Automated Roof Plan Extraction and Dimensioning Using Artificial Intelligence

A computer-implemented method to determine roof dimensions, comprising:

• receiving a plurality of labeled rooftop image datasets, wherein each dataset includes images including photographs, aerial imagery, or satellite imagery of a roof structure and annotated geometric measurements of roof edges, slopes, and obstructions;
• training an AI model using supervised machine learning to correlate pixel patterns in the imagery with the annotated geometric measurements, wherein the training optimizes a loss function evaluating accuracy of predicted roof edges, facet lengths, and slope angles;
• processing an unlabeled rooftop image through the trained AI model to:
• detect perimeter boundaries of the roof structure, segment distinct roof facets, calculate planar angles between adjacent facets;
• and
• generating a dimensioned roof diagram specifying linear measurements for each facet edge and angular measurements for intersecting planes.

A computer-implemented method for automated solar panel layout generation comprising:

• retrieving satellite imagery of a target geographic location;
• processing said satellite imagery through a multi-task convolutional neural network to detect roof structural elements, said processing comprising:
• extracting line segments from said imagery using Line Segment Detection (LSD) with multi-scale analysis;
• identifying roof vertices through heatmap regression of intersection points between detected line segments;
• constructing a weighted graph representation of roof topology where nodes represent vertices and edges represent structural connections weighted by Euclidean distances;
• executing obstacle detection through oriented bounding box prediction using a deep learning model trained on architectural documents, said detection comprising:
• differential processing of black-and-white versus color-enhanced document versions to isolate annotations;
• coordinate matching between obstacle positions and satellite imagery through scale normalization and intersection-over-union (IoU) calculations;
• generating solar panel layout parameters through constrained optimization that evaluates:
• azimuth-adjusted panel orientations relative to roof slope vectors;
• minimum inter-panel spacing compliant with fire safety regulations;
• and
• shading projections based on historical sun path trajectories;
• outputting a digital installation blueprint specifying panel coordinates, tilt angles, and interconnection pathways.