Pith. sign in

REVIEW 3 major objections 5 minor 44 references

Video Compression for Spatiotemporal Earth System Data

T0 review · 3 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper claims standard video codecs compress Earth system data up to 250x without degrading downstream machine learning performance.

desk verdict Practical library plus solid rate-distortion measurements, but the 'completely interchangeable' claim is overreaching and the DeepExtremeCubes downstream test is confounded by preprocessing changes. read the letter →

arxiv 2506.19656 v1 pith:2OJT5MH5 submitted 2025-06-24 cs.CV cs.DLeess.IVphysics.geo-ph

Oscar J. Pellicer-Valero , Cesar Aybar , Gustau Camps Valls This is my paper · ORCID
classification cs.CVcs.DLeess.IVphysics.geo-ph
keywords video-compressionEarth-system-dataxarrayvideoH.265multispectral-imageryERA5lossy-compressiondownstream-tasks
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that the spatial, temporal, and spectral redundancies of large Earth system datasets are close enough to ordinary video that standard, off-the-shelf video codecs can compress them dramatically without compromising scientific use. It presents xarrayvideo, a library that writes xarray data cubes as standard video files (H.265 or VP9 through ffmpeg) and reads them back, reporting compression ratios up to 250x and PSNRs between roughly 40 and 66 dB on four real datasets. The load-bearing conclusion is interchangeability: a dataset compressed lossily at sufficiently high quality should behave identically to the original in any downstream task. As evidence, pretrained models for landcover segmentation and next-step reflectance forecasting score essentially identically on original and compressed data, and two compressed datasets (DeepExtremeCubes at 270 GB from 2.3 TB, DynamicEarthNet at 8.5 GB from 525 GB) are publicly released. If this holds, terabyte-scale Earth observation becomes distributable and trainable at a fraction of current storage and bandwidth costs.

What carries the argument

The machinery is the xarray2video mapping: a spatiotemporal DataArray with axes (bands, time, y, x) is reshaped so that frames are (time, y, x) slices; bands are packed three per video (an extra band is triplicated to fill the third channel, or PCA reduces the band count); each video is encoded by ffmpeg with libx265 or VP9 at 12-bit depth, with JPEG2000 via GDAL as the image-codec baseline; lossless auxiliary masks use ffv1; and all non-video metadata goes into a small NetCDF file (x.nc). The same mapping rules choose codec, bit depth, quality preset, and whether PCA is applied. This turns decades of video-codec engineering for motion compensation, transform coding, and rate control into a generic compressor for Earth system data, and it is what allows compressing clouds, misregistration, missing data, and smooth reanalysis fields with the same tool.

What would settle it

Run a third downstream model, such as a regression of a biophysical variable or a rare-event detection model, on the released compressed DynamicEarthNet and DeepExtremeCubes versions and on the original data, and check whether performance differs beyond noise.

Watch

Extended reading notes

Core claim

On its own terms, the central claim is that the time axis of Earth system data carries exactly the kind of redundancy that video codecs are built to remove, so a standard encoder can serve as a general, off-the-shelf compressor for multispectral spatiotemporal cubes. xarrayvideo encodes the (time, y, x) array as a video, packs bands into three-channel videos, and reports PSNRs of 55.86, 40.60, 46.58, and 43.23 dB at 0.1 bpppb and 65.91, 54.28, 62.90, and 55.04 dB at 1 bpppb on DynamicEarthNet, DeepExtremeCubes, ERA5, and SimpleS2. It then supplies the key evidence for interchangeability: pretrained models for landcover segmentation and next-step reflectance prediction, applied without modification, score essentially identically on the original and compressed data at high quality (60 dB for DynamicEarthNet, 56 dB for DeepExtremeCubes), and two such compressed datasets are publicly released at 270 GB and 8.5 GB. The paper states the strong version of the conclusion: no performance loss is observed when compressed versions are used in the respective deep learning downstream tasks.

Load-bearing premise

The argument collapses if two pretrained models are not enough to certify that codec artifacts are inert for all downstream Earth system applications, since the paper never identifies the quality threshold at which losses begin.

