{
 "id": "DarthReca/hydro-chronos",
 "owner": "DarthReca",
 "name": "hydro-chronos",
 "downloads": 150,
 "likes": 2,
 "last_modified": "2026-05-02T12:09:06.000Z",
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 "gated": false,
 "tasks": [
  "image-to-image"
 ],
 "citation": "@inproceedings{10.1145/3748636.3762732,\n  author = {Rege Cambrin, Daniele and Poeta, Eleonora and Pastor, Eliana and Corley, Isaac and Cerquitelli, Tania and Baralis, Elena and Garza, Paolo},\n  title = {HydroChronos: Forecasting Decades of Surface Water Change},\n  year = {2025},\n  isbn = {9798400720864},\n  publisher = {Association for Computing Machinery},\n  address = {New York, NY, USA},\n  url = {https://doi.org/10.1145/3748636.3762732},\n  doi = {10.1145/3748636.3762732},\n  abstract = {Forecasting surface water dynamics is crucial for water resource management and climate change adaptation. However, the field lacks comprehensive datasets and standardized benchmarks. In this paper, we introduce HydroChronos, a large-scale, multi-modal spatiotemporal dataset for surface water dynamics forecasting designed to address this gap. We couple the dataset with three forecasting tasks. The dataset includes over three decades of aligned Landsat 5 and Sentinel-2 imagery, climate data, and Digital Elevation Models for diverse lakes and rivers across Europe, North America, and South America. We also propose AquaClimaTempo UNet, a novel spatiotemporal architecture with a dedicated climate data branch, as a strong benchmark baseline. Our model significantly outperforms a Persistence baseline for forecasting future water dynamics by +14\\% and +11\\% F1 across change detection and direction of change classification tasks, and by +0.1 MAE on the magnitude of change regression. Finally, we conduc",
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 "revision": "3a8242d0053ac2697f53a976bea9d43c63c023d3",
 "stated_evidence": "https://huggingface.co/datasets/DarthReca/hydro-chronos/blob/3a8242d0053ac2697f53a976bea9d43c63c023d3/README.md",
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}