GeoWind2Plan: Mission-Time 3D Urban Wind Prediction for Energy-Efficient UAV Planning

Shaoxiang Qin; Yucheng Zhao; Fuyuan Lyu; Di Zhou; Jiachen Yao; Xue Liu; Anima Anandkumar; Liangzhu Leon Wang

cs.AISep 28, 2026

GeoWind2Plan: Mission-Time 3D Urban Wind Prediction for Energy-Efficient UAV Planning

Authors: Shaoxiang Qin, Yucheng Zhao, Fuyuan Lyu, Di Zhou, Jiachen Yao, Xue Liu, Anima Anandkumar, Liangzhu Leon Wang, +1 more

Organizations: University of Tennessee, Knoxville · Concordia University · McGill University · California Institute of Technology · MBZUAI

Abstract

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.

Figures & tables

Figure 1 : Overview of the GeoWind2Plan framework. Given a start-goal pair, 3D building geometry, and a background wind vector, GeoWind2Plan extracts a mission-relevant corridor, rotates the local geometry into a reference-wind frame, predicts corridor-relevant wind patches with a localized geometry-conditioned neural operator, stitches them into a queryable local 3D wind field, rescales the field from the reference training speed to the given background wind speed, and rotates the predicted wind field back to the city frame. The resulting local wind field is used by an RRT-initialized continuous optimizer to produce an energy-efficient 3D UAV path and speed profile.

Urban block	$U~(\mathrm{m}/\mathrm{s})$	Method	Unit-distance energy consumption $(\mathrm{Wh}/\mathrm{km})$
Urban block	$U~(\mathrm{m}/\mathrm{s})$	Method	All	Tailwind	Headwind	Crosswind
A	2	Ground truth	12.4 $\pm$ 0.8	11.3 $\pm$ 0.4	13.2 $\pm$ 0.3	12.5 $\pm$ 0.6
		Wind-agnostic	12.7 $\pm$ 0.8 (2.4% $\uparrow$ )	11.7 $\pm$ 0.3 (3.1% $\uparrow$ )	13.7 $\pm$ 0.4 (3.8% $\uparrow$ )	12.7 $\pm$ 0.6 (1.5% $\uparrow$ )
		Profile wind	12.6 $\pm$ 0.8 (1.3% $\uparrow$ )	11.4 $\pm$ 0.4 (1.2% $\uparrow$ )	13.4 $\pm$ 0.4 (2.1% $\uparrow$ )	12.6 $\pm$ 0.6 (1.0% $\uparrow$ )
		Ours	12.5 $\pm$ 0.8 (0.4% $\uparrow$ )	11.3 $\pm$ 0.4 (0.2% $\uparrow$ )	13.3 $\pm$ 0.3 (0.7% $\uparrow$ )	12.5 $\pm$ 0.6 (0.3% $\uparrow$ )
	4	Ground truth	12.3 $\pm$ 1.4	10.2 $\pm$ 0.6	13.7 $\pm$ 0.5	12.4 $\pm$ 1.0

Table 1 : Quantitative evaluation of UAV trajectory-planning efficiency across urban blocks A, B, and E at wind speed

U=4\,\mathrm{m/s}

and relative wind-angle regimes (see Appendix Table 4 for C and D). The table reports unit-distance energy consumption

(\mathrm{Wh}/\mathrm{km})

as mean

\pm

standard deviation, with extra energy measured relative to Ground truth reference. The best results are highlighted in bold.

Figure 2 : Qualitative trajectory-planning comparison in urban block D. Panels (a)–(d) show four randomly selected start–goal missions with different relative wind angles. Each pair compares the trajectory planned with the offline CFD reference wind field ( Ground truth ) and the trajectory planned with GeoWind2Plan’s predicted 3D wind field ( Ours ). Wind-speed slices and horizontal vectors at $75\,\mathrm{m}$ are shown as context, with optimized UAV trajectories overlaid.

Panel of Figure 3 — Figure 3 : Representative trajectories and trajectory statistics under different relative wind angles.

Method	Corridor Width	Energy (Wh/km)
Ground truth	Full field	$12.3\pm 1.4$
Ours	Full field	$12.5\pm 1.5$
Ours	50%	$12.4\pm 1.5$
Ours	20%	$12.4\pm 1.6$
Ours	10%	$12.4\pm 1.9$
Wind-agnostic	Full field	$13.3\pm 1.8$

Table 2 : UAV unit-distance energy consumption under varying corridor width constraints on urban block A, averaged over all relative wind-angle regimes.

Appendix figures & tables8 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 4 : Additional task trajectory visualization over urban blocks A–E. Four tasks are randomly selected from each urban block, demonstrating generalizability across different urban environments and start–goal settings.

Figure 5 : Additional trajectory-planning comparisons across 40 randomly sampled missions in urban block D. Each panel overlays trajectories planned with four different planning-time wind inputs: offline CFD reference wind ( GT ), GeoWind2Plan-predicted local 3D wind ( Ours ), height-only profile wind ( Profile ), and zero wind ( Agnostic ).

				Mean Altitude (m)				Mean Speed (m/s)
Urban block	Height (m)	Wind speed	Condition	Ground truth	Ours	Profile wind	Wind agnostic	Ground truth	Ours	Profile wind	Wind agnostic
A	50	4	Overall	71.4	71.4	63.1	66.8	13.91	13.92	13.82	13.53
			Tailwind	100.7	102.3	108.2	65.5	15.71	15.89	15.50	13.53
			Headwind	53.6	53.5	34.7	69.1	12.90	12.80	12.88	13.54
			Crosswind	69.4	69.0	60.6	66.1	13.75	13.74	13.67	13.53
	75	2	Overall	86.7	86.1	75.0	87.6	13.65	13.68	13.66	13.54

Appendix

Table 3 : Additional statistics of flight characteristics under different wind conditions, heights, and wind speeds, including altitude and speed.

