Map-Based Roof Detection
Map-Based Roof Detection is a solar design technology that automatically identifies roof outlines, edges, tilt planes, and usable installation areas directly from satellite maps, aerial images, or high-resolution GIS data. It eliminates the need for manual tracing, speeds up the design process, and improves layout accuracy during the early stages of solar system planning.
In solar workflows, Map-Based Roof Detection helps designers instantly generate roof boundaries, highlight obstructions, apply code setbacks, and prepare a surface for Auto-Design or manual layout. When integrated into platforms like Solar Designing, it dramatically reduces the time required to build a reliable starting model.
This feature is especially valuable for high-volume residential design, remote site analysis, and sales teams that need fast, accurate visuals for proposals.
Key Takeaways
- Map-Based Roof Detection automatically identifies roof geometry from imagery.
- It eliminates manual tracing and speeds up the solar design workflow.
- Highly valuable for residential, commercial, and remote solar design operations.
- Enables accurate Auto-Design, stringing, shading analysis, and energy modeling.
- Provides the foundation for clean, buildable, code-compliant solar layouts.
What Is Map-Based Roof Detection?
Map-Based Roof Detection is an automated process that interprets roof geometry from map imagery. Instead of a designer manually outlining a roof plane, the software:
- Reads aerial or satellite imagery
- Detects edges, ridgelines, hips, and valleys
- Identifies usable planes
- Flags obstructions
- Creates roof polygons automatically
It is the first step in many automated design workflows, ensuring Solar PV systems begin with a clean, accurate model before panel placement or stringing begins.
Related concepts include Array Boundary Tool, 3D Solar Modeling, and Auto-Design.
How Map-Based Roof Detection Works
While implementations vary, most systems follow these steps:
1. Map Imagery Is Loaded
The tool pulls satellite, aerial, or GIS imagery of the roof surface.
2. AI Analyzes Roof Architecture
Algorithms identify lines, angles, shapes, and shadows to determine:
- Roof edges
- Ridges & hips
- Tilt direction
- Surface breaks
3. Roof Polygon Is Automatically Generated
A complete outline is drawn, creating the boundaries for panel layout.
4. Setbacks and Fire Pathways Are Applied
The system can automatically apply AHJ and NEC setbacks—see AHJ Compliance.
5. Obstructions Are Detected or Added
Chimneys, vents, skylights, and equipment are excluded.
6. The Surface Is Prepared for Auto-Design
At this point, the layout engine (see Solar Layout Optimization) can start placing modules.
Types / Variants of Map-Based Roof Detection
1. Basic Edge Detection
Uses contrast differences in imagery to detect roof outlines.
2. Advanced AI Roof Recognition
Machine learning models identify accurate roof planes even in noisy or low-resolution images.
3. Tilt & Orientation Detection
The system detects pitch direction and estimate tilt angles.
4. Multi-Plane Roof Detection
Generates accurate polygons for complex roofs with:
- Dormers
- Multi-level roofs
- Overhangs
- Irregular shapes
5. Obstruction Detection
Some systems automatically detect skylights, chimneys, vents, and shaded zones.
How It’s Measured
Map-Based Roof Detection accuracy is evaluated using:
Edge Accuracy (%)
How closely the detection matches real roof outlines.
Plane Detection Rate
Percentage of roof facets correctly identified.
Obstruction Mapping Accuracy
How reliably obstructions are detected.
Usable Area Coverage
How well the tool defines actual installation zones.
Time-to-Detection
Speed of processing imagery.
Typical Values / Ranges
Residential Roof Accuracy
- 85–95% accuracy from satellite imagery
- 95–99% accuracy from aerial photography
Commercial Roofs
- Highly accurate (large, flat surfaces)
- Multi-plane accuracy varies with shadows and roof complexity
Detection Speed
- 0.5–5 seconds per roof (depending on system)
Practical Guidance for Solar Designers & Installers
1. Always verify detected boundaries
Especially important for irregular roofs or heavy shading.
2. Combine roof detection with shading analysis
Use Shadow Analysis to confirm panel performance on each detected plane.
3. Use the detection output as the base for Auto-Design
Tools like SurgePV can instantly convert roof polygons into a full layout—see Auto-Design.
4. Adjust setbacks based on AHJ requirements
Use AHJ Compliance before finalizing the layout.
5. Cross-check with field measurements for final engineering
A digital model is extremely accurate, but final verification on-site is essential for installation.
6. Use high-resolution imagery where possible
Higher-quality imagery improves detection precision significantly.
Real-World Examples
1. Residential Solar Project
A designer uploads satellite imagery into SurgePV.
The tool automatically detects the roof outline, applies setbacks, excludes a chimney, and prepares a layout zone—all in under 3 seconds.
2. Commercial Building
A large warehouse roof is detected with precise polygons showing parapets and edge offsets.
This makes it easy to lay out a ballasted system with safe O&M walkways.
3. Multi-Family Building
A complex multi-plane roof is auto-detected, identifying different tilt angles and orientations.
The software prepares each plane for individual array placement.
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