DarthReca/hydro-chronos

Tasks

• image-to-image

Citation text from the card

@inproceedings{10.1145/3748636.3762732,
  author = {Rege Cambrin, Daniele and Poeta, Eleonora and Pastor, Eliana and Corley, Isaac and Cerquitelli, Tania and Baralis, Elena and Garza, Paolo},
  title = {HydroChronos: Forecasting Decades of Surface Water Change},
  year = {2025},
  isbn = {9798400720864},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3748636.3762732},
  doi = {10.1145/3748636.3762732},
  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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