Estimating Roadside Vegetation Height for Maintenance Decision-Making

Abstract

The upkeep of roadside vegetation is essential for ensuring the safety of both motorists and pedestrians. However, identifying the necessity for such maintenance is frequently a time-consuming and costly process, with a high potential for errors in annotation. This work therefore proposes the development of a solution capable of estimating the height of vegetation along roadsides in an automated manner. A machine learning model was developed and evaluated using a dataset of manually annotated data. The model employs a convolutional neural network architecture, adapted for the task of classifying vegetation heights. The results demonstrate that the model is able to detect the different vegetation height classes with low error rates, indicating its potential for automating the decision-making process for mowing vegetation. This study contributes to the advancement of road monitoring techniques, providing greater operational efficiency and reducing costs for road maintenance.