Training models using Satellite imagery on Amazon Rekognition Custom Labels

How to access Sentinel-2 imagery

  • Use one of the browsers mentioned on the page https://registry.opendata.aws/sentinel-2/
  • Download imagery from the Amazon S3 bucket directly. Amazon S3 is a storage service that provides scalable and secure access for downloading and uploading data.

Find Sentinel-2 scene

Download image bands

Prepare False Color Sentinel-2 image

How to train and run Amazon Rekognition model

Create and label Dataset

  1. Create dataset
  2. Choose “Upload images from your computer”
  3. On the dataset page click “Add images”
  4. On the pop-up window, click Choose Files and choose files from your computer. Then click “Upload images”
  5. Create labels “active field”, “semi-active field”, “non-active field”
  6. Click “Start labeling”, choose images, and then click “Draw bounding box”
  7. On the new page, you can now choose labels and then draw rectangles for each label. After you’ve finished labeling you can switch to a different image or click “Done”.

Train and run the model

  1. On the projects page click “Create Project”
  2. On the project page choose “Train new model”
  3. Choose the dataset which we just created and then choose “Split training dataset” for the test dataset. Then click “Train”.
  4. Once the model is trained you can start making predictions.

Visualize the result

Conclusion

--

--

--

I'm a senior machine learning engineer at Instrumental, where I work on analytical models for the manufacturing industry, and AWS Machine Learning Hero.

Love podcasts or audiobooks? Learn on the go with our new app.

Recommended from Medium

Explaining machine learning models to business users using BigQueryML and Google Data Studio

Machine Learning With R Technology — Chapter 02

On the Road to Detect Financial Crime — Isolation Forest Robustness

How to create great Machine Learning solutions

Deep Architectures

NeuroNuggets: ACL in Review I

Data Pre-processing in Machine Learning Part 3 with encoding categorical data

Improving sample classification in metabolomics datasets (part 1: class imbalance, resampling)

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Rustem Feyzkhanov

Rustem Feyzkhanov

I'm a senior machine learning engineer at Instrumental, where I work on analytical models for the manufacturing industry, and AWS Machine Learning Hero.

More from Medium

Graph Databases and Object Graph Mapping with neo4j and python

Containerizing Machine Learning Models using Docker

Can ML predict where my cat is now — part 2

Anomaly Detection with Richard