Effect of dataset distribution on automatic road extraction in very high-resolution orthophoto using DeepLab V3+ - Sussi, Sussi; Husni, Emir; Siburian, Arthur; Yusuf, Rahadian; Budi Harto, Agung; Suwardhi, Deni (IAES International Journal of Artificial Intelligence (IJ-AI), 2024)
Road extraction is one of the stages in the map-making process, which has been done manually, takes a long time, and costs a lot. Deep Learning is used to speed up the road extraction process by performing binary semantic segmentation on the image. We propose DeepLab V3+ to produce road extraction from very high-resolution orthophoto for Indonesia study area, which poses many challenges, such as road obstruction by trees, clouds, building shadows, dense traffic, and similarities to rivers and rice fields. We compared the distribution of datasets to obtain the optimal performance of the DeepLab V3+ model in relation to the dataset. The results showed that dataset ratio of 75:10:15 resulted in mean Intersection Over Union (mIoU) of 0.92 and Dice Loss of 0.042. Visually, the results of road extraction are more accurate when compared to the results obtained from different distributions of the dataset.