MACityChat: Integrating remote sensing professional large model with general-purpose large model for multi-domain urban land use analysis

Abstract

Urbanization remains a global trend, with urban land use being a key component of the process. The effective integration and management of land use are critical for the sustainable development of cities. Traditional urban land use analysis methods can fit dynamic models of land use changes nonlinearly, but they face two challenges: First, the analysis process of existing technologies is often a black-box, with unknown principles, reducing the reliability and authenticity of results. Second, traditional machine learning can only analyze urban land use changes from a single domain, such as remote sensing, overlooking the influence of economic and sociological factors. We propose an interpretable urban land use change analysis task and design MACityChat, a framework that combines remote sensing-specific large models with general-purpose large language models for multidisciplinary generalized analysis, while also visualizing the model’s analytical results. In this framework remote sensing images are input into a remote sensing large model, which transforms the semantic objects in the images into textual descriptions. These descriptions are then fed into a general-purpose large language model. A regional tag-guiding module directs the general-purpose language model to incorporate local economic, policy, and cultural knowledge to perform generalized analysis. Finally, the analysis results are visualized on the remote sensing images, providing a detailed examination of urban land use. Extensive experiments show that MACityChat can provide detailed and effective analyses of urban land use changes and visualize these analyses, offering an interpretable and superior solution to urban land use problems.