Editorial extensions

If this is right

  • DynamicEarthNet can be distributed at 62x smaller size (8.5 GB at 60.15 dB) and DeepExtremeCubes at 8.5x smaller (270 GB at 56.65 dB) while reproducing the original datasets' scores on the tested segmentation and reflectance-forecasting models.
  • At more aggressive settings the same pipeline still yields usable data: a 2.1 GB DynamicEarthNet at 54 dB (about 249x smaller) matches the original metrics, while DeepExtremeCubes at 47 dB and 85 GB costs only a small drop in test R2 from 0.906 to 0.899.
  • Because xarrayvideo builds on ffmpeg and standard codecs, any xarray-shaped dataset can be compressed without neural-model training, specialized hardware, or per-dataset tuning.
  • Lossless layers such as cloud masks, scene classification, and validity masks compress about 34x with the ffv1 codec, so mixed lossy/lossless packaging is practical.
  • Codec choice is data-dependent: x265 and VP9 beat JPEG2000 for natural-looking Earth observation scenes at low bitrates, while JPEG2000 wins on smooth reanalysis fields at high bitrates.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the interchangeability claim is accepted, benchmark and foundation-model corpora could ship one high-quality lossy master plus checksums, with the originals archived, cutting distribution cost by one to two orders of magnitude; this extension is not developed in the paper.
  • The paper's two downstream tests leave open whether codec artifacts such as blocking, ringing, temporal flicker, and chroma subsampling are inert for other tasks like rare-event detection, biophysical parameter retrieval, or change tracking; testing the released cubes on a broader task battery would settle that.
  • Because the released DeepExtremeCubes version also drops band B07, forward-fills missing values, and adds an invalid mask, part of the reported performance equivalence may come from those preprocessing changes rather than from the codec itself; an ablation on a preprocessed-but-lossless baseline would separate the two effects.
  • The finding that PCA before compression was neutral or detrimental suggests that the simple route of splitting bands into three-channel videos is the more robust design choice for more than three bands, at least with the tested x265 settings.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper introduces xarrayvideo, an open-source Python library that compresses multichannel spatiotemporal Earth system datasets by encoding them as standard videos via ffmpeg. The method maps xarray variables to video files, with options for codec (x265, VP9, FFV1, JPEG2000), bit depth, PCA preprocessing, and quality presets. The authors benchmark the library on four datasets: DynamicEarthNet (Planet imagery), DeepExtremeCubes (Sentinel-2), ERA5 (reanalysis), and SimpleS2, reporting rate-distortion curves in terms of bits per pixel per band (bpppb), PSNR, SSIM, and spectral angle. They also redistribute compressed versions of DynamicEarthNet (525 GB to 8.5 GB) and DeepExtremeCubes (2.3 TB to 270 GB) through HuggingFace in the TACO format. Downstream evaluations apply pretrained models from the original dataset publications without modification: landcover semantic segmentation for DynamicEarthNet and next-step reflectance prediction for DeepExtremeCubes. The paper reports no performance loss for these tasks at the high-quality compression levels and argues that lossy compressed data at sufficiently high quality is completely interchangeable with original data for any downstream application.

Significance. If the central claims hold, the contribution is practically significant for the Earth observation and machine learning communities. xarrayvideo provides a simple, standardized, and accessible way to compress large spatiotemporal datasets using mature video codecs, with fully disclosed encoder configurations (Table A.1) and reversible mapping to xarray structure. The released compressed datasets are substantial real artifacts: DynamicEarthNet at 62x compression and 60.15 dB PSNR, and DeepExtremeCubes at 8.5x compression and about 56 dB PSNR. The downstream tests use pretrained models from prior publications without modification, which is an appropriate external benchmark. The paper is also transparent about limitations (full-cube memory loading, decompression overhead) and about the specific codec configurations used. However, the strength of the 'no performance loss' and 'completely interchangeable' claims is not yet matched by the evidence, which is based on only two downstream tasks and, for DeepExtremeCubes, on a comparison that is confounded by simultaneous preprocessing changes.

