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.

License

Notifications You must be signed in to change notification settings

limiao766/Satellite_ComputerVision

Folders and files

NameName
Last commit message
Last commit date

Latest commit

63bfde0 · Jan 3, 2021

History

40 Commits
Jan 3, 2021
Dec 6, 2019
Jan 3, 2021
Jan 3, 2021
Nov 14, 2019
Dec 6, 2019
Mar 25, 2020
Dec 4, 2020
Jan 3, 2021
Jan 3, 2021

Repository files navigation

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

About

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

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Jupyter Notebook 64.0%
  • Python 36.0%