GAEA: A Geolocation Aware Conversational Model

• URL: http://arxiv.org/abs/2503.16423v2
• Date: Mon, 24 Mar 2025 14:29:42 GMT
• Title: GAEA: A Geolocation Aware Conversational Model
• Authors: Ron Campos, Ashmal Vayani, Parth Parag Kulkarni, Rohit Gupta, Aritra Dutta, Mubarak Shah,
• Abstract summary: We introduce a conversational model GAEA that can provide information regarding the location of an image, as required by a user.<n>GAEA-1.6M is a comprehensive dataset with 800K images and around 1.6M question-answer pairs constructed by leveraging OpenStreetMap (OSM) attributes and geographical context clues.<n>We consider 11 state-of-the-art open-source and proprietary LMMs and demonstrate that GAEA significantly outperforms the best open-source model, LLaVA-OneVision by 25.69% and the best proprietary model, GPT-4o by 8.28%.
• Score: 41.622284747213925
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Image geolocalization, in which, traditionally, an AI model predicts the precise GPS coordinates of an image is a challenging task with many downstream applications. However, the user cannot utilize the model to further their knowledge other than the GPS coordinate; the model lacks an understanding of the location and the conversational ability to communicate with the user. In recent days, with tremendous progress of large multimodal models (LMMs) -- proprietary and open-source -- researchers have attempted to geolocalize images via LMMs. However, the issues remain unaddressed; beyond general tasks, for more specialized downstream tasks, one of which is geolocalization, LMMs struggle. In this work, we propose to solve this problem by introducing a conversational model GAEA that can provide information regarding the location of an image, as required by a user. No large-scale dataset enabling the training of such a model exists. Thus we propose GAEA-1.6M, a comprehensive dataset with 800K images and around 1.6M question-answer pairs constructed by leveraging OpenStreetMap (OSM) attributes and geographical context clues. For quantitative evaluation, we propose a diverse benchmark, GAEA-Bench, comprising 4K image-text pairs to evaluate conversational capabilities equipped with diverse question types. We consider 11 state-of-the-art open-source and proprietary LMMs and demonstrate that GAEA significantly outperforms the best open-source model, LLaVA-OneVision by 25.69% and the best proprietary model, GPT-4o by 8.28%. Our dataset, model and codes are available.

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