Abstract：Task-oriented route planning from remote sensing imagery requires understanding of open-ended semantic constraints and fine-grained perception of complex land-cover environments. Existing methods relying on high-definition maps, road networks, vehicle-mounted sensors, or manually specified rules struggle to adapt to such scenarios. A neuro-symbolic collaborative route planner(NSCRP) for route planning in remote sensing is proposed. First, a multimodal large language model is utilized to translate natural-language tasks into structured traversability rules and preference representations. A stable semantic land-cover map is constructed by integrating multiple semantic segmentation models. Then, semantic constraints and environmental perception results are uniformly mapped into a pixel-level cost map. An interpretable path with the minimum accumulated cost is generated via heuristic search on the predicted cost map. Experiments based on NeSy-Route show that NSCRP outperforms existing approaches in constraint satisfaction, path quality, and overall planning ability.
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2026, Vol. 39 
