Roof Structure Extraction from Remote Sensing Images
H.Y. Cheng (TU Delft - Architecture and the Built Environment)
L. Nan – Mentor (TU Delft - Urban Data Science)
Weixiao Gao – Mentor (TU Delft - Urban Data Science)
A. Rafiee – Graduation committee member (TU Delft - Digital Technologies)
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Abstract
This thesis presents a method for extracting structured roof surfaces from remote sensing images. It achieved this by combining semantic segmentation with polygon-based refinement, which allows rooftop boundaries to be described more accurately using line and shape information. The method includes three main stages: (1) using an instance segmentation model to detect and classify rooftop areas; (2) generating polygonal candidates for plannar roof regions based on detected line features; and (3) optimizing label assignments through a Markov Random Field (MRF) model, which integrates prediction confidence with the spatial relationships between polygons. Experiments on benchmark datasets show that this approach improves the accuracy and consistency of rooftop segmentation while reducing incorrect detections. The system is modular and flexible, making it suitable for applications that require reliable roof structure analysis in urban environments.