major comments (3)
  1. [Section III-B, Table IV] The DeepExtremeCubes downstream comparison is confounded. The 'Video 56dB' version differs from the original in at least three ways simultaneously: lossy x265 compression, omission of band B07, and a different missing-data policy (forward-filling plus an added invalid mask), as acknowledged in Section III-B. The near-identical test metrics (R2 0.905 vs 0.906, L1 0.037 vs 0.036) therefore cannot be attributed to the video codec alone. Since this experiment is the only evidence for the abstract's claim that 'No performance loss is observed' for DeepExtremeCubes and for the advertised redistribution 'without compromising quality,' the authors should provide an ablation that isolates the codec effect, for example by compressing the full 7-band data with unchanged missing-data handling, or by comparing codec-only versus codec-plus-preprocessing variants.
  2. [Section I and Section IV] The claim that a dataset 'compressed lossily at sufficiently high quality is completely interchangeable with the original data for any downstream application' is substantially overgeneralized relative to the evidence. Only two downstream tasks are tested, one of which is confounded as noted above, and the study does not characterize the quality threshold below which performance degrades. Table IV itself shows a measurable drop at 47 dB PSNR (test R2 from 0.906 to 0.899, L1 from 0.036 to 0.040), so the 'sufficiently high quality' regime is not pinned down. The authors should soften the interchangeability claim to the tested tasks and quality levels, or add a rate-performance study that maps downstream accuracy as a function of compression level.
  3. [Section II-C and Section IV] The PSNR definition used here is non-standard and complicates comparisons. PSNR is computed with respect to the maximum intensity present in the original image sequence for each channel, rather than a fixed bit-depth maximum. This can inflate headline numbers relative to conventional PSNR and makes the comparisons with Das et al. and Cra5 in the Discussion questionable, since those results are based on different error metrics and PSNR conventions. The authors should report a standard fixed-range PSNR as well, or at least provide a conversion, so that the reported 55-65 dB figures can be interpreted by readers outside the paper.
minor comments (5)
  1. [Table IV] Row 'Original (3,2 Tb)' appears to be a typo; the text and elsewhere state DeepExtremeCubes is 2.3 TB, so '2,3 Tb' is likely intended.
  2. [Section III-A] The word 'respectivley' should be 'respectively' in the sentence on median compression and decompression times.
  3. [Listing 1] The comment '(# input tyxc coords)' contains a typo; the coordinate tuple is ('time', 'y', 'x'), so 'tyx' would be the correct abbreviation.
  4. [Section III-B] The phrase 'channel B07 ... is not included from the original dataset' could be rephrased as 'channel B07 is omitted from the compressed version of the original dataset' for clarity.
  5. [Section I] The paper uses both 'Principal Components (PC) Analysis' and 'PCA transformation'; please standardize the terminology (e.g., PCA).

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: compression quality and downstream equivalence are measured against external artifacts; no fitted parameter is renamed as a prediction.

full rationale

The paper's derivation chain is a direct engineering evaluation: data are encoded and decoded with standard codecs and compared to the originals via PSNR/SSIM/SA (Tables I-VI), and the downstream claims are tested by feeding original and compressed inputs to previously released pretrained models ([18] for DynamicEarthNet, [42] for DeepExtremeCubes) without retraining or modification (Tables II, IV). There is no fitted parameter in this paper that is later relabeled as a prediction; the quality-rate curves are measured, not derived. The self-citations ([1], [42], [31]-[34]) are not load-bearing in a circular way: [42] supplies a runnable model artifact rather than an unverified theorem, and the equivalence conclusion is reported as measured numbers, not as a consequence of the citation. The manuscript's own Section III-B note discloses that the released DeepExtremeCubes version also drops band B07, forward-fills missing values, and adds an invalid mask; this makes the original-vs-compressed comparison confounded for that dataset, but it is an experimental design issue rather than a reduction by construction, because the transformed inputs and the measured R2/L1 scores are explicit and not assumed by the codec method. The 'completely interchangeable' phrasing in the Introduction is an overgeneralization whose 'sufficiently high quality' threshold is unspecified, but it is a rhetorical claim, not a derivation: the paper does not define interchangeability in terms of the compression parameters. No circular step can be exhibited by equations or by construction, so the appropriate finding is no significant circularity.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The paper is an empirical benchmark, so the ledger records disclosed experimental operating points rather than hidden fitted parameters. The rate-distortion points, and therefore the headline PSNR and bpppb numbers, depend on hand-chosen x265 and VP9 quality presets, output bit depths, and PCA retention choices, all listed in Table A.1 and Section III. The scientific claims additionally assume that per-channel-normalized PSNR/SSIM/SA are meaningful fidelity measures for EO data, that two pretrained models are adequate downstream probes, that band duplication for codec channel filling is neutral to the bpppb metric, and that the NetCDF side file is lossless. No invented entities are introduced.

