Road Segmentation using U-Net architecture
Norel Ya Qine Abderrahim, Abderrahim Saadane, Rida Azmi
Abstract
The detection of objects has become a critical step to update ground cover information, and the availability of very high-resolution satellite images made us discover new classification methods that give us more details such as pixel classification. This study aims to explore the potential and performance of machine learning algorithms in poor urban conditions in order to show the power of the deep neural networks to detect objects and, more precisely, to detect roads. We propose a U-net architecture for road extraction from Massachusetts dataset. The results have been compared with different automatic classification learning algorithms. The results of the classification using U-net showed a high accuracy of 97.7%, more precise than all the other models, which is why it is the best method to solve classification tasks for objects detection in large-scale datasets.