arXiv:2605.24515v1 Announce Type: new Abstract: With climate change and increasing human pressure on natural landscapes, inland water resources are becoming progressively scarcer, more vulnerable, and more difficult to manage sustainably. Reliable and automated methods for detecting, monitoring, and assessing surface water bodies are therefore of growing scientific and practical importance. In this paper, we investigate and compare three distinct machine learning architectures for water body identification and monitoring. Their performance is evaluated through quantitative metrics and real-wor
The increasing pressure on natural landscapes due to climate change and human activity necessitates advanced monitoring solutions, driven by improvements in satellite imagery and machine learning.
Reliable and automated methods for water body monitoring are crucial for sustainable resource management, impacting agriculture, urban planning, and environmental conservation in regions facing hydrological stress.
The application of advanced AI to satellite data will enable more precise and dynamic water resource assessment, moving beyond manual or less sophisticated methods to predictive and real-time monitoring.
- · Water resource management agencies
- · Environmental intelligence companies
- · Agriculture sector
- · Satellite data providers
- · Regions without advanced monitoring infrastructure
- · Organizations reliant on outdated assessment methods
Improved decision-making for water allocation and drought response across affected regions.
Increased investment in related fields like hydrological modeling and climate adaptation technologies.
Potential for new international agreements or standards based on globally integrated water data.
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