free parameters (4)
  • x265 quality preset CRF values = 51, 1, 7, 16, 27 (Best to Very low); qpmin=0:qpmax=0.01 for Very high
    Hand-chosen rate-distortion operating points defining each reported (bpppb, PSNR) pair; disclosed in Table A.1.
  • VP9 quality preset CRF values = 0, 5, 12, 20, 30 (Best to Very low)
    Same role as the x265 presets; disclosed in Table A.1.
  • Output bit depth = 8, 10, 12 bits (video); 16 bits (JPEG2000)
    Hand-chosen per experiment; the conclusion that 12 bits beats 10 and 8 bits depends on this choice (Section III-A).
  • PCA component retention (SimpleS2) = 9 or 10 of 10 PCs
    Hand-chosen ablation settings for the PCA experiments (Section III-C).
assumptions (4)
  • domain assumption Standard video codecs (x265, VP9) have rate-distortion behavior on multispectral EO data that is meaningfully captured by PSNR, SSIM, and spectral angle computed with per-channel dynamic range normalization.
    Underlies all fidelity numbers in Tables I-VI and Figures 2-5; Section II.C.
  • domain assumption The pretrained models from references [18] and [42] are adequate probes for downstream equivalence of compressed Earth observation data.
    The 'no performance loss' claim rests on these two models and tasks; Section III-A-B, Tables II and IV.
  • domain assumption Duplicating a band three times to fill a three-channel codec container does not bias the bits-per-pixel-per-band comparisons.
    DynamicEarthNet NIR is encoded as NIR, NIR, NIR in a second video (Section II-A); the metric treats band counts uniformly.
  • domain assumption Non-video variables and metadata stored in the accompanying compressed NetCDF file are preserved losslessly by the pipeline.
    Lossless side of the format is asserted in Section II-A but not stress-tested in the benchmarks.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Video Compression for Spatiotemporal Earth System Data." pith.science (2026). https://pith.science/paper/2OJT5MH5

@misc{pith2026250619656,
  author       = {Pith},
  title        = {Pith review of: Video Compression for Spatiotemporal Earth System Data},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2OJT5MH5}},
  note         = {Machine review of arXiv:2506.19656}
}
read the original abstract

Large-scale Earth system datasets, from high-resolution remote sensing imagery to spatiotemporal climate model outputs, exhibit characteristics analogous to those of standard videos. Their inherent spatial, temporal, and spectral redundancies can thus be readily exploited by established video compression techniques. Here, we present xarrayvideo, a Python library for compressing multichannel spatiotemporal datasets by encoding them as videos. Our approach achieves compression ratios of up to 250x while maintaining high fidelity by leveraging standard, well-optimized video codecs through ffmpeg. We demonstrate the library's effectiveness on four real-world multichannel spatiotemporal datasets: DynamicEarthNet (very high resolution Planet images), DeepExtremeCubes (high resolution Sentinel-2 images), ERA5 (weather reanalysis data), and the SimpleS2 dataset (high resolution multichannel Sentinel-2 images), achieving Peak Signal-to-Noise Ratios (PSNRs) of 55.86, 40.60, 46.58, and 43.23 dB at 0.1 bits per pixel per band (bpppb) and 65.91, 54.28, 62.90, and 55.04 dB at 1 bpppb. We are redistributing two of these datasets, DeepExtremeCubes (2.3 Tb) and DynamicEarthNet (525 Gb), in the machine-learning-ready and cloud-ready TACO format through HuggingFace at significantly reduced sizes (270 Gb and 8.5 Gb, respectively) without compromising quality (PSNR 55.77-56.65 and 60.15). No performance loss is observed when the compressed versions of these datasets are used in their respective deep learning-based downstream tasks (next step reflectance prediction and landcover segmentation). In conclusion, xarrayvideo presents an efficient solution for handling the rapidly growing size of Earth observation datasets, making advanced compression techniques accessible and practical to the Earth science community. The library is available for use at https://github.com/IPL-UV/xarrayvideo

Figures

Figures reproduced from arXiv: 2506.19656 by the authors.

