Identifying Area of Rooftops in Denver, CO, for Solar Policy
Independent capstone project to Springboard's Data Science program.
Given satellite imagery of Denver, we can train a convolutional neural network to correctly identify rooftop area.
- Label 75+ images of rooftops with the tool provided generously by Dr. Tony Szedlak
- Build a data generator to apply random cropping of size (256,256) of the (800,800) images
- Build a CNN
- Train the CNN given the generated data
- Fine tune the model’s hyperparameters to increase model accuracy
- Predict given all 1,800+ satellite images for Denver, CO
The City and County of Denver generously provides open source data via https://www.denvergov.org/opendata. For this project, I am using their 2004 dataset of satellite imagery of Denver County. These data consist of almost 2,500 JP2 files.
From the Open Data website:
- Color, 6 inch resolution aerial photography for the City and County of Denver acquired in 2004. True-ortho correction is applied to all structures 4 stories and above in designated areas including all bridges and highway overpasses.
- Coverage includes a 359 square mile area encompassing the City and County of Denver, City of Glendale, City of Littleton, and the Denver Water Service Area. This project does not include DIA.
- Spatial reference is NAD 1983 HARN StatePlane Colorado Central FIPS 0502.
- For the benefit of the analysis, I converted these JP2 files into JPEGs using XnConverter for Linux.
The exploratory data analysis of the 1,000+ image dataset, provided by Denver Open Data
The code used for the data generator The base of my data generator takes an (1000, 1000) px image and it’s corresponding mask and:
- randomly rotates the images between 0 and 360 degrees
- randomly crops the images to size (256,256).
- randomly flips the images along the vertical axis
- randomly rotates the images an additional 90 degrees
The model’s architecture is based off of the U-NET architecture U-NET is a artificial neural network based on ConvNets that is widely used for image segmentation work. The U-NET architecture which features a set of encoding layers then decoding layers. Each encoding chunk, as shown by the set of three lines in each row below, has a convolution block followed by a maxpool downsampling. The decoding section features upsampling and concatenation followed by more convolution.
The modified model architecture I chose for this project is based off of Fabio Sancinetti’s U-NET ConvNet for CT-Scan segmentation.