Canberra Solar Footprint Mapping
Identified building footprints in Canberra equipped with solar panels by training a VGG16 binary classifier on aerial imagery, enhancing renewable energy mapping efforts for urban planning.
Project Type
Research Project
Key Results
Classification Accuracy
Coverage
Urban Planning
To Other Cities
The Challenge
Manual identification of solar panel installations across a city is time-consuming and quickly becomes outdated as new installations occur.
Key challenges included:
- No automated method to detect solar installations
- Manual surveys expensive and quickly outdated
- Varying roof colors and materials complicate detection
- Need for city-wide coverage at building level
- Integration with existing urban planning systems
Urban Planning Context
Understanding solar adoption patterns helps urban planners assess grid capacity needs, identify areas for targeted incentive programs, and track progress toward renewable energy goals.
Renewable Energy
Track solar adoption citywide
Grid Planning
Inform infrastructure decisions
Our Solution
Trained a VGG16 deep learning model on labeled aerial imagery to automatically classify buildings with solar installations. Achieved high accuracy through careful data augmentation and transfer learning techniques.
Model Architecture
Input
Building footprint aerial tiles
VGG16 Base
Pre-trained ImageNet features
Fine-tuning
Custom classification layers
Output
Solar/No-solar classification
Transfer Learning
Leveraged VGG16 pre-trained on ImageNet as base model, fine-tuning final layers for solar detection task.
Data Augmentation
Applied rotation, flipping, and color augmentation to increase training data diversity and model robustness.
GDAL Processing
Used GDAL for efficient aerial imagery processing, tiling, and georeferencing.
Building Footprint Masks
Applied cadastral building footprints as masks to focus classification on individual buildings.
Binary Classification
Simple but effective binary classifier determining presence or absence of solar panels.
Confidence Scoring
Output includes confidence scores for quality filtering and manual review prioritization.
Technology Stack
Project Impact
Technical Achievement
- High classification accuracy on Canberra buildings
- Robust to varying roof materials and conditions
- Efficient processing of city-wide aerial imagery
- Methodology documented for reproducibility
Planning Applications
- Supports renewable energy policy decisions
- Identifies areas for targeted solar programs
- Scalable approach for other Australian cities
- Foundation for ongoing monitoring capability