Learned Elevation Models as a Lightweight Alternative to LiDAR for Radio Environment Map Estimation

• URL: http://arxiv.org/abs/2604.05520v1
• Date: Tue, 07 Apr 2026 07:18:53 GMT
• Title: Learned Elevation Models as a Lightweight Alternative to LiDAR for Radio Environment Map Estimation
• Abstract summary: We propose a two-stage framework that eliminates the need for 3D data at inference time.<n>Across existing CNN-based REM estimation architectures, the proposed approach improves RMSE by up to 7.8% over image-only baselines.
• Score: 4.489167091323233
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Next-generation wireless systems such as 6G operate at higher frequency bands, making signal propagation highly sensitive to environmental factors such as buildings and vege- tation. Accurate Radio Environment Map (REM) estimation is therefore increasingly important for effective network planning and operation. Existing methods, from ray-tracing simulators to deep learning generative models, achieve promising results but require detailed 3D environment data such as LiDAR-derived point clouds, which are costly to acquire, several gigabytes per km2 in size, and quickly outdated in dynamic environments. We propose a two-stage framework that eliminates the need for 3D data at inference time: in the first stage, a learned estimator predicts elevation maps directly from satellite RGB imagery, which are then fed alongside antenna parameters into the REM estimator in the second stage. Across existing CNN- based REM estimation architectures, the proposed approach improves RMSE by up to 7.8% over image-only baselines, while operating on the same input feature space and requiring no 3D data during inference, offering a practical alternative for scalable radio environment modelling.

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