Starts: Thursday August 11 at 6 - 7 PM EDT
Satellites can provide detailed snapshots of the Earth’s surface. Here we will take what we learnt in the previous iterations of this Working Group & build an improved landcover classification model to identify the different land classes in Canada.
Prior experience using Python is required for this project. We recommend that user’s are also familiar with scikit-learn, Tensorflow and Numpy for ML-related modelling tasks. No previous remote sensing knowledge is needed for this project.
Top performers of previous cohort!
Fynn Davis | Lead ~ BSc. (Ecology), Dip. (Data Science)
Leah Lourenco | Lead ~ BSc., MSc., Dip (Data Science) & Entrepreneur
Based on the works of … in the first Remote Sensing Working Group
~ Fraser King | Lead PhD.c @ University of Waterloo, Canada
Dr Karen Smith | Advisor ~ Assistant Professor, U of Toronto
Dr Andre Erler | Advisor ~ Senior Climate Scientist at Aquanty
❗ Meeting Link: https://meet.jit.si/LandCoverClassificationRemoteSensingII
❗ Meeting Time: 6:00 PM EDT on Thursdays (starting the 11th of August)
❗ Slack channel: Join here [If issues, drop email@example.com an email]
- Collaborate with peers to get up to speed on, then continue the work completed from the first ML applications for land cover classification using sentinel-2.
- Learn how to access, download and align remotely sensed Earth imagery (Sentinel-2, Sentinel-1 etc.) for industry ML applications.
- Optimize an ensemble of ML classifiers including random forests and convolutional neural networks for land cover classification (using NRCan Land Cover data)
- Minimal goal: Improve the accuracy of ML classifiers developed in ML applications for land cover classification
- Stretch goal: Creation of sufficiently accurate classifier for industry application
Google Colab Essentials → The recommended Virtual Env for Agg Intellect Community Projects.
Project Onboarding Recipes → Resources to bring you quickly to a minimal solution.
Get familiar with domain and previous work. Core Team skimmed out.
Download sample data, perform feature extraction and run baseline model using existing code
Collaborative optimization [playing with classes, features and models for improvement]
- To join the Core Team at the end of session #3 [Be Active to get shortlisted!]: Apply here
- Time commitment: 3-5 hrs a week.
- Even if you don’t make the Core Team, you can still stay involved as a spectator.
- But only the Core Team will be spotlighted at the group’s conclusion.
- This working group will build on skills and techniques created during the first land cover classification working group to develop an accurate and robust classifier that takes as remote sensing data as well as other data sources participants are able to integrate.
- Classification uses NRCan 2015 land cover classes, at a resolution of 60 m. Initial data is based on Sentinel-2 satellite imagery however other sources are encouraged. We have included an example of what the downloaded S2 data will look like from Sentinel-Hub here: https://i.imgur.com/t6blupG.png
- Unlike the previous efforts, this working group is less of a competition and more focused on collaboration. We encourage participants to share code, discoveries and notebooks throughout the project to maximize the progress we can all make in a short period of time.
- If you have any questions about the working group, please let us know. Good luck everyone!
Aggregate Intellect hosts one of the most diverse ML communities in the world. Over the course of the working group
- You’ll get an immersion into that community & walk out with some cool new friends.
- Advance your ML skills by working on real world problems with classification algorithms of increasing sophistication
- Contribute to a study area (land cover classification) which has major impacts to resource management practices, wildlife habitat protection and in advancing our understanding of the Earth’s biophysical systems
- Get spotlighted for your efforts!
Looking forward to meeting everyone in our working group! Please feel free to reach out if you have any questions about the planned project.