# Human-Inspired Neuro-Symbolic World Modeling and Logic Reasoning for Interpretable Safe UAV Landing Site Assessment

arXiv: https://arxiv.org/abs/2510.22204 · HTML: https://mlsanity.com/paper/2510.22204/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in World models for navigation, one of 11 directions in World models.
- Builds on 18 of its 39 references that are in the map.
- First posted on arXiv 25 Oct 2025.

## Placed in

- World models / [World models for navigation](https://mlsanity.com/wm/world-models-for-navigation/)
- LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/)

## Builds on (most cited first, 18 of 18)

- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)
- [Code as Policies: Language Model Programs for Embodied Control](https://mlsanity.com/paper/2209.07753/) (2022)
- [Neural Probabilistic Logic Programming in DeepProbLog](https://mlsanity.com/paper/1907.08194/) (2019)
- [SatLM: Satisfiability-Aided Language Models Using Declarative Prompting](https://mlsanity.com/paper/2305.09656/) (2023)
- [Large Language Models for UAVs: Current State and Pathways to the Future](https://mlsanity.com/paper/2405.01745/) (2024)
- [TensorLog: A Differentiable Deductive Database](https://mlsanity.com/paper/1605.06523/) (2016)
- [LASER: A Neuro-Symbolic Framework for Learning Spatial-Temporal Scene Graphs with Weak Supervision](https://mlsanity.com/paper/2304.07647/) (2023)
- [Vision-Based Autonomous UAV Navigation and Landing for Urban Search and Rescue](https://mlsanity.com/paper/1906.01304/) (2019)
- [LLM-Land: Large Language Models for Context-Aware Drone Landing](https://mlsanity.com/paper/2505.06399/) (2025)
- [Multi-Resolution Elevation Mapping and Safe Landing Site Detection with Applications to Planetary Rotorcraft](https://mlsanity.com/paper/2111.06271/) (2021)
- [GARL: Genetic Algorithm-Augmented Reinforcement Learning to Detect Violations in Marker-Based Autonomous Landing Systems](https://mlsanity.com/paper/2310.07378/) (2023)
- [Free LSD: Prior-Free Visual Landing Site Detection for Autonomous Planes](https://mlsanity.com/paper/1802.09043/) (2018)
- [Visual Environment Assessment for Safe Autonomous Quadrotor Landing](https://mlsanity.com/paper/2311.10065/) (2023)
- [Robust and Efficient AI-Based Attack Recovery in Autonomous Drones](https://mlsanity.com/paper/2505.14835/) (2025)
- [Event-Based Adaptive Koopman Framework for Optic Flow-Guided Landing on Moving Platforms](https://mlsanity.com/paper/2501.16868/) (2025)
- [Drones that Think on their Feet: Sudden Landing Decisions with Embodied AI](https://mlsanity.com/paper/2510.00167/) (2025)

## Built on by (most cited first, 1 of 1)

- [Experiences from Benchmarking Vision-Language-Action Models for Robotic Manipulation](https://mlsanity.com/paper/2511.11298/) (2025)

## Abstract (arXiv)

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.