The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks
Open accessAbstract
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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“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]
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“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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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Feature Pyramid Networks for Object Detection
Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.106 · Cited by 30,939
He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.322 · Cited by 29,137
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-K., & Woo, W.-C. (2015) · arXiv · doi:10.48550/arxiv.1506.04214 · Cited by 7,709
Deep learning in agriculture: A survey
Kamilaris, A., & Prenafeta-Boldú, F. X. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.02.016 · Cited by 5,886
Stacked Hourglass Networks for Human Pose Estimation
Newell, A., Yang, K., & Deng, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46484-8_29 · Cited by 5,634
Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services
Drusch, M., Bello, U. D., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., Martimort, P., et al. (2012) · Remote Sensing of Environment · doi:10.1016/j.rse.2011.11.026 · Cited by 4,906
Research works printed but not linked: 1
- zhao, 2018, Tree canopy differentiation using instance-aware semantic segmentation, ASABE Annual International Meeting
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Transfer learning in environmental remote sensing
Ma, Y., Chen, S., Ermon, S., & Lobell, D. B. (2023) · Remote Sensing of Environment · doi:10.1016/j.rse.2023.113924 · Cited by 486
Remote Sensing Object Detection in the Deep Learning Era—A Review
Gui, S., Song, S., Qin, R., & Tang, Y. (2024) · Remote Sensing · doi:10.3390/rs16020327 · Cited by 367
SatMAE: Pre-Training Transformers for Temporal and Multi-Spectral Satellite Imagery
Cong, Y., Khanna, S., Meng, C., Liu, P., Rozi, E., He, Y., Burke, M., Lobell, D., & Ermon, S. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-0015 · Cited by 303
SatlasPretrain: A Large-Scale Dataset for Remote Sensing Image Understanding
Bastani, F., Wolters, P., Gupta, R., Ferdinando, J., & Kembhavi, A. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01538 · Cited by 223
Multi-modal temporal attention models for crop mapping from satellite time series
Garnot, V. S. F., Landrieu, L., & Chehata, N. (2022) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2022.03.012 · Cited by 156
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Metadata
- 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
- 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
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion, other-oa)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › 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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