GAEA: A Geolocation Aware Conversational Model

Summary

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 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 a comprehensive dataset `GAEA-Train` 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 best proprietary model, GPT-4o by 8.28%. We will publicly release our dataset and codes.

GAEA is the first open-source conversational model for conversational capabilities equipped with global-scale geolocalization.

Main contributions:

1. GAEA-Train: A Diverse Training Dataset: We propose GAEA-Train, a new dataset designed for training conversational image geolocalization models, incorporating diverse visual and contextual data.
2. GAEA-Bench: Evaluating Conversational Geolocalization: To assess conversational capabilities in geolocalization, we introduce GAEA-Bench, a benchmark featuring various question-answer formats.
3. GAEA: An Interactive Geolocalization Chatbot: We present GAEA, a conversational chatbot that extends beyond geolocalization to provide rich contextual insights about locations from images.
4. Benchmarking Against State-of-the-Art LMMs: We quantitatively compare our model’s performance against 8 open-source and 3 proprietary LMMs, including GPT-4o and Gemini-2.0-Flash.

This page is dedicated to the GAEA model

Model Description

Architecture

How To Use

Evaluation Results

Comparison with SoTA LMMs on GAEA-Bench (Conversational)

Qualitative Results (Conversational)

Comparison with Specialized Models on Standard Geolocalization Datasets

Comparison with best SoTA LMMs on City/Country Prediction