Detecting Deforestation from Satellite Images

Deep Learning

Project Description

A Deep Learning approach to detecting deforestation risk, using satellite images and a deep learning model. We relied on Planet imagery from two Kaggle datasets (one from the Amazon rainforest and another on oil palm plantations in Borneo) and trained a ResNet model using FastAI.

Task and Results

  • Trained a ResNet50 model on FastAI, achieving 95.6% validation accuracy and results comparable to a Kaggle competition’s top of the leaderboard.
  • Developed and deployed a dashboard using Streamlit, which enables not only for interacting with our datasets and modeling results but also to test new images.
  • Set up a data infrastructure in Google Cloud, where we stored our labels, images and data collected from users.
  • Analyzed our model’s performance on out-of-domain data.
  • Structured and maintained our group’s project management through a Notion workspace.
  • Published a Medium article on Towards Data Science.
  • Got the web app featured at the top of Streamlit’s official weekly roundup.
  • Highlighted on PyCoders weekly newsletter.

Short Video

Detailed Report

Full Stack Deep Learning - Final Project Submission

Full Stack Deep Learning - Certificate

Karthik Bhaskar
Karthik Bhaskar
Machine Learning Researcher | Data Scientist | Software Engineer

Machine Learning Researcher | Software Engineer | Vector Institute | University of Toronto | University Health Network