Paper
6 September 2019 Road network mapping from aerial images
Author Affiliations +
Abstract
Building and expansion of an efficient transportation network are essential for urban city advancement. However, tracking road development in an area is not an easy task as city planners do not always have access to credible information. A road network mapping framework is proposed which uses a random forest model for pixel-wise road segmentation. Road detection is followed by computer vision post-processing steps including Connected Component Analysis (CCA) and Hough Lines method for network extraction from high-resolution aerial images. The custom dataset used consists of images collected from an urban settlement in India.
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Suchit Jain, Rohan Mittal, Prakamya Mishra, and Aakash Sinha "Road network mapping from aerial images", Proc. SPIE 11139, Applications of Machine Learning, 1113917 (6 September 2019); https://doi.org/10.1117/12.2529005
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KEYWORDS
Roads

Image segmentation

Simulation of CCA and DLA aggregates

Hough transforms

Data modeling

Image processing

Image resolution

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