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Efficient analysis of ground cover types is vital during the investigative stages of civil construction projects. Traditional surveying methods, although accurate, are often expensive, time-consuming, and cumbersome, especially for large-scale infrastructure developments. This study explores the way of enhancing the automation of ground cover classification using high-resolution UAV imagery and machine learning. Various classification algorithms, including Support Vector Machine (SVM) and k-Nearest Neighbour (kNN), were tested. Post-processing technique such as multi-image averaging was employed to mitigate the effects of shadows and transient objects like vehicles, resulting in improved classification accuracy. Accuracy assessments comparing UAV-based automated classification results with manually classified datasets and feature survey data demonstrate the potential of this approach to streamline data capture during the early stages of civil construction projects. The use of UAVs not only reduces costs but also accelerates the data collection process. However, challenges such as noise, shadows, and misclassifications persist, indicating the need for further refinement and integration of additional data sources like multispectral imagery and UAV LiDAR data. The findings suggest that automated ground cover classification can support quicker decision-making in civil construction, improving the efficiency of future data collection and planning processes. The SVM algorithm proved to be the more effective method, consistently achieving higher accuracy in classifying the raster datasets into the designated classes. This study contributes to improved decision-making in construction planning and highlights the potential for further advancements in automated classification technologies in construction environments.
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