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.

1. Informal settlements and
cadastral mapping
Divyani Kohli
Assistant Professor
Department of Urban and Regional Planning and Geo-Information
Management

2. Outline
 Slums/informal settlements overview
 Uncertainty analysis
 Visual boundaries for Cadastral mapping
 Conclusion

3. Planned Neighbourhood

4. A contrast!

5. What is a slum ?
 The most prominent feature are
 highest concentrations of poor people
 Precarious housing conditions
 Poor physical and environmental condition
 Challenge for image-based identification
 Different countries develop their own definitions
 Different appearances across various contexts

6. Ward Map
Pune, India

7. Different built-up ( Pune, India)

9. Source of errors in classification

10. Quantification of the uncertainties related to
image interpretations of urban slums*
*Based on the article:
Kohli, D., Stein, A., Sliuzas, R. 2013. Uncertainty analysis for image interpretations of urban slums
(Submitted to Journal of Computers, Environment and Urban Systems).
Informal settlement mapping

11. Delineations by experts

12. Overlay of experts' slum interpretations
for Ahmedabad

13. Overlay of experts' slum interpretations
for Nairobi

14. Overlay of experts' slum interpretations
for Cape Town

15. Existential uncertainty expresses the uncertainty about the
existence of a slum in reality.
Extensional uncertainty implies that the area covered by a
slum can be determined with limited certainty.
Uncertainty analysis

16. Agreement level layers of slum delineations by
19 experts

17. Cities
Levels of agreement
15-19 experts
(% area)
Reference
data (%)
Ahmedabad 42 46
Nairobi 61 70
Cape Town 61 52
Percentage of identified slum area at
maximum agreement level

18. Uncertainty analysis for Nairobi

19.  A particular context may be easier for delineating slums
compared to other. Local knowledge is needed.
 Ahmedabad, India seems to be more complex than Nairobi
and Cape Town.
 Uncertainty analysis should be part of classification in such
cases.
Main conclusions

20. Cadastral mapping
 Explore and evaluate techniques for automatic/
semi-automatic detection and extraction of visible
cadastral boundaries

21.  To quantify the percentage of completely visible cadastral
parcels using VHR satellite images in a variety of contexts*
* This objective is published as the following article:
Kohli, D., Bennett, R. M., Lemmen, C. H. J., Asiama, K. O., Morales, J. A., Pinheiro, A., Zevenbergen, J. A. (2017). Quantitative comparison of completely visible cadastral
parcels using satellite images: a step towards automation. In: Proceedings of FIG working week 2017: Surveying the world of tomorrow: from digitalisation to augmented
reality, 29 May - 2 June 2017, Helsinki, Finland. 14 p.
Cadastral mapping

22. Case locations
 For visible boundaries analysis
Ethiopia, Rwanda, Guatemala, Ghana, Mozambique, Nepal
and Kenya

23. Visual boundary analysis – Ethiopia

24. Visual boundary analysis
– Ethiopia

25. Visual boundary analysis – Ghana

26. Visual boundary analysis – Rwanda

27. Visual boundary analysis – Rwanda

28. Visual boundary analysis – Mozambique

29. Visual boundary analysis – Guatemala

30. Visual boundary analysis – Nepal

31. Visual boundary analysis – Kenya

32. Visual boundary analysis
Place Total
Parcels
Fully visible
Landscape
Number Percentage
Ethiopia 128 92 71% Rural
Rwanda 151 33 22% Rural
Guatemala 172 47 27% Urban
Ghana 200 25 12.5% Rural
Mozambique 190 47 24.7% Urban
Nepal 164 0 0 Rural-hilly
Kenya 179 23 12.8% Peri-
urban/informal

33. Main conclusions
 The analysis was useful to get understanding of the diversity in
boundary morphologies and their visibility across different areas.
The percentage of completely visible cadastral parcels ranged from
zero to 71 percent when compared to the reference cadastral map
 Small-holder farms and organized urban areas have significant
potential for visual boundary/parcel identification

34. Thank you