# Semantically Aware UAV Landing Site Assessment from Remote Sensing Imagery via Multimodal Large Language Models

Source: [Semantically Aware UAV Landing Site Assessment from Remote Sensing Imagery via Multimodal Large Language Models](<https://www.sota2.com/research/paper/6981813ba8b01e76cc8e23bb>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-02-04T05:00:15.039Z

Authors: Chunliang Hua, Zeyuan Yang, Lei Zhang, Jiayang Sun, Fengwen Chen, Chunlan Zeng, Xiao Hu

Published: 2026-02-01T11:30:03.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [UAV Emergency Landing Site Ranking on Potsdam ELSS Benchmark 1.0 (test)](<https://www.sota2.com/research/sota/uav-emergency-landing-site-ranking-on-potsdam-elss-benchmark-1-0-test.md>): Ranking performance for UAV emergency landing site assessment; Potsdam ELSS Benchmark 1.0 (test); R.R.: 68.

- [UAV Emergency Landing Site Ranking on Nanjing ELSS Benchmark 1.0 (test)](<https://www.sota2.com/research/sota/uav-emergency-landing-site-ranking-on-nanjing-elss-benchmark-1-0-test.md>): Ranking performance for UAV emergency landing site assessment; Nanjing ELSS Benchmark 1.0 (test); Recall Rate: 67.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2602.01163>)

[PDF](<https://arxiv.org/pdf/2602.01163.pdf>)