Skip to content
Repository containing code used to produce computer vision models that can identify infrastructure in publicly available satellite imagery using Google Earth Engine and Tensorflow.
Jupyter Notebook
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
images
LICENSE
README.md
UNET_G4G_2019_Parking.ipynb
UNET_G4G_2019_solar.ipynb

README.md

Computer Vision with Free Satellite Data

This repository contains code used to produce computer vision models that can identify infrastructure in publicly available satellite imagery using Google Earth Engine and Tensorflow.

Parking lots

As part of the Long Island Solar Roadmap, we are testing the ability for computer vision models to automate the detection and delineation of parking lots in NAIP satellite imagery. This analysis uses the Deeplab v3 model with a pre-trained ResNet backbone.

Solar arrays

Ground mounted solar arrays are prominent features on the landscape, and their proliferation can be hard to keep up with. In collaboration with The Nature Conservancy's North Carolina chapter, we trained a computer vision model to detect and delineate solar arrays from Sentinel-2 data. This UNET model can be used to rapidly update the map of solar energy in NC and other states.

App

The outputs are available for inspection interactively through a Google Earth Engine App App image

You can’t perform that action at this time.