HVAS: Detection of Vegetation Height near Electric Transmission Lines Using Deep Learning and Satellite Images

Abstract. Vegetation encroachment near power transmission lines poses risks such as outages, fires, and increased maintenance costs. This study presents a method for estimating vegetation height using public satellite imagery and convolutional neural networks (CNNs). The approach involves segmenting dense vegetation areas, calculating height, and a new method for generating georeferenced alerts for risk analysis. Height estimates using Sentinel-2 and GEDI data showed a root mean square error (RMSE) of 7.7 meters and a mean absolute error (MAE) of 5 meters compared to high-resolution LiDAR data. The results demonstrate the originality of this study by identifying risk regions associated with the presence of vegetation near to power transmission lines, contributing to the improvement of vegetation management using public data in the electricity sector.