الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
The advance in technology over the past years has made it possible to launch different types of satellites equipped with many sensors to orbit the earth. This has led to the availability of tons of raw data that could be used in many applications such as precision agriculture, city remote sensing, environmental monitoring, population analysis and road network extraction.
The constant update in road Networks requires a massive amount of work. The road networks have a complicated structure because roads in the same area may vary in width and paving materials.
The problem of determining roads in complicated intersections, bridges, and tunnels is a challenge even for human operators. Difficulties could include the nature of satellite images, trees occluding roads, and shadows cast by buildings and clouds.
A framework for automatic road network construction is proposed along with a comprehensive overview of the state of art deep learning methods applied to the field of study.
The study problem was regarded as a pixel based classification one. Different techniques were proposed to extract the road network map. Next the output map is thresholded and skeletonized as a step towards converting the generated map into a graph where roads are represented as edges and road intersections are represented as nodes. This graph could be used in many routing applications.
The results were evaluated using pixel based and graph based evaluation metrics on a dataset of seven different cities spanning four continents. At the end the thesis is concluded with a discussion of the possible future research in the field.