StreetviewLLM: Extracting Geographic Information Using a Chain-of-Thought Multimodal Large Language Model
This addresses geospatial prediction challenges for fields like disaster management and urban planning, offering a novel integration of LLMs into urban analytics, though it appears incremental as it builds on existing chain-of-thought and multimodal methods.
The paper tackled the problem of geospatial predictions using unstructured multi-modal data like street view imagery by proposing StreetViewLLM, a framework that integrates a large language model with chain-of-thought reasoning and multimodal sources, resulting in superior performance in predicting urban indicators across seven global cities with improved predictive accuracy.
Geospatial predictions are crucial for diverse fields such as disaster management, urban planning, and public health. Traditional machine learning methods often face limitations when handling unstructured or multi-modal data like street view imagery. To address these challenges, we propose StreetViewLLM, a novel framework that integrates a large language model with the chain-of-thought reasoning and multimodal data sources. By combining street view imagery with geographic coordinates and textual data, StreetViewLLM improves the precision and granularity of geospatial predictions. Using retrieval-augmented generation techniques, our approach enhances geographic information extraction, enabling a detailed analysis of urban environments. The model has been applied to seven global cities, including Hong Kong, Tokyo, Singapore, Los Angeles, New York, London, and Paris, demonstrating superior performance in predicting urban indicators, including population density, accessibility to healthcare, normalized difference vegetation index, building height, and impervious surface. The results show that StreetViewLLM consistently outperforms baseline models, offering improved predictive accuracy and deeper insights into the built environment. This research opens new opportunities for integrating the large language model into urban analytics, decision-making in urban planning, infrastructure management, and environmental monitoring.