Figure 1
Figure 1. The xarrayvideo compression pipeline: Diverse Earth system datasets (DynamicEarthNet, DeepExtremeCubes, SimpleS2, and ERA5) are processed by xarrayvideo, leveraging standard video codecs, to produce significantly smaller compressed versions while maintaining high fidelity (PSNR). The compressed DynamicEarthNet and DeepExtremeCubes datasets are available on HuggingFace in the FAIR-compliant TACO format [1] at a 62× a… view at source ↗
Figure 2
Figure 2. Top: Sample image from the DynamicEarthNet dataset showing RGB bands (original is a 16-bit integer in range 0-65535) after scaling by a factor [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Top: Sample image from the DeepExtremeCubes dataset showing RGB bands after scaling by a factor of 4 and clipping between 0 and 1; from left [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Top: Sample image from the SimpleS2 dataset showing RGB bands (original is a 16-bit integer in range 0-65535) after scaling by a factor of 1/2000 [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Top: Sample image from the ERA5 dataset showing the first three pressure levels (as RGB image channels) of the relative humidity after scaling [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]

Review history (2 revisions) →

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

44 extracted references · 34 canonical work pages

  1. [1]

    Taco: Transparent access to cloud-optimized spatio-temporal datasets,

    C. Aybar, L. G ´omez-Chova, J. Contreras, O. Pellicer, C. Ma, G. Camps- Valls, D. Montero, M. D. Mahecha, M. Sudmanns, and D. Tiede, “Taco: Transparent access to cloud-optimized spatio-temporal datasets,” in Proceedings of the Living Planet Symposium 2025 . Vienna, Austria: European Space Agency (ESA), 2025, presented at the Living Planet Symposium 2025

    work page 2025

  2. [2]

    (2023) Ucs satellite database

    Union of Concerned Scientists. (2023) Ucs satellite database. Union of Concerned Scientists. Updated May 1, 2023. [Online]. Available: https://www.ucsusa.org/resources/satellite-database

    work page 2023

  3. [3]

    Deep learning and process understanding for data-driven earth system science,

    M. Reichstein, G. Camps-Valls, B. Stevens, M. Jung, J. Denzler, N. Carvalhais, and F. Prabhat, “Deep learning and process understanding for data-driven earth system science,” Nature, vol. 566, no. 7743, pp. 195–204, 2019

    work page 2019

  4. [4]

    P ¨ortner, D

    H.-O. P ¨ortner, D. Roberts, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr´ıa, M. Craig, S. Langsdorf, S. L ¨oschke, V . M¨oller, A. Okem, and B. Rama, Eds., Climate Change 2022: Impacts, Adaptation and Vulnerability. Cambridge, UK and New York, NY , USA: Cambridge University Press, 2022, contribution of Working Group II to the Sixth Assessment Repor...

    work page 2022

  5. [5]

    Multimodal ground-based remote sensing cloud classification via learning hetero- geneous deep features,

    S. Liu, L. Duan, Z. Zhang, X. Cao, and T. S. Durrani, “Multimodal ground-based remote sensing cloud classification via learning hetero- geneous deep features,” IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 11, pp. 7790–7800, 2020

    work page 2020

  6. [6]

    The role of satellite remote sensing in climate change studies,

    J. Yang, P. Gong, R. Fu, M. Zhang, J. Chen, S. Liang, B. Xu, J. Shi, and R. Dickinson, “The role of satellite remote sensing in climate change studies,” Nature climate change , vol. 3, no. 10, pp. 875–883, 2013

    work page 2013

  7. [7]

    Remote sensing of coastlines: detection, extraction and monitoring,

    R. Gens, “Remote sensing of coastlines: detection, extraction and monitoring,” International Journal of Remote Sensing , vol. 31, no. 7, pp. 1819–1836, 2010

    work page 2010

  8. [8]

    Neural networks and support vector machine algorithms for automatic cloud classification of whole-sky ground-based images,

    A. Taravat, F. Del Frate, C. Cornaro, and S. Vergari, “Neural networks and support vector machine algorithms for automatic cloud classification of whole-sky ground-based images,” IEEE Geoscience and Remote Sensing Letters , vol. 12, no. 3, pp. 666–670, March 2015

    work page 2015

Show all 44 references
  1. [9]

    The value of near real-time earth observa- tions for improved flood disaster response,

    P. C. Oddo and J. D. Bolten, “The value of near real-time earth observa- tions for improved flood disaster response,” Frontiers in Environmental Science, vol. 7, p. 127, 2019

    2019

  2. [10]

    A comparison of linear regression, regularization, and machine learning algorithms to develop europe-wide spatial models of fine particles and nitrogen dioxide,

    J. Chen, K. de Hoogh, J. Gulliver, B. Hoffmann, O. Hertel, M. Ketzel, M. Bauwelinck, A. Van Donkelaar, U. A. Hvidtfeldt, K. Katsouyanni et al. , “A comparison of linear regression, regularization, and machine learning algorithms to develop europe-wide spatial models of fine pa...

