As Uncrewed Aerial Vehicles (UAVs) transition toward higher levels of autonomy, the ability to perform unassisted recovery in non-cooperative, unstructured environments becomes critical. Achieving safe autonomous landing requires high-fidelity semantic resolution to distinguish navigable terrain from hazardous obstacles, yet development is often hindered by the scarcity of annotated aerial datasets. This work proposes a comprehensive perception and data generation pipeline designed to bridge the sim-to-real gap for autonomous landing tasks. We introduce a procedural synthetic data engine that generates photorealistic urban environments with automated semantic annotations through domain randomization. A Transformer-based OneFormer architecture is fine-tuned exclusively on this synthetic data, leveraging multi-head self-attention mechanisms for global context resolution. To ensure operational safety, a deterministic landing module utilizes a Euclidean Distance Transform (EDT) and dynamic inference logic to identify the largest inscribed safe landing zones while maintaining strict clearance buffers around obstacles. Quantitative benchmarking against the UAVid dataset demonstrates robust semantic segmentation performance, while qualitative validation on real-world UAV footage confirms the system's ability to identify collision-free landing sites in unseen environments. Our results highlight the potential of high-fidelity procedural simulation to eliminate the need for manual annotation while providing robust, edge-deployable situational awareness for autonomous UAV recovery.

翻译：随着无人驾驶飞行器向更高自主性水平过渡，在非合作、非结构化环境中执行无辅助回收的能力变得至关重要。实现安全自主着陆需要高保真语义分辨率以区分可通行地形与危险障碍物，然而由于带标注航拍数据集的稀缺，相关开发常受制约。本文提出一种面向自主着陆任务的综合感知与数据生成流水线，旨在弥合仿真到真实场景的差距。我们引入一个程序化合成数据引擎，通过域随机化技术生成具有照片级真实感的城市场景，并实现自动化语义标注。基于Transformer架构的OneFormer模型完全依赖该合成数据进行微调，通过多头自注意力机制实现全局上下文解析。为确保运行安全，确定性着陆模块利用欧几里得距离变换和动态推理逻辑，在保持严格障碍物安全缓冲距离的同时，定位最大内接安全着陆区域。针对UAVid数据集的定量基准测试验证了稳健的语义分割性能，而对真实无人机影像的定性验证则证实了系统在未知环境中识别无碰撞着陆点的能力。研究结果表明，高保真度程序化仿真有望消除人工标注需求，为无人飞行器自主回收提供鲁棒且可边缘部署的态势感知能力。