Anthill Inside 2017

On theory and concepts in Machine Learning, Deep Learning and Artificial Intelligence. Formerly Deep Learning Conf.

AB

Arnab Biswas

@arnabbiswas

Identifying Urban Makeshift Communities using satellite imagery and geo-coded data

Submitted Jul 10, 2017

The aim of this talk is to provide a comprehensive description of the experimentation & explorations done by DataKind-Bangalore to identify non-permanent urban poor communities in Bangalore using Machine Learning (Transfer learning) with satellite imagery and geo-coded data for Pollinate Energy.

Pollinate Energy is a social business bringing life changing products such as solar lanterns, improved cook stoves, solar fans and water filters to urban makeshift communities. They identify communities by manually scouring through google maps and then visiting door-to-door. This is an expensive and time consuming process. Volunteers at DataKind Bangalore are working to help Pollinate Energy reduce man-hours and resources by suggesting urban makeshift communities using recent Computer Vision & Deep Learning Techniques.

Outline

This session will be organized as:

  • Problem Description
  • Data Gathering
    • Challenges with data
  • Various Modeling Approaches
    • Satellite Imaging Approach
      • Data Sources
      • Computer Vision Approach (No labeled data)
      • Supervised Learning Approach
      • Transfer Learning Approach
    • Geo-Coded Data based Approach
      • Data Sources
      • Undersampling Approach
      • Stratified sampling Approach
  • Closing Thoughts/Learning

Requirements

Basic understanding of Machine Learning and Computer Vision terminology

Speaker bio

I volunteer with DataKind Bangalore. Professionally, I am working at Infrrd AI on Machine Learning.

Links

Slides

https://docs.google.com/presentation/d/1cmQiE1_r6xbTZnPsCNiMd7gKn138eRJifaW5DYe3c0w/edit#slide=id.g1f94036a34_0_1

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