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Predicting the Quality of Life from Satellite Imagery
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This was presented at O'Reilly AI Conference, London on Oct 17th 2019, by Soumya Ranjan and Ganes Kesari. Session URL: https://conferences.oreilly.com/artificial-intelligence/ai-eu/public/schedule/detail/78143 Description In large developing countries like India, it’s tough to base policy decisions on data. Of a population of 1.3 billion, 70% reside in rural areas with minimal digital trails. Large governmental initiatives such as the national census are collected once every 10 years. Despite the best of intentions, they aren’t comprehensive and quickly get out of sync with reality. Ganes Kesari and Soumya Ranjan explore how satellite imagery offers an alternate ground truth that’s accurate at high resolution, available across periods as a time series, easily accessible, and is relatively inexpensive. While this is a rich source of visual information, the challenge has been in processing images and generating useful insights. The advances in deep learning have helped solve this last hurdle, placing enormous power in people’s hands and paving the way for socioeconomic data analytics. This information helps answer basic questions about India’s patterns by applying deep learning on satellite imagery, enriched with census data, and using advanced analytics approaches. It can show the extent and pace of urbanization over time, provide a comparison of census-based indicators of poverty and visual indicators of development from aerial imagery, and identify anomalies observed in census-measured factors such as literacy, employment, and healthcare when viewed from the lens of satellite imagery. Ganes and Soumya explain the inspiration of their work, Stefano Ermon et al, who used night light as a proxy to detect poverty in Africa. Deep learning requires a lot of labeled data; in this case, they used transfer learning to get over the initial hurdle. Planet.com releases some open data, and they used SpaceNet data for training the model’s weights, which was then adapted to the specific satellite imagery of villages and cities in India. Three networks were trained to do three different tasks, extract building footprints, extract land use and road patterns, and extract night lighting patterns. The output of these three networks was structured and combined with the district-level census data to be used as an input for the socioeconomic analytics. Statistical techniques and machine learning algorithms were shed light on demographic insights that can drive potential policy decisions. Prerequisite knowledge A basic understanding of data and its application What you'll learn Gain approaches to process satellite imagery using deep learning Learn novel applications of insights from aerial imagery by enriching them with conventional data Understand the implications of demographic insights for policy decisions.
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Predicting the Quality of Life from Satellite Imagery
- 1 Quality of Living Bhutan Best Cities Cost of Living GDP Ranking Most livable cities Healthy nations Infra rankings Favorite cities Best student citiesSafe cities Expensive cities Corrupt cities Cleanest cities Congested cities The Business of Rankings
- 2 Predicting the Quality of Life from Satellite Imagery
- 3
- 4 Ganes Kesari Co-founder & Head of Analytics “Simplify Data Science for all” 100+ ClientsInsights as Stories @kesaritweets Help business start, apply and adopt Data Science Soumya Ranjan Senior Data Scientist @srmsoumya Gramener AI Labs Introduction
- 5 Live Poll: A quick check on familiarity
- 6 Income Housing Health Education Environment SecurityInfrastructure Governance Culture Work-life balance Let’s say you are relocating to a new country… ..what factors are most important to you?
- 7 INFRA ISSUES CORRUPTION COST NON COOPERATION How are they currently measured: National Census Challenges 1870 Census Illustration, Library of Congress/LC-USZ62-93675 Census over the years
- 8 LACKS GRANULAR DATANON REPRESENTATIVE How are they currently measured: Sample Surveys Challenges
- 9 Any better use for the GPS maps.. ..beyond our daily commute and pizza deliveries? Alternatives?
- 10 Daytime satellite imagery Night-time satellite imagery Inspiring work by Stanford on Predicting Poverty http://sustain.stanford.edu/predicting-poverty
- 11 Model extracts features.. ..and predicts poverty in Africa Estimate per-capita consumption expenditure http://sustain.stanford.edu/predicting-poverty
- 12Icons by Freepik on Flaticon Our work on predicting the Quality of Life
- 13 Data Pre- process Model Results Let’s dive into our Work
- 14 Live Poll: Do you think this will work?
- 15 Getting the Data 1
- 16 Mapping the Earth Constellation of Satellites Image by Naideh Bremer
- 17 Some sources of Satellite Imagery Google Earth Engine Earth on AWS Planet Google Earth Engine | Earth on AWS | Planet
- 18 MODIS 250m daily Air & Climate NOAA NCEP, OMI, ... Terrain & Land Cover Landsat & Sentinel 10-30m, 14-day Google Earth Engine (GEE) Catalogue GEE Catalog: Karin Tuxen-Bettman
- 19 • 1972 - Present • 30m Resolution • 5 visible + NIR • 2 SWIR + 2 TIR • 16 days global cov. Landsat 4. 5. 7. 8.
