Record link: 
http://hdl.handle.net/20.500.12708/203929
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Title: 
Making Archives Searchable: Vision-Language Models for Classification of Historical Aerial Imagery
en
Citation: 
Burges, M., Zambanini, S., & Sablatnig, R. (2024). Making Archives Searchable: Vision-Language Models for Classification of Historical Aerial Imagery. In GeoSearch ’24: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data (pp. 1–8). https://doi.org/10.1145/3681769.3698578
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Publisher DOI: 
10.1145/3681769.3698578
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Publication Type: 
Inproceedings - Full-Paper Contribution
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Language: 
English
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Authors: 
Burges, Marvin 
Zambanini, Sebastian 
Sablatnig, Robert 
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Organisational Unit: 
E193-01 - Forschungsbereich Computer Vision 
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Published in: 
GeoSearch '24: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data 
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ISBN: 
9798400711480
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Date (published): 
2024
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Event name: 
3rd ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data (GeoSearch '24) 
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Event date: 
29-Oct-2024
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Event place: 
Atlanta, United States of America (the)
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Number of Pages: 
8
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Peer reviewed: 
Yes
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Keywords: 
Vision-Language Models; Remote Sensing; Aerial Images; Cultural Heritage
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Project title: 
Domain-adaptive Remote sensing Image Analysis with Human-in-the-loop: 880883 (FFG - Österr. Forschungsförderungs- gesellschaft mbH) 
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Research Areas: 
Visual Computing and Human-Centered Technology: 100%
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Science Branch: 
1020 - Informatik: 90%
1010 - Mathematik: 10%
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Appears in Collections:
Conference Paper

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