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UrbanCross: Enhancing Satellite Image-Text Retrieval with Cross-Domain Adaptation
April 23, 2024, 4:47 a.m. | Siru Zhong, Xixuan Hao, Yibo Yan, Ying Zhang, Yangqiu Song, Yuxuan Liang
cs.CV updates on arXiv.org arxiv.org
Abstract: Urbanization challenges underscore the necessity for effective satellite image-text retrieval methods to swiftly access specific information enriched with geographic semantics for urban applications. However, existing methods often overlook significant domain gaps across diverse urban landscapes, primarily focusing on enhancing retrieval performance within single domains. To tackle this issue, we present UrbanCross, a new framework for cross-domain satellite image-text retrieval. UrbanCross leverages a high-quality, cross-domain dataset enriched with extensive geo-tags from three countries to highlight domain …
abstract access applications arxiv challenges cs.ai cs.cv diverse domain domain adaptation domains however image information performance retrieval satellite semantics text type urban
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