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Machine Learning for Better Maps

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Quantifying Error in Training Data for Mapping and Monitoring the Earth System - A Workshop on “Quantifying Error in Training Data for Mapping and Monitoring the Earth System” was held on January 8-9, 2019 at Clark University, with support from Omidyar Network’s Property Rights Initiative, now PlaceFund.

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Machine Learning for Better Maps

  1. 1. Machine Learning for Better Maps Zhuangfang Yi- Development Seed - @geonanayi Workshop @Clark University, 01/07/2019 OpenStreetMap label quality and geodiversity for machine learning applications
  2. 2. Deep Learning Machine Learning + OpenStreetMap + Satellite Imagery
  3. 3. Urban = Yes Image Classification Training Dataset
  4. 4. Object Detection training dataset
  5. 5. Segmentation training dataset
  6. 6. Label Maker OpenStreetMap is an attractive label/tags database for machine learning applications that holds repid updated mapped object daily by thousands of users.
  7. 7. Training Data Completeness Matters - OSM tag/label info and popularity Tag info in France. Landuse is one of tags that has been frequently used by the users.
  8. 8. Label Maker OpenStreetMap is an attractive label/tags database for machine learning applications that host rapidly update mapped object daily by thousands of users.
  9. 9. Training data Completeness Matters OpenStreetMap Label Quality for Machine Learning Applications
  10. 10. ISO standard for geographic information data: positional accuracy, completeness, and logical consistency. Other data quality issues in OSM: - Vandalism - Missing details - Completeness and accuracy Training data Completeness Matters
  11. 11. Training Data Completeness Matters Available tools for data quality assessment: - OSM analytics (OSM v.s. Human Settlement Layer) - OSM-lint (e.g. OSM v.s. US census TIGER in USA)
  12. 12. Training Data Completeness Matters Building classification in Vietnam with LeNet on AWS SageMaker. Individual building detection with Tensorflow Object detection in Mexico 60% -> 84% from Vietnam to Mexico
  13. 13. - OSM label data + satellite images match - OSM label data is not well-aligned with the paired satellite image Training Data Completeness Matters
  14. 14. Training Data Completeness Matters HOT Task Manager
  15. 15. Training Data Completeness Matters Urchn for urban change detection with ML
  16. 16. Training Data Geodiversity Matters Applying Machine Learning Applications for Geospatial Analysis
  17. 17. Training Data Geodiversity Matters High-voltage grid detection with deep learning in Pakistan, Nigeria, and Zambia
  18. 18. Training Data Geodiversity Matters
  19. 19. Training Data Geodiversity Matters
  20. 20. Training Data Geodiversity Matters Urban settlement change detection in Ethiopia between 2000 - 2017 with random
  21. 21. Conclusions When it comes to applying machine learning applications: - Training data quality matters, and to use OSM label data for ML applications, I recommend: 1. Do a proper label completeness assessment with currently available tools; 2. Check OSM tag/label info and frequency for your area of interest; 3. For segmentation ML application, make sure the image tiles align-well with your label dataset; 4. Prepare training dataset using: Label Maker, or RoboSat or other data prep tools. - Training data geodiversity matters, and recommend to do: a. data/image feature similarity analysis;
  22. 22. Contacts Twitter @geonanayi GitHub @geoyi Email nana@developmentseed.org
  23. 23. Data Completeness Matters HOT Analytics for Health With support of the Bill and Melinda Gates Foundation and the Clinton Health Access Initiative, we have designed an analysis tool to evaluate the accuracy and precision of OpenStreetMap field data.
  24. 24. Other data quality issues in OSM: - Vandalism - Missing details - Completeness and accuracy The results of this analysis found the positional accuracy of OpenStreetMap data to be very good in comparison to OS MasterMap, with over 80% overlap between most the road objects tested between the two datasets. The results also found there to be a positive correlation between road name attribute completeness and number of users per area. Training data Completeness Matters

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