Reliable assessment of safe landing sites in unstructured environments is essential for deploying Unmanned Aerial Vehicles (UAVs) in real-world applications such as delivery, inspection, and surveillance. Existing learning-based approaches often degrade under covariate shift and offer limited transparency, making their decisions difficult to interpret and validate on resource-constrained platforms. We present NeuroSymLand, a neuro-symbolic framework for marker-free UAV landing site safety assessment that explicitly separates perception-driven world modeling from logic-based safety reasoning. A lightweight segmentation model incrementally constructs a probabilistic semantic scene graph encoding objects, attributes, and spatial relations. Symbolic safety rules, synthesized offline via large language models with human-in-the-loop refinement, are executed directly over this world model at runtime to perform white-box reasoning, producing ranked landing candidates with human-readable explanations of the underlying safety constraints. Across 72 simulated and hardware-in-the-loop landing scenarios, NeuroSymLand achieves 61 successful assessments, outperforming four competitive baselines, which achieve between 37 and 57 successes. Qualitative analysis highlights its superior interpretability and transparent reasoning, while deployment incurs negligible edge overhead. Our results suggest that combining explicit world modeling with symbolic reasoning can support accurate, interpretable, and edge-deployable safety assessment in mobile systems, as demonstrated through UAV landing site assessment.

翻译：在非结构化环境中可靠评估安全着陆点对于在配送、巡检和监测等实际应用中部署无人机至关重要。现有的基于学习的方法通常在协变量偏移下性能下降，且透明度有限，使其决策难以在资源受限的平台上进行解释和验证。本文提出NeuroSymLand，一个用于无标记无人机着陆点安全评估的神经符号框架，该框架明确将感知驱动的世界建模与基于逻辑的安全推理分离。一个轻量级分割模型增量式构建概率语义场景图，对物体、属性及空间关系进行编码。通过大型语言模型离线合成并经人机协同优化的符号化安全规则，在运行时直接基于此世界模型执行，实现白盒推理，生成排序的着陆候选点及其底层安全约束的人类可读解释。在72个模拟和硬件在环着陆场景中，NeuroSymLand实现了61次成功评估，优于四个性能相当的基线方法（其成功次数介于37至57之间）。定性分析突显了其卓越的可解释性和透明推理能力，同时部署仅产生可忽略的边缘开销。我们的结果表明，如通过无人机着陆点评估所验证的，将显式世界建模与符号推理相结合，能够为移动系统提供准确、可解释且可边缘部署的安全评估。