arXiv Machine Learning

Mapping small reservoirs across Brazil from 1984 to 2025

arXiv:2606. 00675v2 Announce Type: replace Abstract: Water research in Brazil largely overlooks the widespread damming of small streams for agricultural uses including watering cattle, farm-scale hydropower, irrigation, and aquaculture.

arXiv AI
Jun 17

A geometric and deep learning reproducible pipeline for monitoring floating anthropogenic debris in urban rivers using in situ cameras

arXiv:2510. 23798v2 Announce Type: replace-cross Abstract: The proliferation of floating anthropogenic debris in rivers has emerged as a pressing environmental concern, exerting a detrimental influence on biodiversity, water quality, and human activities such as navigation and recreation.

By Gauthier Grimmer, Romain Wenger, Cl\'ement Flint, Germain Forestier, Gilles Rixhon, Valentin Chardon
arXiv AI
Jun 2

CAFOSat: A Strongly Annotated Dataset for Infrastructure-Aware CAFO Mapping Using High-Resolution Imagery

arXiv:2606. 00548v1 Announce Type: cross Abstract: Concentrated Animal Feeding Operations (CAFOs) play an important role in agricultural production but are also associated with environmental, public health, and disease surveillance concerns.

By Oishee Bintey Hoque, Nibir Chandra Mandal, Mandy L Wilson, Samarth Swarup, Madhav Marathe, Abhijin Adiga
arXiv Machine Learning
Jun 11

LakeFM: Toward a Foundation Model for Aquatic Ecosystems Using Irregular Multivariate Multi-depth Time Series Data

arXiv:2606. 11268v1 Announce Type: new Abstract: Understanding and forecasting lake dynamics is critical for monitoring water quality and ecosystem health across lakes and reservoirs.

By Abhilash Neog, Sepideh Fatemi, Medha Sawhney, Kazi Sajeed Mehrab, Aanish Pradhan, Bennett J. McAfee, Emma Marchisin, Arka Daw, Robert Ladwig, Cayelan C. Carey, Paul Hanson, Anuj Karpatne
Hugging Face Trending Papers
Jul 30

Large scale cross-regional remote sensing flood monitoring framework for operative mapping and impact analysis

Effective flood monitoring is critical for minimizing the impacts of flood disasters on populations and infrastructure. Yet reliable remote sensing across extensive and environmentally diverse regions remains challenging, as most segmentation algorithms lack the generalisation capacity required for large-scale application, while annotated flood data are scarce and unevenly distributed.