seventy-five 1024 ร 1024 px regions (โ 3 m GSD) across the globe, sampled daily from 1 January 2018 to 31 December 2019. Each day is delivered as four-band PlanetFusion surface-reflectance images (B04 Red, B03 Green, B02 Blue, B8A Narrow-NIR). Monthly pixel-wise labels annotate seven land-cover classes: impervious, agriculture, forest, wetlands, bare soil, water and snow/ice.
All original GeoTIFF stacks (โ 525 GB) are transcoded with xarrayvideo to 12-bit H.265/HEVC, yielding dramatic size savings while preserving scientific fidelity:
Version | Size | PSNR | Ratio |
---|---|---|---|
Raw GeoTIFF | 525 GB | โ | 1 ร |
DynamicEarthNet-video | 8.5 GB | 60.1 dB | 62 ร |
Extra-compressed (optional) | 2.1 GB | 54 dB | 249 ร |
Extensive tests show that semantic change-segmentation scores obtained with U-TAE, U-ConvLSTM and 3D-UNet remain statistically unchanged (ฮ mIoU โค 0.02 pp) when the compressed cubes replace the raw imagery.
The compact video format therefore removes I/O bottlenecks and enables:
- end-to-end training of sequence models directly from disk,
- rapid experimentation on 4-band daily time-series,
- efficient sharing of benchmarks for change detection and forecasting.
๐ฐ๏ธ Sensors
Instrument | Platform | Bands | Native GSD | Role |
---|---|---|---|---|
PlanetFusion | PlanetScope / SkySat fusion | RGB + NIR | 3 m | Daily image sequence |
๐ค Creators
Name | Affiliation |
---|---|
Achraf Toker | Technical University of Munich (TUM) |
Lisa Kondmann | TUM |
Manuel Weber | TUM |
Martin Eisenberger | TUM |
Alfonso Camero | TUM |
Jing Hu | TUM |
Andrรฉ Pregel Hรถderlein | TUM |
รagatay ลenaras | Planet Labs PBC |
Tyler Davis | Planet Labs PBC |
Daniel Cremers | TUM |
Guido Marchisio | Planet Labs PBC |
Xiao Xiang Zhu | German Aerospace Center (DLR) / TUM |
Laura Leal-Taixรฉ | TUM |
๐ Original dataset
Download (TUM Mediatum): https://mediatum.ub.tum.de/1650201
๐ฎ Taco dataset
โก Reproducible Example
import tacoreader
import xarrayvideo as xav
import xarray as xr
import matplotlib.pyplot as plt
# Load tacos
table = tacoreader.load("tacofoundation:dynamicearthnet-video")
# Read a sample row
idx = 0
row = dataset.read(idx)
row_id = dataset.iloc[idx]["tortilla:id"]

๐ฐ๏ธ Sensor Information
Sensors: planet
๐ฏ Task
- Semantic change detection and land-cover mapping on daily 4-band sequences.
- Benchmarks include U-TAE, U-ConvLSTM, 3D-UNet (official splits A/B/C) .
- DynamicEarthNet-video can also serve for next-frame prediction and self-supervised representation learning on high-frequency optical data.
๐ References
Publication 01
- DOI: 10.48550/arXiv.2203.12560
- Summary: Toker et al. introduce DynamicEarthNet, a benchmark of 75 daily 4-band PlanetFusion image cubes (3 m, 2018-2019) with monthly 7-class land-cover masks for semanticโchange segmentation. The paper establishes U-TAE, U-ConvLSTM and 3D-UNet baselines and proposes spatially blocked cross-validation to limit autocorrelation. ([arXiv][1])
- BibTeX Citation
@inproceedings{toker2022dynamicearthnet,
title = {DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation},
author = {Toker, Aykut and Kondmann, Leonie and Weber, Markus and Eisenberger, Marvin and Camero, Alejandro and others},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2022},
doi = {10.48550/arXiv.2203.12560}
}
๐ฌ Discussion
Chat with the maintainers: https://huggingface.co/datasets/tacofoundation/DynamicEarthNet-video/discussions
๐ค Data Providers
Name | Role | URL |
---|---|---|
Planet Labs PBC | Imagery provider | https://www.planet.com |
๐ฅ Curators
Name | Organization | URL |
---|---|---|
Oscar J. Pellicer-Valero | Image Signal Processing (ISP) | Google Scholar |
Cesar Aybar | Image Signal Processing (ISP) | Google Scholar |
Julio Contreras | Image Signal Processing (ISP) | GitHub |
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