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From Drone Imagery to Livability Mapping: AI-powered Environment Perception in Rural China

arXiv 2508.21738 ↗First posted 29 Aug 2025cs.CY cs.CVcited by 0

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Abstract (arXiv)

The high cost of acquiring rural street view images has constrained comprehensive environmental perception in rural areas. Drone photographs, with their advantages of easy acquisition, broad coverage, and high spatial resolution, offer a viable approach for large-scale rural environmental perception. However, a systematic methodology for identifying key environmental elements from drone photographs and quantifying their impact on environmental perception remains lacking. To address this gap, a Vision-Language Contrastive Ranking Framework (VLCR) is designed for rural livability assessment in China. The framework employs chain-of-thought prompting strategies to guide multimodal large language models (MLLMs) in identifying visual features related to quality of life and ecological habitability from drone photographs. Subsequently, to address the instability in pairwise village comparison, a text description-constrained drone photograph comparison strategy is proposed. Finally, to overcome the efficiency bottleneck in nationwide pairwise village comparisons, an innovation ranking algorithm based on binary search interpolation is developed, which reduces the number of comparisons through automated selection of comparison targets. The proposed framework achieves superior performance with a Spearman Footrule distance of 0.74, outperforming mainstream commercial MLLMs by approximately 0.1. Moreover, the mechanism of concurrent comparison and ranking demonstrates a threefold enhancement in computational efficiency. Our framework has achieved data innovation and methodological breakthroughs in village livability assessment, providing strong support for large-scale village livability analysis. Keywords: Drone photographs, Environmental perception, Rural livability assessment, Multimodal large language models, Chain-of-thought prompting.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 83 references that are in the map. First posted on arXiv 29 Aug 2025.

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15 of 83 references.

References · 15

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  10. 2024A citizen science toolkit to collect human perceptions of urban environments using open street view imagescited by 9
  11. 2024MVP-Bench: Can Large Vision--Language Models Conduct Multi-level Visual Perception Like Humans?cited by 9
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3 more
  1. 2025Multimodal Large Language Models-Enabled UAV Swarm: Towards Efficient and Intelligent Autonomous Aerial Systemscited by 7
  2. 2025Interpretable Multimodal Framework for Human-Centered Street Assessment: Integrating Visual-Language Models for Perceptual Urban Diagnosticscited by 2
  3. 2022Combining deep learning and crowdsourcing geo-images to predict housing quality in rural Chinacited by 1

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