City-Scale Industrial Land Grounding and Environmental Risk Mapping With Retrieval-Augmented Vision–Language Models

Abstract

Effective management of industrial land is essential given its substantial environmental footprint, intensive resource consumption, and implications for urban planning and public health. Accurate visual grounding of industrial parcels in remote sensing imagery provides a critical basis for assessing associated environmental risks and supporting evidence-based resource allocation. However, existing approaches often rely primarily on visual cues and remain vulnerable to domain-specific ambiguity in complex urban environments. To address these limitations, we propose retrieval-augmented geospatial vision–language grounding (RAGVLG), a model-agnostic framework that integrates structured knowledge retrieval with vision–language models (VLMs) to improve industrial land grounding in remote sensing imagery. RAGVLG retrieves domain knowledge and contextual exemplars to guide grounding decisions, enabling flexible adaptation to different VLM backbones. We construct an industrial land-grounding dataset and systematically evaluate the proposed framework, demonstrating consistent improvements over baseline VLMs across multiple backbones. We further demonstrate its utility through city-scale applications in Shenzhen and Hong Kong, where industrial land is mapped, and the spatial distribution of potential environmental risks is assessed by integrating key environmental parameters. Local indicators of spatial association are used to quantify spatial clustering of risk. The results show that high–high hotspots account for 10.62% of all grids in Shenzhen, forming a continuous coastal corridor primarily across Nanshan and Baoan Districts. Hong Kong exhibits a higher proportion of high–high clusters, at 15.31%, with more fragmented high-risk areas concentrated in Kwai Tsing and Kowloon. These findings provide quantitative and spatially explicit evidence for regional industrial planning, environmental risk management, and sustainable industrial development.