Update README.md

README.md

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 ## Overview
 
-Coastal and marine environments play a critical role in climate regulation, biodiversity, and the global economy. Satellite remote sensing provides consistent large-scale observation of these areas, yet utilizing representation learning for marine domains remains under-explored. Existing geospatial foundation models (GFMs) are primarily evaluated on terrestrial tasks and lack comprehensive marine benchmarks.
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 WaterBench is a benchmark and evaluation protocol for assessing GFMs on high-resolution (10m) radar and optical satellite imagery (Sentinel-1, Sentinel-2) across two downstream task families: image-level regression and classification (e.g., water quality, bathymetry, oil-slick detection) and pixel-level segmentation (e.g., mangroves, seagrass). We also include 300m Sentinel-3 ocean-color imagery as contextual information. WaterBench spans multiple coastal regions and seasons, defining fixed splits for in-distribution (ID) evaluation and held-out tests that are spatial (new geographies) and, where applicable, temporal (new years).
 
 **Total size:** ~164 GB (44 tar archives across 6 tasks)