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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. 

The discovery of functional dye materials with superior optical properties is crucial for advancing technologies in biomedical imaging, organic photovoltaics, and quantum information systems. Recent advancements highlight the need to accelerate this discovery process by integrating computational strategies with experimental methods. In this regard, we have employed a computational approach to explore the latent space of dye materials, utilizing swarm optimization techniques to efficiently navigate complex chemical spaces and identify optimal values of molecular properties using machine learning methods based on target properties, such as high extinction coefficients ($$\varepsilon$$). The latent space based evaluation outperformed all available features of a domain. This approach enhances inverse material design by systematically correlating molecular parameters with desired optical characteristics by implementing VAEs. In this process, by defining target properties as inputs, the model effectively determines the key molecular features necessary for engineering high-performance dye compounds.