DINO self-supervised features from Sentinel-2 imagery predict piped water and sewage access at 2.56 km resolution in Africa, achieving AUROCs of 91.54% and 93.24% with R² alignments of 0.92 and 0.72 to JMP statistics.
Remote Sensing of Environment , volume =
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A tiered ensemble remote-sensing system delivers continuous daily flood inundation maps for Pakistan by prioritizing high-resolution sensors and falling back to coarser ones when needed.
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Seeing SDG 6 from space: local-scale monitoring of piped water and sewage system access across Africa using satellite imagery and self-supervised learning
DINO self-supervised features from Sentinel-2 imagery predict piped water and sewage access at 2.56 km resolution in Africa, achieving AUROCs of 91.54% and 93.24% with R² alignments of 0.92 and 0.72 to JMP statistics.
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Continuous Flood Nowcasting in South Asia: A Multi-Sensor Ensemble Remote Sensing Framework for Flood Extent
A tiered ensemble remote-sensing system delivers continuous daily flood inundation maps for Pakistan by prioritizing high-resolution sensors and falling back to coarser ones when needed.