A Comparative Study of Deep Semantic Segmentation and UAV-Based Multispectral Imaging for Enhanced Roadside Vegetation Composition Assessment

Abstract

Roadside vegetation composition assessment is essential to maintain ecological stability, control invasive species, and ensure the adoption of environmental regulations in areas surrounding active roadside construction zones. Traditional monitoring methods involving visual inspections are time-consuming, labor-intensive, and not scalable. Remote sensing offers a valuable alternative to automating large-scale vegetation assessment tasks efficiently. The study compares the performance of proximal RGB imagery processed using deep learning (DL) techniques against the vegetation indices (VIs) extracted at higher altitudes, establishing a foundation to use the prior in performing vegetation analysis using unmanned aerial vehicles (UAVs) for broader scalability. A pixel-wise annotated dataset for eight roadside vegetation species was curated to evaluate the performance of multiple semantic segmentation models in this context. The best-performing MAnet DL achieved a mean intersection over union of 0.90, highlighting the model’s capability in composition assessment tasks. Additionally, in predicting the vegetation cover—the DL model achieved an R2 of 0.996, an MAE of 1.225, an RMSE of 1.761, and an MAPE of 3.003% and outperformed the top VI method of SAVI, which achieved an R2 of 0.491, an MAE of 20.830, an RMSE of 23.473, and an MAPE of 59.057%. The strong performance of DL models on proximal RGB imagery underscores the potential of UAV-mounted high-resolution RGB sensors for automated roadside vegetation monitoring and management tasks at construction sites.