A Comparative Study of Deep Semantic Segmentation and UAV-Based Multispectral Imaging for Enhanced Roadside Vegetation Composition Assessment
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-consu...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/12/1991 |
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| Summary: | 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 <i>R</i><sup>2</sup> of 0.996, an <i>MAE</i> of 1.225, an <i>RMSE</i> of 1.761, and an <i>MAPE</i> of 3.003% and outperformed the top VI method of SAVI, which achieved an <i>R</i><sup>2</sup> of 0.491, an <i>MAE</i> of 20.830, an <i>RMSE</i> of 23.473, and an <i>MAPE</i> 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. |
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| ISSN: | 2072-4292 |