EXTRACTION OF MAIN URBAN ROADS FROM HIGH RESOLUTION SATELLITE IMAGES

Abstract

This paper focuses on automatic road extraction in urban areas from high resolution satellite
images. We propose a new approach based on machine learning. First, many features reflecting road
characteristics are extracted, which consist of the ratio of bright regions, the direction consistency of edges and
local binary patterns. Then these features are input into a learning container, and AdaBoost is adopted to train
classifiers and select most effective features. Finally, roads are detected with a sliding window by using the
learning results and validated by combining the road connectivity. Experimental results on real Quickbird
images demonstrate the effectiveness and robustness of the proposed method. "This paper presents a novel
approach for automatically extracting main urban roads from high-resolution satellite images using machine
learning techniques. Leveraging convolutional neural networks (CNNs) and semantic segmentation, our method
achieves accurate road detection with minimal manual intervention. We demonstrate the effectiveness of our
approach on diverse urban landscapes, achieving high precision and recall rates. The proposed method offers
valuable insights for urban planning, transportation management, and disaster response applications."