A Review of Vegetation Encroachment Detection in Power Transmission Lines using Optical Sensing Satellite Imagery

TL;DR

This paper reviews satellite imagery techniques for detecting vegetation encroachment on power lines, highlighting the potential of machine learning and deep learning to improve accuracy and flexibility in monitoring.

Contribution

It categorizes existing satellite-based vegetation detection methods and explores the integration of machine learning and deep learning for enhanced monitoring.

Findings

Current methods rely on manual threshold setting, making detection static.

Satellite imagery offers high coverage at low cost for vegetation monitoring.

ML and DL algorithms can significantly improve detection accuracy and adaptability.

Abstract

Vegetation encroachment in power transmission lines can cause outages, which may result in severe impact on economic of power utilities companies as well as the consumer. Vegetation detection and monitoring along the power line corridor right-of-way (ROW) are implemented to protect power transmission lines from vegetation penetration. There were various methods used to monitor the vegetation penetration, however, most of them were too expensive and time consuming. Satellite images can play a major role in vegetation monitoring, because it can cover high spatial area with relatively low cost. In this paper, the current techniques used to detect the vegetation encroachment using satellite images are reviewed and categorized into four sectors; Vegetation Index based method, object-based detection method, stereo matching based and other current techniques. However, the current methods depend usually on setting manually serval threshold values and parameters which make the detection process very static. Machine Learning (ML) and deep learning (DL) algorithms can provide a very high accuracy with flexibility in the detection process. Hence, in addition to review the current technique of vegetation penetration monitoring in power transmission, the potential of using Machine Learning based algorithms are also included.