A Review of High and Very High Resolution Remote Sensing Approaches for Detecting and Mapping Slums
Regular readers of this site might of noticed that we have an interest in slums. In the past this has focused on modeling them from an agent-based perspective, comparing volunteered geographical information to more authoritative data on slums, to that of attempting to come up with a Slum Severity Index. However, more recently we have taken to looking at how remote sensing approaches have been and can be used to detect and map slums.
To this end we recently had a review paper accepted in Urban Systems entitled “A Critical Review of High and Very High Resolution Remote Sensing Approaches for Detecting and Mapping Slums: Trends, Challenges and Emerging Opportunities“. In this paper we carry out a comprehensive review of studies that have used high and very high resolution (H/VH-R) remote sensing techniques to detect and map slums (along with their global footprint). We discuss approaches used (e.g. multi-scale, image texture analysis, landscape analysis, object-based image analysis, building feature extraction, data mining, socio-economic measures) using H/VH-R imagery for identifying and mapping slums, listing what are the limitations and advantages of each. After this, we discuss emerging sources of geospatial data that should we thing should be considered (e.g., volunteer geographic information, VGI, social media) in conjunction with growing trends and advancements in technology (e.g., geosensor networks, unmanned aerial vehicles (UAVs) or “drones) when trying to map and monitor slums. We argue that it is only through such data integration and analysis that we can then create a benchmark for determining the most suitable methods for mapping slums in a given locality. Below you can read the abstract of the paper and see some of the figures we use to support our discussion, along with the full reference.
Abstract: Slums are a global urban challenge, with less developed countries being particularly impacted. To adequately detect and map them, data is needed on their location, spatial extent and evolution. High- and very high-resolution remote sensing imagery has emerged as an important source of data in this regard. The purpose of this paper is to critically review studies that have used such data to detect and map slums. Our analysis shows that while such studies have been increasing over time, they tend to be concentrated to a few geographical areas and often focus on the use of a single approach (e.g., image texture and object-based image analysis), thus limiting generalizability to understand slums, their population, and evolution within the global context. We argue that to develop a more comprehensive framework that can be used to detect and map slums, other emerging sourcing of geospatial data should be considered (e.g., volunteer geographic information) in conjunction with growing trends and advancements in technology (e.g., geosensor networks). Through such data integration and analysis we can then create a benchmark for determining the most suitable methods for mapping slums in a given locality, thus fostering the creation of new approaches to address this challenge.
Keywords: high and very high resolution imagery; remote sensing, slums; geosensor networks; image analysis.
|Global distribution of urban and slum populations.|
|Country level distribution of H/VH-R studies (studies published between 1997-2016).|
|OSM and Google Maps views of Kibera slum (a) Top:Left OSM and right Google Maps (b) Bottom:Left OSM and right Google Maps.|
Mahabir, R., Croitoru, A., Crooks, A.T., Agouris, P. and Stefanidis, A. (2018), A Critical Review of High and Very High Resolution Remote Sensing Approaches for Detecting and Mapping Slums: Trends, Challenges and Emerging Opportunities, Urban Science. 2(1), 8; doi:10.3390/urbansci2010008 (pdf)
As always, any thoughts or comments are most welcome.