Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series
Open accessAbstract
In this paper, a new self-supervised strategy for learning meaningful representations of complex optical Satellite Image Time Series (SITS) is presented. The methodology proposed, named U-BARN, a Unet-BERT spAtio-temporal Representation eNcoder, exploits irregularly sampled SITS. The designed architecture allows learning rich and discriminative features from unlabeled data, enhancing the synergy between the spatio-spectral and the temporal dimensions. To train on unlabeled data, a time series reconstruction pretext task inspired by the BERT strategy but adapted to SITS is proposed. A Sentinel-2 large-scale unlabeled data-set is used to pre-train U-BARN. During the pre-training, U-BARN processes annual time series composed of a maximum of 100 dates. To demonstrate its feature learning capability, representations of SITS encoded by U-BARN are then fed into a shallow classifier to generate semantic segmentation maps. Experimental results are conducted on a labeled crop data-set (PASTIS) as well as a dense land cover data-set (MultiSenGE). Two ways of exploiting U-BARN pre-training are considered: either U-BARN weights are frozen or fine-tuned. The obtained results demonstrate that representations of SITS given by the frozen U-BARN are more efficient for land cover and crop classification than those of a supervised-trained linear layer. Then, we observe that fine-tuning boosts U-BARN performances on MultiSenGE dataset. Additionally, we observe on PASTIS, in scenarios with scarce…
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Most geospatial foundation models do not account for the multi-temporal nature of Earth observation data.
“although EO data is inherently multi-temporal, most GFMs do not account for this characteristic.”
From Prithvi-EO-2.0: A Versatile Multitemporal Foundation Model for Earth Observation Applications (2026) · cites this paper as [5]
Those geospatial foundation models that do account for multi-temporal data either process only point data or focus on long time series restricted to small patches or individual pixels.
“Those that do either process only point data or focus on long time series restricted to small patches or individual pixels [4], [5].”
From Prithvi-EO-2.0: A Versatile Multitemporal Foundation Model for Earth Observation Applications (2026) · cites this paper as [5]
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- Authors
- Dumeur, I., Valero, S., & Inglada, J.
- Affiliations
- Centre d'Études Spatiales de la Biosphère (FR)
- Venue
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Published
- 2024
- Volume
- 17
- Pages
- 4350-4367
- ISSN
- 1939-1404 (ISSN-L)
- 2151-1535
- OpenAlex
- W4391235321
- Cited by
- 39
- References
- 48
- Citations by year
- 2026: 15 · 2025: 19 · 2024: 5
- FWCI
- 9.77
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- DeepChange (ANR-DeepChange)
- Topics
- Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
- Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Remote Sensing and Land Use
- Keywords
- satellite image time series, self-supervised learning, spatio-temporal representation learning, irregularly sampled time series, unlabeled data, land cover classification, crop classification, semantic segmentation, fine-tuning, Sentinel-2
About these checks
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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