{"ID":2875512,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.02451","arxiv_id":"2509.02451","title":"RiverScope: High-Resolution River Masking Dataset","abstract":"Surface water dynamics play a critical role in Earth's climate system, influencing ecosystems, agriculture, disaster resilience, and sustainable development. Yet monitoring rivers and surface water at fine spatial and temporal scales remains challenging -- especially for narrow or sediment-rich rivers that are poorly captured by low-resolution satellite data. To address this, we introduce RiverScope, a high-resolution dataset developed through collaboration between computer science and hydrology experts. RiverScope comprises 1,145 high-resolution images (covering 2,577 square kilometers) with expert-labeled river and surface water masks, requiring over 100 hours of manual annotation. Each image is co-registered with Sentinel-2, SWOT, and the SWOT River Database (SWORD), enabling the evaluation of cost-accuracy trade-offs across sensors -- a key consideration for operational water monitoring. We also establish the first global, high-resolution benchmark for river width estimation, achieving a median error of 7.2 meters -- significantly outperforming existing satellite-derived methods. We extensively evaluate deep networks across multiple architectures (e.g., CNNs and transformers), pretraining strategies (e.g., supervised and self-supervised), and training datasets (e.g., ImageNet and satellite imagery). Our best-performing models combine the benefits of transfer learning with the use of all the multispectral PlanetScope channels via learned adaptors. RiverScope provides a valuable resource for fine-scale and multi-sensor hydrological modeling, supporting climate adaptation and sustainable water management.","short_abstract":"Surface water dynamics play a critical role in Earth's climate system, influencing ecosystems, agriculture, disaster resilience, and sustainable development. Yet monitoring rivers and surface water at fine spatial and temporal scales remains challenging -- especially for narrow or sediment-rich rivers that are poorly c...","url_abs":"https://arxiv.org/abs/2509.02451","url_pdf":"https://arxiv.org/pdf/2509.02451v2","authors":"[\"Rangel Daroya\",\"Taylor Rowley\",\"Jonathan Flores\",\"Elisa Friedmann\",\"Fiona Bennitt\",\"Heejin An\",\"Travis Simmons\",\"Marissa Jean Hughes\",\"Camryn L Kluetmeier\",\"Solomon Kica\",\"J. Daniel Vélez\",\"Sarah E. Esenther\",\"Thomas E. Howard\",\"Yanqi Ye\",\"Audrey Turcotte\",\"Colin Gleason\",\"Subhransu Maji\"]","published":"2025-09-02T16:00:27Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Transformer\",\"Convolutional Neural Network\"]","has_code":false}