Road Detection and Segmentation from Aerial Images using CNN

Abstract

This project presents a deep learning-based 
approach for automatic road detection and 
segmentation from high-resolution aerial images. A 
Convolutional Neural Network (CNN) architecture 
is designed and trained to identify road networks 
from complex aerial scenes. The proposed method 
leverages spatial context and multiscale feature 
extraction to accurately detect and segment roads.  
The CNN model is trained on a large dataset of aerial 
images with annotated road masks. Experimental 
results demonstrate superior performance compared 
to traditional computer vision techniques. The 
proposed approach enables efficient and accurate 
road mapping, facilitating applications in urban 
planning, autonomous vehicles, and geographic 
information systems.