Tenure from space - Using Remote Sensing in support of land administration

M-GEO
M-SE
PLUS
Additional Remarks

Possible case study area: Indonesia

Topic description

A huge task is waiting to realize the global agenda in relation to tenure security as recognized by the sustainable development goals (SDGs) set by the UN. The SDG, goal 1, target 1.4 aims for security of tenure for all, especially for the poor and the vulnerable (UNDP, 2015). Methods that could provide cheap, fast and effective solutions to speed up mapping can assist and thus, are actively being investigated. These methods could be used for Fit-For-Purpose land administration solutions, specifically for informal settlements - most global south countries have either incomplete or no concrete data about such areas. So far, there has been limited attention and trials to explore the feasibility of using VHR satellite images and extraction tools for mapping, especially in the domain of land administration. Thus, the main objective of this study will be to explore the use of feature extraction methods for identification and characterization of informal settlements using very high resolution (VHR) satellite imagery. Further, the aim is to identify image-based proxies that can determine land administration related processes on ground, e.g. upgraded roofs or roads may signify a change in tenure within a settlement. The generated knowledge could potentially be useful for inclusive planning in cities where informal settlements are an integral part of the urban fabric.

Topic objectives and methodology

This will be a comparative research based around specific case area – where supervised and unsupervised image analysis methods will be tested and compared to identify different tenure types using image-based proxies.

References for further reading

Alemie, B. K., Bennett, R. M., & Zevenbergen, J. (2015). A socio-spatial methodology for evaluating urban land governance: the case of informal settlements. Journal of Spatial Science, 60(2), 289-309. doi:10.1080/14498596.2015.1004654
Kuffer, M., Wang, J., Nagenborg, M., Pfeffer, K., Kohli, D., Sliuzas, R., & Persello, C. (2018). The scope of earth-observation to improve the consistency of the SDG slum indicator. ISPRS International Journal of Geo-Information, Vol. 7. https://doi.org/10.3390/ijgi7110428
Kohli, D., Sliuzas, R., Kerle, N., & Stein, A. (2012). An ontology of slums for image-based classification. Computers, Environment and Urban Systems, 36(2), 154-163. doi:10.1016/j.compenvurbsys.2011.11.001