{"slug": "geowind2plan-mission-time-3d-urban-wind-prediction-for-energy-efficient-uav", "title": "GeoWind2Plan: Mission-Time 3D Urban Wind Prediction for Energy-Efficient UAV Planning", "summary": "GeoWind2Plan, a geometry-to-wind-to-planning framework from researchers publishing on arXiv (2609.36056v1), predicts mission-relevant 3D urban wind in about 3 seconds versus roughly 8 hours for computational fluid dynamics, according to the paper's abstract. Under CFD evaluation, trajectories planned with GeoWind2Plan cut UAV energy use by 6.9% in tailwind, 12.7% in headwind, and 4.5% in crosswind missions compared with wind-agnostic planning, recovering 87.9%, 85.7%, and 75.0% of CFD-reference savings. The framework uses a localized geometry-conditioned neural operator to stitch wind patches into a queryable local field and optimizes a 3D path and speed profile with a physically grounded UAV energy model.", "body_md": "arXiv:2609.36056v1 Announce Type: new \nAbstract: In urban low-altitude flight, buildings reshape ambient wind into spatially varying 3D flow, making unmanned aerial vehicle (UAV) energy depend on local wind exposure as well as path length. However, building-resolved wind information is rarely available when a mission must be planned. Computational fluid dynamics (CFD) can produce high-fidelity urban flow fields, but each simulation is tied to a fixed inflow boundary condition and can take hours to days, which is incompatible with urban UAV missions that typically last minutes to tens of minutes. We present GeoWind2Plan, a geometry-to-wind-to-planning framework for mission-time 3D urban wind prediction and energy-efficient UAV planning. Given only a background wind vector, 3D building geometry, and a start-goal pair, GeoWind2Plan transforms the building geometry into a reference-wind frame, predicts mission-relevant 3D wind patches with a localized geometry-conditioned neural operator, stitches them into a queryable local wind field, and optimizes a feasible 3D path and speed profile using a physically grounded UAV energy model. Rather than pursuing CFD-perfect reconstruction, GeoWind2Plan targets decision-useful wind prediction: trajectories are planned with predicted wind and evaluated under high-fidelity CFD wind. Across held-out urban domains, wind speeds, and mission wind-angle regimes, GeoWind2Plan performs corridor-localized wind inference in about 3 seconds, compared with roughly 8 hours for CFD. Under CFD evaluation, trajectories planned with GeoWind2Plan reduce energy by 6.9%, 12.7%, and 4.5% in tailwind, headwind, and crosswind missions relative to wind-agnostic planning, recovering 87.9%, 85.7%, and 75.0% of CFD-reference savings. These results show that fast, corridor-localized 3D urban wind prediction can make wind-aware UAV energy planning practical at mission time.", "url": "https://wpnews.pro/news/geowind2plan-mission-time-3d-urban-wind-prediction-for-energy-efficient-uav", "canonical_source": "https://arxiv.org/abs/2609.36056", "published_at": "2026-09-30 04:00:00+00:00", "updated_at": "2026-09-30 04:18:08.855300+00:00", "lang": "en", "topics": ["machine-learning", "robotics", "autonomous-vehicles", "ai-research"], "entities": ["GeoWind2Plan", "arXiv", "UAV", "CFD"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/geowind2plan-mission-time-3d-urban-wind-prediction-for-energy-efficient-uav", "markdown": "https://wpnews.pro/news/geowind2plan-mission-time-3d-urban-wind-prediction-for-energy-efficient-uav.md", "text": "https://wpnews.pro/news/geowind2plan-mission-time-3d-urban-wind-prediction-for-energy-efficient-uav.txt", "jsonld": "https://wpnews.pro/news/geowind2plan-mission-time-3d-urban-wind-prediction-for-energy-efficient-uav.jsonld"}}