RiverScope: High-Resolution River Masking Dataset

• URL: http://arxiv.org/abs/2509.02451v1
• Date: Tue, 02 Sep 2025 16:00:27 GMT
• Title: RiverScope: High-Resolution River Masking Dataset
• 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,
• Abstract summary: RiverScope comprises 1,145 high-resolution images covering 2,577 square kilometers.<n>We establish the first global, high-resolution benchmark for river width estimation.<n>RiverScope provides a valuable resource for fine-scale and multi-sensor hydrological modeling.
• Score: 9.870770596247395
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Exploring the Underwater World Segmentation without Extra Training [55.291219073365546]
  We introduce textbfAquaOV255, the first large-scale and fine-grained underwater segmentation dataset.<n>We also present textbfEarth2Ocean, a training-free OV segmentation framework.
  arXiv  Detail & Related papers  (2025-11-11T07:22:56Z)
• A Transfer Learning-Based Method for Water Body Segmentation in Remote Sensing Imagery: A Case Study of the Zhada Tulin Area [17.87554359007837]
  The Tibetan Plateau, known as the Asian Water Tower, faces significant water security challenges due to its high sensitivity to climate change.<n>This study proposes a two-stage transfer learning strategy using the SegFormer model to overcome domain shift and data scarcit--key barriers in developing robust AI for climate-sensitive applications.
  arXiv  Detail & Related papers  (2025-07-14T09:11:33Z)
• Data Augmentation and Resolution Enhancement using GANs and Diffusion Models for Tree Segmentation [49.13393683126712]
  Urban forests play a key role in enhancing environmental quality and supporting biodiversity in cities.<n> accurately detecting trees is challenging due to complex landscapes and the variability in image resolution caused by different satellite sensors or UAV flight altitudes.<n>We propose a novel pipeline that integrates domain adaptation with GANs and Diffusion models to enhance the quality of low-resolution aerial images.
  arXiv  Detail & Related papers  (2025-05-21T03:57:10Z)
• Efficient Self-Supervised Learning for Earth Observation via Dynamic Dataset Curation [67.23953699167274]
  Self-supervised learning (SSL) has enabled the development of vision foundation models for Earth Observation (EO)<n>In EO, this challenge is amplified by the redundancy and heavy-tailed distributions common in satellite imagery.<n>We propose a dynamic dataset pruning strategy designed to improve SSL pre-training by maximizing dataset diversity and balance.
  arXiv  Detail & Related papers  (2025-04-09T15:13:26Z)
• Image-Based Relocalization and Alignment for Long-Term Monitoring of Dynamic Underwater Environments [57.59857784298534]
  We propose an integrated pipeline that combines Visual Place Recognition (VPR), feature matching, and image segmentation on video-derived images.<n>This method enables robust identification of revisited areas, estimation of rigid transformations, and downstream analysis of ecosystem changes.
  arXiv  Detail & Related papers  (2025-03-06T05:13:19Z)
• StreakNet-Arch: An Anti-scattering Network-based Architecture for Underwater Carrier LiDAR-Radar Imaging [44.96583097079915]
  We introduce StreakNet-Arch, a real-time, end-to-end binary-classification framework based on our self-developed Underwater Carrier LiDAR-Radar (UCLR)<n>Under controlled water tank validation conditions, StreakNet-Arch with Self-Attention or DBC-Attention outperforms traditional bandpass filtering.<n>We validate our UCLR system in a South China Sea trial, reaching an error of 46mm for 3D target at 1,000 m depth and 20 m range.
  arXiv  Detail & Related papers  (2024-04-14T06:19:46Z)
• Using Multi-Temporal Sentinel-1 and Sentinel-2 data for water bodies mapping [40.996860106131244]
  Climate change is intensifying extreme weather events, causing both water scarcity and severe rainfall unpredictability. This paper aims to provide valuable insights for comprehensive water resource monitoring under diverse meteorological conditions.
  arXiv  Detail & Related papers  (2024-01-05T18:11:08Z)
• DeepAqua: Self-Supervised Semantic Segmentation of Wetland Surface Water Extent with SAR Images using Knowledge Distillation [44.99833362998488]
  We present DeepAqua, a self-supervised deep learning model that eliminates the need for manual annotations during the training phase. We exploit cases where optical- and radar-based water masks coincide, enabling the detection of both open and vegetated water surfaces. Experimental results show that DeepAqua outperforms other unsupervised methods by improving accuracy by 7%, Intersection Over Union by 27%, and F1 score by 14%.
  arXiv  Detail & Related papers  (2023-05-02T18:06:21Z)
• GLH-Water: A Large-Scale Dataset for Global Surface Water Detection in Large-Size Very-High-Resolution Satellite Imagery [2.342488890032597]
  We propose the GLH-water dataset that consists of 250 satellite images and manually labeled surface water annotations. Each image is of the size 12,800 $times$ 12,800 pixels at 0.3 meter spatial resolution. To build a benchmark for GLH-water, we perform extensive experiments employing representative surface water detection models, popular semantic segmentation models, and ultra-high resolution segmentation models.
  arXiv  Detail & Related papers  (2023-03-16T13:35:56Z)
• Deep Learning Models for River Classification at Sub-Meter Resolutions from Multispectral and Panchromatic Commercial Satellite Imagery [2.121978045345352]
  This study focuses on rivers in the Arctic, using images from the Quickbird, WorldView, and GeoEye satellites. We use the RGB, and NIR bands of the 8-band multispectral sensors. Those trained models all achieve excellent precision and recall over 90% on validation data, aided by on-the-fly preprocessing of the training data specific to satellite imagery. In a novel approach, we then use results from the multispectral model to generate training data for FCN that only require panchromatic imagery, of which considerably more is available.
  arXiv  Detail & Related papers  (2022-12-27T20:56:34Z)
• Water Level Estimation Using Sentinel-1 Synthetic Aperture Radar Imagery And Digital Elevation Models [0.0]
  We propose a novel water level extracting approach, which employs Sentinel-1 Synthetic Aperture Radar imagery and Digital Elevation Model data sets. Experiments show that the algorithm achieved a low average error of 0.93 meters over three reservoirs globally.
  arXiv  Detail & Related papers  (2020-12-11T18:42:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.