Perceiving multidimensional disaster damages from street-view images using visual-language models

Category

Published in:

Abstracts of the ICA
Vol. 10.2025 (16.12.2025), art. no. 310, p. 1-2

Volume:

10.2025

Publication date:

16.12.2025

URN:

urn:nbn:de:0307-20260507-004-4

DOI:

10.5194/ica-abs-10-310-2025

Language:

English

Type of Resource:

Text

Extent:

1 Online-Ressource (Seite 1-2, 2,27 MB) : 2 Illustrationen, 1 Tabelle, 1 Textdatei (PDF)

Keywords:

Large Language Models; LLM; Disaster Perception; Street-view Imagery; Multimodal AI; Resilience; Großes Sprachmodell^GND; Katastrophenwahrnehmung; Street-View-Bild; multimodale KI; Resilienz^GND

DDC subject of DNB:

520 Astronomy & allied sciences

URL:

https://doi.org/10.5194/ica-abs-10-310-2025

URL:

https://ica-abs.copernicus.org/articles/10/310/2025/

Link to Raw Object:

https://ica-abs.copernicus.org/articles/10/310/2025/ica-abs-10-310-2025.pdf

Hosting institution:

Texas A&M University (TAMU) [US]

Hosting institution:

Internationale Forschungseinrichtungen

Statement of responsibility:

Yifan Yang, Lei Zou

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