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

Fare Garnot, Vivien Sainte; Landrieu, Loic

doi:10.1109/iccv48922.2021.00483

Cited by 263Remote-Sensing Image ClassificationRemote Sensing in AgricultureAdvanced Neural Network Applications

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

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Fare Garnot, Vivien Sainte, Landrieu, Loic·2021·2021 IEEE/CVF International Conference on Computer Vision (ICCV)

DOI 10.1109/iccv48922.2021.00483

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Abstract

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 spatiotemporal 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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SatMAE outperforms UTAE, a SITS state-of-the-art, by 18% on temporal fMoW RGB.
“We also outperform UTAE [48], a SITS state-of-the-art, by 18%.”
From SatMAE: Pre-Training Transformers for Temporal and Multi-Spectral Satellite Imagery (2022) · cites this paper as [48]
For PASTIS, the dataset followed the original split: folds 1-3 for training, 4 for validation, and 5 for testing.
“The dataset followed the original split [37]: folds 1-3 for training, 4 for validation, and 5 for testing.”
From Prithvi-EO-2.0: A Versatile Multitemporal Foundation Model for Earth Observation Applications (2026) · cites this paper as [37]

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Authors: Fare Garnot, V. S., & Landrieu, L.
Affiliations: Univ. Gustave Eiffel, ENSG, IGN,LASTIG,Saint-Mande,France,F-94160
Venue: 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Publisher: IEEE
Published: 2021-10
Pages: 4852-4861
DOI: 10.1109/iccv48922.2021.00483
OpenAlex: W3185151088
Cited by: 242
References: 56
Citations by year: 2026: 49 · 2025: 67 · 2024: 60 · 2023: 43 · 2022: 20 · 2021: 3
FWCI: 54.60
Type: conference paper
Language: en
Open access: green
arxiv.org
Licence: doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
arXiv (Cornell University) (submittedVersion)
HAL (submittedVersion, other-oa)
arXiv (Cornell University)
Topics: Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords: panoptic segmentation, satellite image time series, crop phenology, agricultural parcels, temporal self-attention, spatio-temporal features, semantic segmentation

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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

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