Transfer to Sky: Unveil Low-Altitude Route-Level Radio Maps via Ground Crowdsourced Data

TL;DR

This paper presents a transfer learning approach that uses ground crowdsourced data and simulation to accurately predict low-altitude radio maps for UAV route planning, overcoming data sparsity and domain gaps.

Contribution

It introduces a novel transfer learning framework combining simulation, adversarial alignment, and limited UAV data fine-tuning for radio map prediction.

Findings

Achieves over 50% higher accuracy than existing methods

Effectively bridges ground-aerial data domain gap

Utilizes crowdsourced ground signals as auxiliary supervision

Abstract

The expansion of the low-altitude economy is contingent on reliable cellular connectivity for unmanned aerial vehicles (UAVs). A key challenge in pre-flight planning is predicting communication link quality along proposed and pre-defined routes, a task hampered by sparse measurements that render existing radio map methods ineffective. This paper introduces a transfer learning framework for high-fidelity route-level radio map prediction. Our key insight is to leverage abundant crowdsourced ground signals as auxiliary supervision. To bridge the significant domain gap between ground and aerial data and address spatial sparsity, our framework learns general propagation priors from simulation, performs adversarial alignment of the feature spaces, and is fine-tuned on limited real UAV measurements. Extensive experiments on a real-world dataset from Meituan show that our method achieves over 50% higher accuracy in predicting Route RSRP compared to state-of-the-art baselines.