- 20 • 2014 - Present • 10m Resolution • 13 Spectral bands • 10 days global cov. Sentinel 1. 2. 3. 5P.
- 21 Landsat Sentinel Making the Decision Bangalore, as seen through the lens of..
- 22 Finding the Ground Truth Census 2011 Health SurveysOpen Street Map
- 23https://dhsprogram.com/ DHS: Demographic & Health Survey • Collect and analyse data on population, health, HIV & nutrition across 90 countries
- 24 DHS VII Survey - India • 2015 - 2016, ~5000 Metrics • Quality of Life Metrics • Rural vs Urban • Material of roof • Wealth Index • Source of drinking water • Type of toilet facility • Cooking fuel • Owns land for agriculture • Owns livestock • Drought Index • Population Density https://dhsprogram.com/
- 25 • Human settlements closer to the coast • Northern belt has high density Population Distribution Maps on Kepler.gl
- 26 • Kerala (in the south) is wealthy despite higher population Wealth Distribution Maps on Kepler.gl
- 27 • North east region has better toilets despite low wealth Type of Toilet facilities Maps on Kepler.gl
- 28 Rural Urban But, what about data privacy? To preserve Cluster Anonymity… Locations are displaced • 5 km sq area - Rural • 2 km sq area - Urban Weakly Labelled Data
- 29 2 Preparing the Data In data science, 80 percent of the time is spent preparing data, 20 percent of the time is spent complaining about preparing data. “
- 30 Extracting the data from GEE Raw data Processed data
- 31 1 MEDIAN Pixel wise median for all bands 3 SORT by CLOUD COVER Limit to top-5 5 Filter by DATE 2015-06-01 to 2015-31-12 2 Select BANDS B4, B3, B2, B8, B11, B12 4 Define the ROI 2 KM or 5 KM radius Layered approach using Google Earth Engine Michael DeWitt, EE Overview
- 32 Satellite Images: Hidden features • Start with actual RGB image Sentinel 2-MSI Sunabeda, Odisha
- 33 NDVI Mask • Extract green cover Sentinel 2-MSI Sunabeda, Odisha
- 34 NDWI Mask • Extract water cover Sentinel 2-MSI Sunabeda, Odisha
- 35 NDBI Mask • Extract building cover Sentinel 2-MSI Sunabeda, Odisha
- 36 + + = ND- VI / WI / BI Mask Sentinel 2-MSI Sunabeda, Odisha • We extracted the masks & combined the satellite map layers
- 37 3 Building the Model
- 38 Pre-trained Network ResNet/DenseNet Pre-trained Network ResNet/DenseNet FC1 FC2 FC3 Water Source Detected Model Architecture Fully Connected LayersTransfer Learning Layer
- 39 Auxiliary Learning Karate Kid, Gif
- 40 Multi Task Auxiliary Learning Pretrained Network ResNet/DenseNet Pretrained Network ResNet/DenseNet FC1 FC2 FC3 Water SourceFully Connected Layers Roof ..metrics Transfer Learning Layer Karate Kid, Gif
- 41 4 Results Time
- 42 Application Demo App built on Streamlit.io
- 43 Model predictions: Wealth levels in India Actual vs Predicted Maps on Kepler.gl
- 44 Model predictions: Toilet facilities Actual vs Predicted
- 45 Model predictions: Roof materials Actual vs Predicted
- 46 Curious case of 2 States: Uttar Pradesh & Kerala Population Density Actual vs Predicted Kerala UP Kerala UP
- 47 Agricultural Land Actual vs Predicted Curious case of 2 States: Uttar Pradesh & Kerala
- 48 Wealth Actual vs Predicted Curious case of 2 States: Uttar Pradesh & Kerala
- 49 Toilet Facilities Actual vs Predicted Curious case of 2 States: Uttar Pradesh & Kerala
- 50 Where can we use such solutions? Disaster Response Image Source: Disaster response Behavioral Segmentation Poor Breakout Aspirant Owner Business Rich
- 51 Where can we use such solutions? Eradicating Mosquito-borne diseases Image Source: Deadliest animals | Dengue risk area
- 52 Session Takeaways Data & Methodology Slideshare Live Demo Gramener Code Github Coming Soon!
- 53 @kesaritweetsgramener.com Thank You! Presentation deck with references at gkesari.com/OReillyAI @srmsoumya
- Please rate our session! Session page on conference website O’Reilly Events App