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Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks

16 Jul 2021ICCV 2021 10arXiv:2107.07933archive 2025-07-28

Vivien Sainte Fare Garnot, Loic Landrieu

Unprecedented access to multi-temporal satellite imagery has opened new perspectives for a variety of Earth observation tasks. Among them, pixel-precise panoptic segmentation of agricultural parcels has major economic and environmental implications. While researchers have explored this problem for single images, we argue that the complex temporal patterns of crop phenology are better addressed with temporal sequences of images. In this paper, we present the first end-to-end, single-stage method for panoptic segmentation of Satellite Image Time Series (SITS). This module can be combined with our novel image sequence encoding network which relies on temporal self-attention to extract rich and adaptive multi-scale spatio-temporal features. We also introduce PASTIS, the first open-access SITS dataset with panoptic annotations. We demonstrate the superiority of our encoder for semantic segmentation against multiple competing architectures, and set up the first state-of-the-art of panoptic segmentation of SITS. Our implementation and PASTIS are publicly available.

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ConvBlock VSainteuf/utae-paps/src/backbones/utae.py official repository ran fingerprinted MIT (permissive) · 45da329fac60ca4d · report
ConvLayer VSainteuf/utae-paps/src/backbones/utae.py official repository ran fingerprinted MIT (permissive) · e7e11ee365ea4bce · report
DownConvBlock VSainteuf/utae-paps/src/backbones/utae.py official repository ran fingerprinted MIT (permissive) · 4a4104e4258a152b · report
LTAE2d VSainteuf/utae-paps/src/backbones/utae.py official repository ran MIT (permissive) · 0c52a14cf35ab27e · report
MultiHeadAttention VSainteuf/utae-paps/src/backbones/utae.py official repository ran MIT (permissive) · 05217b7e7b5d3120 · report
PositionalEncoder VSainteuf/utae-paps/src/backbones/utae.py official repository ran fingerprinted MIT (permissive) · de6db552db8d11c4 · report
ScaledDotProductAttention VSainteuf/utae-paps/src/backbones/utae.py official repository ran MIT (permissive) · 2b213e91591f2da8 · report
Temporal_Aggregator VSainteuf/utae-paps/src/backbones/utae.py official repository ran fingerprinted MIT (permissive) · 95b9e0afd8ec4312 · report
TemporallySharedBlock VSainteuf/utae-paps/src/backbones/utae.py official repository ran MIT (permissive) · f56238c8ef11a6d5 · report
UpConvBlock VSainteuf/utae-paps/src/backbones/utae.py official repository ran MIT (permissive) · a64d9cde74098274 · report
UTAE VSainteuf/utae-paps/src/backbones/utae.py official repository unverified MIT (permissive) · 8abb5042ed8b7f79 · report

Tasks

Cloud RemovalEarth ObservationFlood extent forecastingPanoptic SegmentationSegmentationSemantic SegmentationTemporal SequencesTime SeriesTime Series Analysis

Datasets

Introduced by this paper, per the archive.

PASTIS

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Cloud Removal SEN12MS-CR-TS U-TAE PSNR 27.05 #4 of 7 Archive leaderboard report
Cloud Removal SEN12MS-CR-TS U-TAE RMSE 0.051 #4 of 7 Archive leaderboard report
Cloud Removal SEN12MS-CR-TS U-TAE SAM 11.649 #4 of 7 Archive leaderboard report
Cloud Removal SEN12MS-CR-TS U-TAE SSIM 0.849 #4 of 7 Archive leaderboard report
Flood extent forecasting Global Flood forecasting U-TAE F1 score 0.77 #1 of 5 Archive leaderboard report
Panoptic Segmentation PASTIS U-TAE + PaPs PQ 40.4 #3 of 3 Archive leaderboard report
Panoptic Segmentation PASTIS U-TAE + PaPs RQ 49.2 #3 of 3 Archive leaderboard report
Panoptic Segmentation PASTIS U-TAE + PaPs SQ 81.3 #3 of 3 Archive leaderboard report
Semantic Segmentation PASTIS U-TAE Mean IoU (test) 63.1 #3 of 3 Archive leaderboard report
Semantic Segmentation PASTIS U-TAE Number of Params 1.1M #3 of 3 Archive leaderboard report
Semantic Segmentation PASTIS U-TAE Overall Accuracy 83.2 #3 of 3 Archive leaderboard report

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