Automated Rooftop Detection & Segmentation
Manual inspection of rooftops from aerial and satellite imagery is time-consuming, expensive, and prone to human error. For industries like solar energy and real estate, inaccurate measurements lead to significant operational inefficiencies:
Developed a custom computer vision pipeline that automates the detection, segmentation, and measurement of residential rooftop structures from high-resolution aerial views.
Core technical implementation:
Trained custom segmentation models on curated datasets of high-resolution aerial and satellite imagery. Utilized ensemble methods to handle diverse architectural styles and lighting conditions.
Implemented automated orthorectification and coordinate mapping to ensure spatial accuracy across varying camera angles and elevations.
Developed algorithms to calculate roof area, pitch estimations, and solar potential based on segmented polygons and shadows.
Integrated address database matching with visual property ID recognition to automate the entire assessment workflow from input to report.
Precision in identifying rooftop boundaries
Compared to manual inspection methods
Property assessments processed in batch
High-precision area and boundary calculations
State-of-the-art architectures for pixel-level accuracy in complex urban and suburban environments.
Automated labeling system that connects visual detection to institutional property databases.
Robust detection algorithms that maintain accuracy across diverse drone and satellite camera perspectives.
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