    2019

  3. [11]

    Camps-Valls, D

    G. Camps-Valls, D. Tuia, L. G ´omez-Chova, S. Jim ´enez, and J. Malo, Remote Sensing Image Processing , 1st ed., ser. Synthesis Lectures on Image, Video, and Multimedia Processing. Springer Cham, 2022. [Online]. Available: https://doi.org/10.1007/978-3-031-02247-0

    2022 doi

  4. [12]

    Camps-Valls, D

    G. Camps-Valls, D. Tuia, X. X. Zhu, and M. Reichstein, Deep learning for the Earth Sciences: A comprehensive approach to remote sensing, climate science and geosciences . John Wiley & Sons, 2021. 11 TABLE VI COMPARISON OF DIFFERENT COMPRESSION CONFIGURATIONS FOR THE ERA5 DATAS...

    2021

  5. [13]

    Deep learning and earth observation to support the sustainable development goals: Current approaches, open challenges, and future opportunities,

    C. Persello, J. D. Wegner, R. H ¨ansch, D. Tuia, P. Ghamisi, M. Koeva, and G. Camps-Valls, “Deep learning and earth observation to support the sustainable development goals: Current approaches, open challenges, and future opportunities,” IEEE Geoscience and Remote Sensing Mag-...

    2022

  6. [14]

    On the opportunities and risks of foundation models,

    R. Bommasani, D. A. Hudson, E. Adeli, R. Altman, S. Arora, S. von Arx, M. S. Bernstein, J. Bohg, A. Bosselut, E. Brunskill, E. Brynjolfsson, and others., “On the opportunities and risks of foundation models,” arXiv, 8 2021. [Online]. Available: https://arxiv.org/abs/2108.07258v3

    2021 arXiv

  7. [15]

    reben: Refined bigearthnet dataset for remote sensing image analysis,

    K. N. Clasen, L. Hackel, T. Burgert, G. Sumbul, B. Demir, and V . Markl, “reben: Refined bigearthnet dataset for remote sensing image analysis,” arXiv preprint arXiv:2407.03653 , 2024

    2024 arXiv

  8. [16]

    Ssl4eo-s12: A large-scale multimodal, multitemporal dataset for self-supervised learning in earth observation [software and data sets],

    Y . Wang, N. A. A. Braham, Z. Xiong, C. Liu, C. M. Albrecht, and X. X. Zhu, “Ssl4eo-s12: A large-scale multimodal, multitemporal dataset for self-supervised learning in earth observation [software and data sets],” IEEE Geoscience and Remote Sensing Magazine , vol. 11, no. 3, p...

    2023

  9. [17]

    Major tom: Expandable datasets for earth observation,

    A. Francis and M. Czerkawski, “Major tom: Expandable datasets for earth observation,” arXiv preprint arXiv:2402.12095 , 2024

    2024 arXiv

  10. [18]

    Dynamicearthnet: Daily multi-spectral satellite dataset for semantic change segmentation,

    A. Toker, L. Kondmann, M. Weber, M. Eisenberger, A. Camero, J. Hu, A. P. Hoderlein, C ¸ . S ¸enaras, T. Davis, D. Cremers et al. , “Dynamicearthnet: Daily multi-spectral satellite dataset for semantic change segmentation,” in Proceedings of the IEEE/CVF Conference on Computer ...

    2022

  11. [19]

    Earthnet2021: A large-scale dataset and challenge for earth surface forecasting as a guided video prediction task

    C. Requena-Mesa, V . Benson, M. Reichstein, J. Runge, and J. Denzler, “Earthnet2021: A large-scale dataset and challenge for earth surface forecasting as a guided video prediction task.” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 202...

