Multi-temporal surface water mapping with high-resolution elevation and image data through weakly supervised deep learning

Category

Published in:

Abstracts of the ICA
Vol. 10.2025 (15.12.2025), art. no. 275, p. 1-3

Volume:

10.2025

Publication date:

15.12.2025

URN:

urn:nbn:de:0307-20260423-000-4

DOI:

10.5194/ica-abs-10-275-2025

Language:

English

Type of Resource:

Text

Extent:

1 Online-Ressource (Seite 1-3, 831,14 kB) : 1 Illustration, 1 Tabelle, 1 Textdatei (PDF)

Keywords:

hydrography; deep learning; drainage network; shallow water channel depth; U-net; Hydrografie; Entwässerungsnetz^GND; Tiefe von Flachwasserkanälen; Convolutional Neural Network^GND; Bildsegmentierung^GND

DDC subject of DNB:

520 Astronomy & allied sciences

URL:

https://doi.org/10.5194/ica-abs-10-275-2025

URL:

https://ica-abs.copernicus.org/articles/10/275/2025/

Link to Raw Object:

https://ica-abs.copernicus.org/articles/10/275/2025/ica-abs-10-275-2025.pdf

Hosting institution:

Ohio State University [US]

Hosting institution:

United States Geological Survey (USGS) [US]

Hosting institution:

University of Illinois System [US]

Hosting institution:

Internationale Forschungseinrichtungen

Statement of responsibility:

Lawrence Stanislawski, Rongjun Qin, Jung Kuan Liu, Ethan Shavers, Shaowen Wang, Nattapon Jaroenchai, Philip Thiem

Bibliographic notes:

Published in: Abstracts of the ICA, Volume 10, 2025 - 32nd International Cartographic Conference (ICC 2025); 17–22 August 2025, Vancouver, Canada; Editors: Mir Abolfazl Mostafavi, Jonathan Li, and Emmanuel Stefanakis | https://ica-abs.copernicus.org/articles/10/index.html