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.
Abstract (translated)
地表水动态在地球气候系统中扮演着关键角色，影响生态系统、农业、灾害抵御能力和可持续发展。然而，在细粒度的空间和时间尺度上监测河流和其他地表水仍然具有挑战性——尤其是在分辨率较低的卫星数据难以捕捉到的狭窄或含泥量高的河流区域。为了解决这个问题，我们介绍了RiverScope，这是一个通过计算机科学和水文学专家合作开发的高分辨率数据集。RiverScope包括1,145张高分辨率图像（覆盖2,577平方公里），并附有专家标注的河流与地表水面掩模，这些标记需要超过100小时的手动注释工作。每一张图像都与其他几种传感器的数据进行了同步注册，如Sentinel-2、SWOT以及SWOT河流数据库（SWORD）。这使得能够评估不同传感器在成本和准确性之间的权衡——这对运营水资源监测来说是一个关键考量因素。 此外，我们建立了第一个全球性的高分辨率基准来估计河流宽度，并达到了7.2米的中位误差值，显著超越了现有的卫星衍生方法。我们在多个网络架构（例如CNNs和transformers）、预训练策略（如监督式学习与自我监督式学习）以及各种培训数据集（如ImageNet及卫星图像）上广泛测试了深度神经网络模型。 我们的最佳表现模型通过转移学习的益处，结合使用所有多光谱PlanetScope通道并通过学习适配器来实现。RiverScope为精细尺度和多传感器水文建模提供了宝贵的资源，并支持气候适应与可持续水资源管理。
URL
https://arxiv.org/abs/2509.02451
