RiverScope: High-Resolution River Masking Dataset
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| author | Daroya, Rangel Rowley, Taylor Flores, Jonathan Friedmann, Elisa Bennitt, Fiona An, Heejin Simmons, Travis Hughes, Marissa Jean Kluetmeier, Camryn L Kica, Solomon Vélez, J. Daniel Esenther, Sarah E. Howard, Thomas E. Ye, Yanqi Turcotte, Audrey Gleason, Colin Maji, Subhransu |
| author_facet | Daroya, Rangel Rowley, Taylor Flores, Jonathan Friedmann, Elisa Bennitt, Fiona An, Heejin Simmons, Travis Hughes, Marissa Jean Kluetmeier, Camryn L Kica, Solomon Vélez, J. Daniel Esenther, Sarah E. Howard, Thomas E. Ye, Yanqi Turcotte, Audrey Gleason, Colin Maji, Subhransu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_02451 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RiverScope: High-Resolution River Masking Dataset Daroya, Rangel Rowley, Taylor Flores, Jonathan Friedmann, Elisa Bennitt, Fiona An, Heejin Simmons, Travis Hughes, Marissa Jean Kluetmeier, Camryn L Kica, Solomon Vélez, J. Daniel Esenther, Sarah E. Howard, Thomas E. Ye, Yanqi Turcotte, Audrey Gleason, Colin Maji, Subhransu Computer Vision and Pattern Recognition 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. |
| title | RiverScope: High-Resolution River Masking Dataset |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.02451 |