Multi-temporal surface water mapping with high-resolution elevation and image data through weakly supervised deep learning
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Abstract
Monitoring the extent of surface water features (hydrography), accurately storing them in databases, and representing them on topographic maps are essential for various applications such as navigation and policy-making for legislative boundaries and permitting. In this context, hydrographic data includes features that generally have water present or image data showing signs that water is forming a terrain channel, and which would be included in 1:24,000 or larger scale topographic maps. In addition, reliable hydrographic data play a critical role to help manage environmental risks such as droughts, floods, fires, and landslides, as well as monitoring biological resources and pollutants. Inaccuracies in hydrography data can lead to modelling inaccuracies, resulting in economic, social, and environmental risks. However, generating sufficiently accurate high-resolution (HR) hydrography and terrain data for these purposes remains a substantial challenge primarily because of complex surface water dynamics and data handling limitations.
| Publication type | Conference Paper |
|---|---|
| Publication Subtype | Conference Paper |
| Title | Multi-temporal surface water mapping with high-resolution elevation and image data through weakly supervised deep learning |
| DOI | 10.5194/ica-abs-10-275-2025 |
| Volume | 7 |
| Issue | 10 |
| Publication Date | December 15, 2025 |
| Year Published | 2025 |
| Language | English |
| Publisher | International Cartographic Association |
| Contributing office(s) | Center for Geospatial Information Science (CEGIS) |
| Description | 275, 3 p. |
| Larger Work Type | Book |
| Larger Work Subtype | Conference publication |
| Larger Work Title | Abstracts of the International Cartographic Association |
| Country | United States |
| State | Alaska |