Towards Interpretable Geo-localization: a Concept-Aware Global Image-GPS Alignment Framework

• URL: http://arxiv.org/abs/2509.01910v2
• Date: Fri, 05 Sep 2025 10:42:33 GMT
• Title: Towards Interpretable Geo-localization: a Concept-Aware Global Image-GPS Alignment Framework
• Authors: Furong Jia, Lanxin Liu, Ce Hou, Fan Zhang, Xinyan Liu, Yu Liu,
• Abstract summary: Geo-localization involves determining the exact geographic location of images captured globally.<n>Current concept-based interpretability methods fail to align effectively with Geo-alignment image-location embedding objectives.<n>To our knowledge, this is the first work to introduce interpretability into geo-localization.
• Score: 9.31168320050859
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Worldwide geo-localization involves determining the exact geographic location of images captured globally, typically guided by geographic cues such as climate, landmarks, and architectural styles. Despite advancements in geo-localization models like GeoCLIP, which leverages images and location alignment via contrastive learning for accurate predictions, the interpretability of these models remains insufficiently explored. Current concept-based interpretability methods fail to align effectively with Geo-alignment image-location embedding objectives, resulting in suboptimal interpretability and performance. To address this gap, we propose a novel framework integrating global geo-localization with concept bottlenecks. Our method inserts a Concept-Aware Alignment Module that jointly projects image and location embeddings onto a shared bank of geographic concepts (e.g., tropical climate, mountain, cathedral) and minimizes a concept-level loss, enhancing alignment in a concept-specific subspace and enabling robust interpretability. To our knowledge, this is the first work to introduce interpretability into geo-localization. Extensive experiments demonstrate that our approach surpasses GeoCLIP in geo-localization accuracy and boosts performance across diverse geospatial prediction tasks, revealing richer semantic insights into geographic decision-making processes.

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