mlsanityData to 7 Oct 2026

A Multimodal Large Language Model-Driven Framework for Context-Aware UAV Emergency Landing Site Selection

arXiv 2602.01163 ↗First posted 1 Feb 2026cs.CV cs.AIcited by 0

On the map: Multimodal LLMs / vision-language models → Embodied and 3D vision-language models

Abstract (arXiv)

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 at https://github.com/chunlianghua/ELSS-dataset. ELSS includes a remote-sensing subset for simulation validation and a UAV aerial-video subset for real-world testing. 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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 8 of its 24 references that are in the map. First posted on arXiv 1 Feb 2026.

Builds on

8 of 24 references.

Closest ancestors · 4

Same direction, newest first

  1. 2025FlightGPT: Towards Generalizable and Interpretable UAV Vision-and-Language Navigation with Vision-Language Models19 May
  2. 2024EarthGPT: A Universal Multi-modal Large Language Model for Multi-sensor Image Comprehension in Remote Sensing Domain30 Jan
  3. 2023GeoChat: Grounded Large Vision-Language Model for Remote Sensing24 Nov
  4. 2023RSGPT: A Remote Sensing Vision Language Model and Benchmark28 Jul

Other references · 4

  1. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,210
  2. 2025UAVs Meet LLMs: Overviews and Perspectives Toward Agentic Low-Altitude Mobilitycited by 48
  3. 2025LLM-Land: Large Language Models for Context-Aware Drone Landingcited by 7
  4. 2022Optimizing Terrain Mapping and Landing Site Detection for Autonomous UAVscited by 3

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025Human-Inspired Neuro-Symbolic World Modeling and Logic Reasoning for Interpretable Safe UAV Landing Site Assessment
  2. 2026DVGBench: Implicit-to-Explicit Visual Grounding Benchmark in UAV Imagery with Large Vision-Language Models
  3. 2025MM-UAVBench: How Well Do Multimodal Large Language Models See, Think, and Plan in Low-Altitude UAV Scenarios?
  4. 2026Dual-Layer Semantic-Spatial Belief Mapping for Aerial Object Goal Navigation
  5. 2026PixDLM: A Dual-Path Multimodal Language Model for UAV Reasoning Segmentation
  6. 2026DisasterBench: A Multimodal Benchmark for UAV-Based Disaster Response in Complex Environments
  7. 2025Is your VLM Sky-Ready? A Comprehensive Spatial Intelligence Benchmark for UAV Navigation
  8. 2026Can Vision-Language Models Think from the Sky? Unifying UAV Reasoning and Generation