Urban block	$U~(\mathrm{m}/\mathrm{s})$	Method	Unit-distance energy consumption $(\mathrm{Wh}/\mathrm{km})$
Urban block	$U~(\mathrm{m}/\mathrm{s})$	Method	All	Tailwind	Headwind	Crosswind
A	2	Ground truth	12.4 $\pm$ 0.8	11.3 $\pm$ 0.4	13.2 $\pm$ 0.3	12.5 $\pm$ 0.6
		Wind-agnostic	12.7 $\pm$ 0.8 (2.4% $\uparrow$ )	11.7 $\pm$ 0.3 (3.1% $\uparrow$ )	13.7 $\pm$ 0.4 (3.8% $\uparrow$ )	12.7 $\pm$ 0.6 (1.5% $\uparrow$ )
		Profile wind	12.6 $\pm$ 0.8 (1.3% $\uparrow$ )	11.4 $\pm$ 0.4 (1.2% $\uparrow$ )	13.4 $\pm$ 0.4 (2.1% $\uparrow$ )	12.6 $\pm$ 0.6 (1.0% $\uparrow$ )
		Ours	12.5 $\pm$ 0.8 (0.4% $\uparrow$ )	11.3 $\pm$ 0.4 (0.2% $\uparrow$ )	13.3 $\pm$ 0.3 (0.7% $\uparrow$ )	12.5 $\pm$ 0.6 (0.3% $\uparrow$ )
	4	Ground truth	12.3 $\pm$ 1.4	10.2 $\pm$ 0.6	13.7 $\pm$ 0.5	12.4 $\pm$ 1.0

Appendix

Table 4 : Quantitative evaluation of UAV trajectory-planning efficiency across five urban blocks (A–E), wind speeds

U\in\{2,4,8\}\,\mathrm{m/s}

, and relative wind-angle regimes. The table reports unit-distance energy consumption

(\mathrm{Wh}/\mathrm{km})

as mean

\pm

standard deviation, with extra energy measured relative to Ground truth reference. The best results are highlighted in bold.

Figure 6 : 3D building geometry visualizations of the five randomly selected evaluation urban blocks used in the experiments: four standard $1.2\,\mathrm{km}\times 1.2\,\mathrm{km}$ blocks (A–D) and one larger $3\,\mathrm{km}\times 3\,\mathrm{km}$ block (E).

Pair	$U_{a}$	$U_{b}$	$U_{\mathrm{ref}}$	$\epsilon_{\mathrm{vec}}$	$\alpha_{\mathrm{mean}}$
$2$ vs. $4$	$4$	$2$	$4$	$0.0669$	$0.1115~\mathrm{rad}$ $(6.39^{\circ})$
$8$ vs. $4$	$4$	$8$	$4$	$0.1124$	$0.1365~\mathrm{rad}$ $(7.82^{\circ})$

Appendix

Table 5 : Metrics for normalized CFD wind-field agreement under speed rescaling. The reference speed is

U_{\mathrm{ref}}=4

Panel of Figure 7 — Figure 7 : Comparison of raw and normalized CFD wind velocity components across varying inlet speeds. The raw spatial flow patterns $(u,v,w)$ are highly similar and differ primarily in absolute scale, while normalizing by the corresponding inlet speeds produces aligned components $(\tilde{u},\tilde{v},\tilde{w})$ across different speeds.

Symbol	Meaning
Urban domain and geometry
$\Omega\subset\mathbb{R}^{3}$	3D urban computational domain.
$\Omega_{\mathrm{fly}}$	Feasible UAV flight envelope, restricted to free space and the allowed altitude band.
$\Omega_{\mathrm{task}}$	Task-relevant region used for local wind inference and planning.
$\bm{x}$ , $\bm{y}$	Spatial locations in the original city frame and the reference-wind frame, respectively.
$\mathcal{G}$	Building occupancy field; $\mathcal{G}(\bm{x})=1$ denotes building space and $\mathcal{G}(\bm{x})=0$ denotes free space.

Appendix

Table 13

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Urban Environments Surrogate Models

Open paper AskPDF arXiv

Advanced air mobility operations hold the potential to enhance and expand regional transportation of both people and goods in populated areas. However, hazardous flight conditions arising from interactions between wind and the built environment remain a significant challenge for aerial vehicles in urban settings. This work proposes a novel framework towards safe flight planning of aerial vehicles in windy urban environments. A learning-based surrogate model is trained to rapidly predict flow fields from readily available information such as building geometry and incident wind. This surrogate prediction is used to calculate a volumetric flight challenge scalar field based on critical flow parameters and proximity to structures. A safe, flow-informed flight trajectory is then identified through a cost-minimizing pathfinder. The complete system is demonstrated experimentally through flight tests of a micro aerial vehicle through a model urban geometry placed in a large fan-array wind tunnel. Comparing this approach to trajectories generated without knowledge of the wind field, we find the flow-informed approach reduces undesired vehicle displacement and improves flight stability. This work is among the first practical demonstrations of safe, wind-aware methodologies for advanced air mobility in urban environments.

Abstract

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