Real time application for estimation of urban pluvial flood damage

Published in:: Abstracts of the ICA
Vol. 9.2025 (14.03.2025), art. no. 22, p. 1-1
Volume:: 9.2025
Publication date:: 14.03.2025
URN:: urn:nbn:de:0307-20250812-001-2
DOI:: 10.5194/ica-abs-9-22-2025
Language:: English
Type of Resource:: Text
Extent:: 1 Online-Ressource (Seite 1-1, 684,31 kB) : 1 Textdatei (PDF)
Keywords:: impact forecasting; building damage; real-time assessment; machine learning; vulnerability mapping; Auswirkungsprognose; Gebäudeschaden; Echtzeit-Bewertung; Maschinelles Lernen^GND; Schwachstellen-Kartierung
DDC subject of DNB:: 520 Astronomy & allied sciences
URL:: https://doi.org/10.5194/ica-abs-9-22-2025
URL:: https://ica-abs.copernicus.org/articles/9/22/2025/
Link to Raw Object:: https://ica-abs.copernicus.org/articles/9/22/2025/ica-abs-9-22-2025.pdf
Hosting institution:: University of Potsdam (UP) [DE]
Statement of responsibility:: Sarah Lindenlaub, Guilherme Samprogna Mohor, Annegret Thieken
Bibliographic notes:: Published in: Abstracts of the ICA, Volume 9, 2025 - RIMMA2025 - International Conference on Forecasting, Preparedness, Warning, and Response. Visualization, Communication, and Information Management; 28–31 January 2025, University of Bern, Switzerland; Editors: Christophe Lienert, Andreas Paul Zischg, Horst Kremers, Jamie McCaughey, Lara Zinkl, and David N. Bresch | https://ica-abs.copernicus.org/articles/9/index.html