    2021

  12. [20]

    Deep- extremecubes: Integrating earth system spatio-temporal data for impact assessment of climate extremes,

    C. Ji, T. Fincke, V . Benson, G. Camps-Valls, M.-A. Fernandez-Torres, F. Gans, G. Kraemer, F. Martinuzzi, D. Montero, K. Mora et al., “Deep- extremecubes: Integrating earth system spatio-temporal data for impact assessment of climate extremes,” arXiv preprint arXiv:2406.18179 , 2024

    2024 arXiv

  13. [21]

    The era5 global reanalysis,

    H. Hersbach, B. Bell, P. Berrisford, S. Hirahara, A. Hor ´anyi, J. Mu ˜noz- Sabater, J. Nicolas, C. Peubey, R. Radu, D. Schepers et al. , “The era5 global reanalysis,” Quarterly Journal of the Royal Meteorological Society, vol. 146, no. 730, pp. 1999–2049, 2020

    1999

  14. [22]

    Netcdf: an interface for scientific data access,

    R. Rew and G. Davis, “Netcdf: an interface for scientific data access,” IEEE computer graphics and applications , vol. 10, no. 4, pp. 76–82, 1990

    1990

  15. [23]

    Zarr: A cloud-optimized storage for interactive access of large arrays,

    J. Moore and S. Kunis, “Zarr: A cloud-optimized storage for interactive access of large arrays,” in Proceedings of the Conference on Research Data Infrastructure, vol. 1, 2023

    2023

  16. [24]

    Jpeg xl next-generation image compression architecture and coding tools,

    J. Alakuijala, R. Van Asseldonk, S. Boukortt, M. Bruse, I.-M. Coms ,a, M. Firsching, T. Fischbacher, E. Kliuchnikov, S. Gomez, R. Obryket al., “Jpeg xl next-generation image compression architecture and coding tools,” in Applications of digital image processing XLII , vol. 111...

    2019

  17. [25]

    Vari- ational image compression with a scale hyperprior,

    J. Ball ´e, D. Minnen, S. Singh, S. J. Hwang, and N. Johnston, “Vari- ational image compression with a scale hyperprior,” arXiv preprint arXiv:1802.01436, 2018

    2018 arXiv

  18. [26]

    Neural discrete representation learning,

    A. Van Den Oord, O. Vinyals et al. , “Neural discrete representation learning,” Advances in neural information processing systems , vol. 30, 2017

    2017

  19. [27]

    Fixed-quality compression of remote sensing images with neural net- works,

    S. M. i Verd ´u, M. ChabertMember, T. Oberlin, and J. Serra-Sagrist `a, “Fixed-quality compression of remote sensing images with neural net- works,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2024

    2024

  20. [28]

    Cra5: Extreme com- pression of era5 for portable global climate and weather research via an efficient variational transformer,

    T. Han, Z. Chen, S. Guo, W. Xu, and L. Bai, “Cra5: Extreme com- pression of era5 for portable global climate and weather research via an efficient variational transformer,” arXiv preprint arXiv:2405.03376 , 2024

    2024 arXiv

  21. [29]

    Remote sensing image compression based on high-frequency and low-frequency components,

    S. Xiang and Q. Liang, “Remote sensing image compression based on high-frequency and low-frequency components,” IEEE Transactions on Geoscience and Remote Sensing , 2024

    2024

  22. [30]

    Hyperspectral image, video compression using sparse tucker tensor decomposition,

    S. Das, “Hyperspectral image, video compression using sparse tucker tensor decomposition,” IET Image Processing , vol. 15, no. 4, pp. 964– 973, 2021

    2021

  23. [31]

    Improved statistically based retrievals via spatial- spectral data compression for iasi data,

    J. Garcia-Sobrino, V . Laparra, J. Serra-Sagrist `a, X. Calbet, and G. Camps-Valls, “Improved statistically based retrievals via spatial- spectral data compression for iasi data,” IEEE Transactions on Geo- science and Remote Sensing , vol. 57, no. 8, pp. 5651–5668, 2019

    2019

  24. [32]

    Statistical Atmospheric Parameter Retrieval Largely Benefits from Spatial-Spectral Image Compression,

    J. Garc ´ıa Sobrino, Serra-Sagrista, V . Laparra, X. Calbet, and G. Camps- Valls, “Statistical Atmospheric Parameter Retrieval Largely Benefits from Spatial-Spectral Image Compression,” IEEE Transactions on Geo- science and Remote Sensing , vol. 55, no. 4, pp. 2213–2224, 2017

    2017

  25. [33]

    On the impact of lossy compression on hyperspectral image classification and unmixing,

    F. Garc ´ıa-V´ılchez, J. Mu ˜noz Mar´ı, M. Zortea, I. Blanes, V . Gonz ´alez- Ruiz, G. Camps-Valls, A. Plaza, and J. Serra-Sagrist`a, “On the impact of lossy compression on hyperspectral image classification and unmixing,” IEEE Geoscience and Remote Sensing Letters , vol. 8, n...

