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DynamicEarthNet-video: Daily PlanetFusion Image Cubes Compressed as Videos

Description

๐Ÿ“ฆ 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‰ˆ 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‰ˆ 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 โ‰ค 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.

๐Ÿ›ฐ๏ธ Sensors

Instrument Platform Bands Native GSD Role
PlanetFusion PlanetScope / SkySat fusion RGB + NIR 3 m Daily image sequence

๐Ÿ‘ค 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
ร‡agatay ลženaras 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

๐Ÿ“‚ Original dataset

Download (TUM Mediatum): https://mediatum.ub.tum.de/1650201

๐ŸŒฎ Taco dataset

โšก Reproducible Example

Open In Colab
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"]

๐Ÿ›ฐ๏ธ Sensor Information

Sensors: planet

๐ŸŽฏ 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.

๐Ÿ“š 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 semanticโ€change 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}
}

๐Ÿ’ฌ Discussion

Chat with the maintainers: https://huggingface.co/datasets/tacofoundation/DynamicEarthNet-video/discussions

๐Ÿค Data Providers

Name Role URL
Planet Labs PBC Imagery provider https://www.planet.com

๐Ÿ‘ฅ 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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