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Gramener Webinar - Dawn of Geospatial AI - A technical sneak peek

Soumya Ranjan, Lead Data Scientist, and Sumedh Ghatage, Senior Data Science Engineer at Gramener conducted a webinar on Geospatial AI. <br><br>Join Gramener for this free webinar and learn more about geospatial analytics. The experts talk about the potential for various industrial divisions and roles. They also guide you through the technical know-how that you might need to get started on, as well as some amazing tips and tricks for navigating the exciting Geospatial Analytics realm.<br><br>Webinar link: https://info.gramener.com/geospatial-ai-technical-sneak-peek

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Gramener Webinar - Dawn of Geospatial AI - A technical sneak peek

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  1. Data People Hello नमस्कार डेटा लोग 數據人 你好 PersonasDe Datos Hola

  2. Dawn of the Geospatial AI : A Technical Sneak Peek19 November 2020

  3. Introduction Insights as Stories Sumedh Ghatage Geospatial Data Scientist Soumya Ranjan Mohanty Lead Data Scientist 100+ Clients /sumedh-ghatage @srmsoumya @ghatagesumedh Help start, apply and adopt Data Science

  4. What is Geospatial Data Science? Getting Started with GIS and Remote Sensing Geospatial Analytics - Python Libraries Jupyter and Papermill Workflow GEO - AI at Gramener What Next? Agenda

  5. What is Data Science? Data science is an inter-disciplinary field Extract knowledge and insights from many structural and unstructured data Asking right questions to the data to get right outputs, visuals, and narrative

  6. What is Geospatial Data Science? Geospatial Data Science is a subset of Data Science Focuses on the unique characteristics of geospatial data Beyond simply looking at where things happen -->To understand why they happen there.

  7. Geospatial Technology GIS Remote Sensing GPS-Survey Digitization Active Passive Reference gislounge.com

  8. Vector Data Vector data structures represent specific features on the Earth’s surface, and assign attributes to those features.

  9. Raster Data A raster consists of a matrix of cells (or pixels) organized into rows and columns (or a grid) where each cell contains a value representing information Reference earthdatascience.org

  10. Understanding Satellite Images Electromagnetic Spectrum True Colour CompositeR,G,B False Colour CompositeNIR, R, G Reference www.nrcan.gc.ca

  11. Commonly Used Platforms • Visualize, question, analyze, and interpret geographical data • Edit Data on the fly • Processing Capabilities • Multiple Layer Overlays • Business Intelligence

  12. Commonly Used Database Systems • Storing and querying data that represents objects defined in a geometric space • Ability to cope-up with Points, Lines & Polygons • Majorly used with Vector Datasets • Ease of working with Spatial Big Data

  13. Python - Geospatial Libraries

  14. GEOFLOW Geospatial Workflow using Python, Jupyter Notebook & Papermill

  15. Automation using Python & Jupyter Notebooks Read & display a GeoJSON

  16. Automation using Python & Jupyter Notebooks Clip Raster to a specific region

  17. Automation using Python & Jupyter Notebooks Visualize building footprints over population data

  18. Reproduce using Papermill Parameterize Notebooks

  19. Repeat using Papermill config.json

  20. GEOFLOW https://srmsoumya.github.io/blog/

  21. Quality of life Geo AI in Gramener Predicting

  22. We have many Satellites today.. Mapping the Earth Image by Naideh Bremer

  23. ..can we put them to Better use? Satellites are more helpful for Pizza delivery... ..than for the delivery of emergency services

  24. Inspiring work by Stanford on Predicting Poverty Daytime satellite imagery Night-time satellite imagery http://sustain.stanford.edu/predicting-poverty

  25. Estimate per-capita consumption expenditure Model extracts features.. ..and predicts poverty in Africa http://sustain.stanford.edu/predicting-poverty

  26. We tried predicting the Quality of Life from Satellite images

  27. …our model predicted wealth levels.. Actual vs Predicted Maps on Kepler.gl

  28. ..and, the model estimated the type of roof! Actual vs Predicted

  29. Here’s a quick demo of the application Demo: https://qol.gramener.com | Built on Streamlit.io

  30. Lives using AI Geo AI in Gramener Saving

  31. What’s the most Dangerous Animal on our Planet? Mosquito-borne diseases: A health hazard in the tropics Dengue ..in 100+ countries.. 400 million cases.. ..40% of world population Image Source: Infographic - Gates Notes | World map from Wikimedia – by KVDP – Own work, CC BY-SA 3.0

  32. WMP’s Innovation: Infect the Mosquitoes! Image Source: WMP website

  33. Success Story: Northern Queensland Source: Success story - WMP website | Paper - Gates Open Research

  34. The Solution: Putting it all together Identify Region of Interest Data Preparation Site Planning Decisions You can edit this text Output Imagery with • Population by Area Coverage and areas with No Human settlement • Grid-wise Building area with Number of Buildings • Spatial Clustering of Grids Train Test Split Building Footprints Gridded Map Model execution Grids Building Footprints Population estimation Region Boundary BFP from Model GPW Data NDVI Mask

  35. What do the Region Density maps look like? Input Satellite Imagery (50 cm resolution) Output Gridded Maps (50m * 50m) Low Population High Population Micro-Scale City-Scale

  36. Outcomes: Highlights of the Solution 1. Effort Savings 2 hrs Reduced the time taken from ~3 weeks 2. Better Effectiveness Accurate release plan with very high ROI 70% 3. Higher Efficiency 50% Efficient post-release monitoring & validation The solution is being rolled out across countries Press Release: Defeating Dengue with AI

  37. Planning & Recovery Geo AI in Gramener Disaster

  38. Assigning Flood Risk Score Road Networks Building Footprints Digital Elevation Model Slope Impervious Surfaces Vegetation Multicriteria Decision Making Analytics

  39. Expected Results Multicriteria Decision Making Analytics

  40. Next ? Geo AI in Gramener What

  41. Agriculture Smart City Analytics Others Location Intelligence Crop Type Mapping and Feature Extraction Crop Insurance and Management Crop monitoring and Damage Assessment Precision Farming & Production Control Land Use - Land Cover Planning Urban- Natural Resource Monitoring Building footprints Extraction Accessibility and Livability Measurement Price Surge Modelling Geomarketing and Recommendation Systems Site Suitability Analytics Solid Waste Management Forest Fire Hazards and Mapping Property Tax Automation Disease Outbreak Monitoring & Control Election Campaign Management

  42. Gramener won MICROSOFT AI AWARD 2018 We’ve been applying AI for Good by solving problems around the world Some of our work in this area… Gramener partnered with Microsoft AI for Earth to help Nisqually River Foundation automate the identification of fish species using AI-driven deep learning models. Predicting Quality of Life from Satellite Imagery Monitoring Salmon in Rivers Species Classification API Saving the African Elephant Camera Traps Penguin Counts in Antarctica

  43. Think Spatialy Insights as Stories Sumedh Ghatage Geospatial Data Scientist Soumya Ranjan Mohanty Lead Data Scientist /sumedh-ghatage @srmsoumya @ghatagesumedh Thank You! Help start, apply and adopt Data Science

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