1. Using aerial images for urban planning
Meenal Pore

2. Results

3. Results
The method detects too many small rooftops. This is due to repairs on tin roofs which are registered as separate roofs due to the edges created by having a new piece of roofing next to the older rusted rooftops
The area of rooftops compared to groundtruth data from Open Street Map building polygons

4. Methodology Google Map images (Quickbird)
Data sources
LiDAR, Digital Surface Model (DSM)
Google Map images (Quickbird)
Drone imagery
Building extraction
Building classification (rooftop type and size)
Other features(slum, vehicles, road, river, vegetation)
Wealth index Approximation
Image segmentation (Drone, Satellite, LiDAR)
Image feature extraction
Image classification

5. Scaling feature extraction from aerial images
Ground truth data
from manual tracing of images and ground surveys (Open Street Map)
Drone data
Used to extract rooftops over a larger area
WARD
LEVEL
Roof map from drone data
Generate map of rooftops for a larger area than can be manually mapped
Satellite data
Used to extract rooftops from low resolution data
CITY LEVEL
Roof map from satellite data
Used to extract rooftops from low resolution data
COUNTRY LEVEL