Mapping and Assessment of Housing Informality Using Object-Based Image Analysis: A Review

Abstract

Research on the detection of informal settlements has increased in the past three decades owing to the availability of high- to very-high-spatial-resolution satellite imagery. The achievement of development goals, such as the Sustainable Development Goals, requires access to up-to-date information on informal settlements. This review provides an overview of studies that used object-based image analysis (OBIA) techniques to detect informal settlements using remotely sensed data. This paper focuses on three main aspects: image processing steps followed when detecting informal settlements using OBIA; informal settlement indicators and image-based proxies used to detect informal settlements; and a review of studies that extracted and analyzed informal settlement land use objects. The success of OBIA in detecting informal settlements depends on the understanding and selection of informal settlement indicators and image-based proxies used during image classification. To meet the local ontology of informal settlements, the transfer of OBIA mapping techniques requires the fine-tuning of the rulesets. Machine learning OBIA techniques using image proxies derived from multiple sensors increase the opportunities for detecting informal settlements on the city or national level.

Slums Evolution and Sustainable Urban Growth: A Comparative Study of Makoko and Badia-East Areas in Lagos City

“…Being the biggest city in Nigeria and a significant financial center, Lagos has seen quick urbanization and a rise in population. However, owing to this rapid expansion, the city's infrastructure and housing development have failed to keep up, resulting in informal settlements and slums [1,26]. This situation disproportionately affects low-income people compelled to reside in substandard housing due to the limited availability of affordable options.…”

“…The absence of effective, sustainable planning has contributed to the expansion of these slums, exacerbating the urban challenges [16,21]. Within slum areas, waste management systems are often lacking, resulting in water pollution and environmental degradation [1,26]. The findings provide evidence for the situation in Lagos, illustrated by Makoko as an example.…”

“…Furthermore, the insufficient state of roads and other fundamental infrastructure elements in urban slums remains a critical area requiring in-depth investigation. It has also been found by different studies [1,14,26,[36][37][38] that people who live without proper planning can sustainably enjoy the environment if they can manage poor urban slum living conditions. Hence, sustainable planning can be achieved by improving their abilities and resources.…”

“…Firstly, different reviewed research papers [2,[24][25][26][27] have delved into the practical potential of Remote Sensing (RS) and Geographic Information Systems (GIS) in addressing issues related to slums by generating maps from satellite images. These papers all share a common idea: It is essential to create accurate maps, study how people are settled in a particular place, highlight spatial changes, guide specific actions, make intelligent decisions, and involve the community.…”

A Geoscience-Aware Network (GASlumNet) Combining UNet and ConvNeXt for Slum Mapping

“…The disparities between slums and non-slums led to the discovery, through an analysis of GSO-related studies, that the vegetation spectral indexes and textural characteristics are crucial to discriminate slums and non-slums [15]. Specifically, houses are tightly packed and disordered in slums, whereas house are relatively sparsely packed and orderly in formal settlements [16]. In addition, there are fewer green spaces in slums, whereas there are adequate green spaces in formal settlements [17].…”