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

TL;DR

This paper introduces a two-stage framework that predicts elevation maps from satellite images to estimate radio environment maps without needing costly 3D data, improving scalability and practicality.

Contribution

The novel approach predicts elevation maps from satellite imagery to enhance REM estimation, eliminating the need for 3D environment data during inference.

Findings

Improves RMSE by up to 7.8% over image-only baselines.

Operates without 3D data during inference, reducing data acquisition costs.

Enhances existing CNN-based REM estimation architectures.

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data such as LiDAR-derived point clouds, which are costly to acquire, several gigabytes per km2 in size, and quickly outdated in dynamic environments.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.