## Vegetation management using deep learning on satellite images and LiDAR data

Extreme weather events and poorly managed forests are causing unprecedented wildfires globally. Every year, utility companies inspect thousands of miles of transmission lines in search of vegetation at risk of contacting lines and causing wildfires. Leveraging deep learning on satellite images and LiDAR data using AWS machine learning services can identify areas of risk. Utility companies can use the identified anomalies to monitor vegetation and proactively intervene to prevent wildfires and protect critical infrastructure. In this workshop, learn how to use Amazon SageMaker to process satellite images and LiDAR data and identify vegetation risks using deep learning. 

## Getting started

This repository contains the files and data required for the AWS Vegetation Management Workshop.

## Problem description

This workshop will showcase how we can use deep learning on satellite images and LiDAR data to identify trees. The trees once identified can be combined with the information of transmission lines to do preventitive vegetation management on trees that are near the transmission lines. The workshop uses the concepts of transfer learning and semantic segmentation. 

## Security

See [CONTRIBUTING](CONTRIBUTING.md#security-issue-notifications) for more information.

## License Summary

The documentation is made available under the Creative Commons Attribution-ShareAlike 4.0 International License. See the LICENSE file.

The sample code within this documentation is made available under the MIT-0 license. See the LICENSE-SAMPLECODE file.