We use the Georeferenced results of the 2010 Census in Mexico to train machine learning algorithms to detect growth in cities and contribute new information to estimate the total population.

1. Machine Learning for Imagery
Abel Coronado & Jimena Juarez

2. Imagery Pipeline

3. Imagery Pipeline
Imagery Pipeline
In order to have a road map.
We build an abstract machine learning pipeline for Imagery
Based on:
IBM CRISP-DM
Cross Industry Standard Process for Data Mining
&
Microsoft Data Science lifecycle
InKyung Choi; Refactoring

4. Imagery Pipeline
Imagery Pipeline

5. Pilot Project Pipeline

6. Establish the problem to be solved
Establish the problem to be solved
Monitor the growth of cities of Mexico (Urban and Rural), which would generate a more timely
input for :
• Cartography update
• Estimation of the population in non-census years
• Related with:
• SDG Indicator 11.3.1: Ratio of land consumption rate to population growth rate
• SDG Indicator 15.3.1: Proportion of land that is degraded over total land area

7. Identify satellite data to address the problem
Identify satellite data to address the problem
In our case, the data to monitor the growth of cities can be Landsat images (NASA & USGS):
• They are open data.
• There is a constant record since the 70s, although they are available from 1985 to date.
• The spatial resolution of Landsat images is 30 meters.
• Temporal resolution is 16 days.
• Spectral resolution is 6 spectral bands (range of wavelength measured by the passive
sensors of the Landsat Satellites)

8. Identify satellite data to address the problem
Identify satellite data to address the problem

9. Identify satellite data to address the problem
Identify satellite data to address the problem

10. Identify satellite data to address the problem
Identify satellite data to address the problem

11. Translate the problem into statistical problem
Translate the problem into statistical problem
We define that is a classification problem:
• Unit of analysis: 1km x 1km squares covering the whole country.
• 3 classes were assigned:
• Urban
• Rural
• None of the above

12. Obtain ground-truth data
Obtain ground-truth data
Obtain layers of geographical information:
• Georeferenced Population Census (Block Level Aggregation)
• Georeferenced Economic Census (Economic Unit Level)
• Visual Inspection

13. Translate the problem into statistical problem
Obtain ground-truth data
Build the polygons (1 km – 1 km)
1’ 975, 719 Polygons

14. Translate the problem into statistical problem
Obtain ground-truth data
Assign Labels

15. Obtain satellite imagery data
Obtain satellite imagery data
• 32 TeraBytes of Images in external discs.
• March 4, 2019
• The images are ARD, Analysis Ready Data.
• In essence ARD means that the pixels of the entire time serie are aligned.
• Which are processed to combine sensors and make comparisons at different times.

16. Obtain satellite imagery data
Obtain satellite imagery data
• 34 Years

17. Obtain satellite imagery data
Obtain satellite imagery data
• 2010 same year of the Census

18. Obtain satellite imagery data
Obtain satellite imagery data
• 2010 same year of the Census
2,737,273,075 pixels – 35 Gb

19. Integrate ground-truth data with satellite data
Integrate ground-truth data with satellite data
1 km x 1 km tagged polygon
33 x 33 pixels
30 meters of resolution
33 Pixels
6Bands
6 swir 2------
5 swir 1------
4 nir---------
3 red---------
2 green-------
1 blue--------
Python
Function
Multidimensional Array
NumPy

20. Take a Random Sample
Take a Random Sample

21. Define features and generate a dataset to be used for analysis
Define features and generate a dataset to be used for analysis
• Feature Engineering
30,000 tagged regions
1km x 1km
33 pixels x 33 pixels x 6 Bands
30,000
4,305
30,000 rows in a data matrix
?

22. Define features and generate a dataset to be used for analysis
Define features and generate a dataset to be used for analysis
• Feature Engineering
30,000 tagged regions
1km x 1km
33 pixels x 33 pixels x 6 Bands
30,000
4,305
30,000 rows in a data matrix

23. Auto Machine Learning
https://epistasislab.github.io/tpot/

24. Evaluate ML
Random Sample
30,000 Elements
Machine Learning
Result of TPOT
2010 National
Classification
76% Accuracy
Training
2010 Geomedian
Classification
30,000
4,305
30,000 rows in a data matrix

25. Detailed results; First Iteration
Detailed results; First Iteration

26. Next Steps

27. Second Iteration
At this moment finishing second iteration
Improve 2 or 3 %

28. Next Steps
Next Steps
• Finish a second iteration, no later than two weeks.
• The grid is also an important innovation and is evolving, it is likely
that we will have a new version very soon and we should update
the classification.
• Apply the model to un-labelled years, for example 2019 first
semester.
• Validate a sample of the un-labelled year, requesting support for
the area of visual interpretation of the geography division, to have
a measure of product quality.
• Hold meetings with the area of sociodemographic statistics,
involve them in the exercise to improve the potential benefit in the
population estimate.
• Hold meetings with the area of statistical research, to receive
feedback and observations that can improve the product.
• Hold meetings with the cartographic update area, to receive
feedback.

29. Next Steps
• To the last week of October
• Write a first draft of the document that explains the process carried out. Probably until the results of the second
iteration and some results of meetings with stakeholders.

30. GRACIAS!