Semantically Aware UAV Landing Site Assessment from Remote Sensing Imagery via Multimodal Large Language Models
This addresses the safety challenge for UAV operations by providing a more comprehensive landing site assessment, though it is incremental as it builds on existing multimodal and segmentation methods.
The paper tackles the problem of UAV emergency landing by assessing semantic risks like crowds and temporary structures from remote sensing imagery, using a multimodal large language model framework, and reports significant outperformance over geometric baselines in risk identification accuracy.
Safe UAV emergency landing requires more than just identifying flat terrain; it demands understanding complex semantic risks (e.g., crowds, temporary structures) invisible to traditional geometric sensors. In this paper, we propose a novel framework leveraging Remote Sensing (RS) imagery and Multimodal Large Language Models (MLLMs) for global context-aware landing site assessment. Unlike local geometric methods, our approach employs a coarse-to-fine pipeline: first, a lightweight semantic segmentation module efficiently pre-screens candidate areas; second, a vision-language reasoning agent fuses visual features with Point-of-Interest (POI) data to detect subtle hazards. To validate this approach, we construct and release the Emergency Landing Site Selection (ELSS) benchmark. Experiments demonstrate that our framework significantly outperforms geometric baselines in risk identification accuracy. Furthermore, qualitative results confirm its ability to generate human-like, interpretable justifications, enhancing trust in automated decision-making. The benchmark dataset is publicly accessible at https://anonymous.4open.science/r/ELSS-dataset-43D7.