Spotting Potential: Classifying Prime Areas for Renewable Wind Energy Farms with Computer Vision (Part 1)
A Brief Introduction on Leveraging Edge Devices and Embedded AI to Track the U.S. Wind Energy Footprint
Published in
3 min readAug 22, 2020
Github Repo: https://github.com/codeamt/WindFarmSpotter
This is a series:
- Part 1: A Brief Introduction on Leveraging Edge Devices and Embedded AI to Track the U.S.Wind Energy Footprint (You are Here)
- Part 2: An Approach to Satelite Arial Image Data Generation and Automation with Google Earth Engine, Basemap, and Colab
- Part 3: Experimenting with Memory, Efficiency, and Scaling Input Resolution using a Fast.ai v3 Training Pipeline
- Part 4: Running Inference Tests: Swift-Python Interoperability, and Hardware Acceleration
- Part 5: Spinning Up Inference APIs — Flask (Just Python) v. Kitura (Python & Swift)
- Part 6: Containerizing Deployments for Web, ARMv8/Jetson NVIDIA Series, and SWAP Hardware Platforms
Recently, I completed a data science and software engineering project as part of a hiring pipeline.
