The document discusses the application of machine learning to improve map accuracy using OpenStreetMap (OSM) data. It emphasizes the importance of training data completeness, quality, and geodiversity for machine learning projects in geospatial analysis, providing examples from various countries. The document also highlights available tools for data quality assessment and suggests best practices for utilizing OSM data effectively in machine learning applications.

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. Deep Learning
Machine Learning + OpenStreetMap + Satellite Imagery

3. Urban = Yes
Image Classification Training Dataset

4. Object Detection training dataset

5. Segmentation training dataset

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.

8. 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.

9. Label Maker
OpenStreetMap is an attractive label/tags database for machine learning
applications that host rapidly update mapped object daily by thousands of users.

10. Training data
Completeness Matters
OpenStreetMap Label Quality for Machine Learning
Applications

11. 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

12. 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)

13. 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

14. - OSM label data + satellite images match
- OSM label data is not well-aligned with the paired satellite image
Training Data Completeness Matters

15. Training Data Completeness Matters
HOT Task Manager

16. Training Data Completeness Matters
Urchn for urban change detection with ML

17. Training Data
Geodiversity Matters
Applying Machine Learning Applications for Geospatial
Analysis

18. Training Data Geodiversity Matters
High-voltage grid detection
with deep learning in
Pakistan, Nigeria, and
Zambia

19. Training Data Geodiversity Matters

20. Training Data Geodiversity Matters

21. Training Data Geodiversity Matters
Urban settlement change detection in Ethiopia between 2000 - 2017 with random

22. 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;

23. Contacts
Twitter @geonanayi
GitHub @geoyi
Email nana@developmentseed.org

24. 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.

25. 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