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Machine learning methods for satellite data have a range of societally relevant applications, but labels used to train models can be difficult or impossible to acquire.
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M. Rußwurm, S. Wang, M. Korner, and D. Lobell · 2020
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Satellite image time series classification with pixel-set encoders and temporal self-attention
V. Sainte Fare Garnot, L. Landrieu, S. Giordano, and N. Chehata · 2020
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Europe builds ‘digital twin’of earth to hone climate forecasts, 2020
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Becoming good at ai for good
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https://developers.google.com/earth-engine/datasets/catalog
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Tick tick bloom: Harmful algal bloom detection challenge · 2023
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Treesatai benchmark archive: A multi-sensor, multi-label dataset for tree species classification in remote sensing
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CROMA: Remote sensing representations with contrastive radar-optical masked autoencoders
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ASU researcher combats food insecurity with AI
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Geo-bench: Toward foundation models for earth monitoring
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Considerations for ai-eo for agriculture in sub-saharan africa
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ViTs for SITS: Vision Transformers for Satellite Image Time Series
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Artificial intelligence to advance earth observation: a perspective
D. Tuia, K. Schindler, B. Demir, G. Camps-Valls, X. X. Zhu, M. Kochupillai, S. Džeroski, J. N. van Rijn, H. H. Hoos, F. Del Frate, et al · 2023
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Mapping crops at global scale! what works and what doesn’t?
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Two decades of winter wheat expansion and intensification in russia
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