    2011

  26. [34]

    On the impact of lossy compression on hyperspectral image classification and unmixing,

    F. Garc ´ıa-V´ılchez, J. Mu ˜noz-Mar´ı, M. Zortea, I. Blanes, V . Gonz ´alez- Ruiz, G. Camps-Valls, A. Plaza, and J. Serra-Sagrist`a, “On the impact of lossy compression on hyperspectral image classification and unmixing,” IEEE Geoscience and remote sensing letters , vol. 8, n...

    2010

  27. [35]

    Performance evaluation of the h. 264/avc video coding standard for lossy hyperspectral image compression,

    L. Santos, S. Lopez, G. M. Callico, J. F. Lopez, and R. Sarmiento, “Performance evaluation of the h. 264/avc video coding standard for lossy hyperspectral image compression,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 5, no. 2, pp. ...

    2011

  28. [36]

    Weather- bench 2: A benchmark for the next generation of data-driven global weather models,

    S. Rasp, S. Hoyer, A. Merose, I. Langmore, P. Battaglia, T. Russell, A. Sanchez-Gonzalez, V . Yang, R. Carver, S. Agrawal et al. , “Weather- bench 2: A benchmark for the next generation of data-driven global weather models,” Journal of Advances in Modeling Earth Systems , vol....

    2024

  29. [37]

    J ¨ahne, Digital image processing

    B. J ¨ahne, Digital image processing . Springer Science & Business Media, 2005

    2005

  30. [38]

    Image quality assessment: from error visibility to structural similarity,

    Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE transactions on image processing , vol. 13, no. 4, pp. 600–612, 2004

    2004

  31. [39]

    The spectral image processing system (sips)—interactive visualization and analysis of imaging spectrometer data,

    F. A. Kruse, A. B. Lefkoff, J. W. Boardman, K. B. Heidebrecht, A. T. Shapiro, P. J. Barloon, and A. F. H. Goetz, “The spectral image processing system (sips)—interactive visualization and analysis of imaging spectrometer data,” in Summaries of the fourth annual JPL 12 airborne...

    1993

  32. [40]

    Locally-weighted-regression scatter-plot smoothing (lowess): a graphical exploratory data analysis technique

    G. W. Moran, “Locally-weighted-regression scatter-plot smoothing (lowess): a graphical exploratory data analysis technique.” Ph.D. dis- sertation, Monterey, California. Naval Postgraduate School, 1984

    1984

  33. [41]

    Dynamicearthnet: Daily multi-spectral satellite dataset for semantic change segmentation - validation and test label,

    A. Toker, L. Kondmann, M. Weber, M. Eisenberger, A. Camero, J. Hu, A. Pregel Hoderlein, C. Senaras, T. Davis, D. Cremers, G. Marchisio, X. X. Zhu, and L. Leal-Taix ´e, “Dynamicearthnet: Daily multi-spectral satellite dataset for semantic change segmentation - validation and te...

    2024 doi

  34. [42]

    Explainable earth surface forecasting under extreme events,

    O. J. Pellicer-Valero, M.- ´A. Fern´andez-Torres, C. Ji, M. D. Mahecha, and G. Camps-Valls, “Explainable earth surface forecasting under extreme events,” arXiv preprint arXiv:2410.01770 , 2024

    2024 arXiv

  35. [43]

    Assessment of klt and bit-allocation strategies in the application of jpeg 2000 to the battlescale forecast meteorological data,

    O. Kosheleva, A. Aguirre, S. D. Cabrera, and E. Vidal, “Assessment of klt and bit-allocation strategies in the application of jpeg 2000 to the battlescale forecast meteorological data,” in IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceeding...

    2000

  36. [44]

    Hyperspectral video restoration using optical flow and sparse coding,

    A. Mian and R. Hartley, “Hyperspectral video restoration using optical flow and sparse coding,” Optics express , vol. 20, no. 10, pp. 10 658– 10 673, 2012. 13 APPENDIX A SUPPLEMENTARY MATERIAL This Appendix contains Table A.1 with the configurations for the quality presets use...

    2012

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.