Combining Object-Based Machine Learning with Long-Term Time-Series Analysis for Informal Settlement Identification

Abstract

Informal settlement mapping is essential for planning, as well as resource and utility management. Developing efficient ways of determining the properties of informal settlements (when, where, and who) is critical for upgrading services and planning. Remote sensing data are increasingly used to understand built environments. In this study, we combine two sources of data, very-high-resolution imagery and time-series Landsat data, to identify and describe informal settlements. The indicators characterising informal settlements were grouped into four different spatial and temporal levels: environment, settlement, object and time. These indicators were then used in an object-based machine learning (ML) workflow to identify informal settlements. The proposed method had a 95% overall accuracy at mapping informal settlements. Among the spatial and temporal levels examined, the contribution of the settlement level indicators was most significant in the ML model, followed by the object-level indicators. Whilst the temporal level did not contribute greatly to the classification of informal settlements, it provided a way of understanding when the settlements were formed. The adaptation of this method would allow the combination of a wide-ranging and diverse group of indicators in a comprehensive ML framework.

Detecting Land Surface Temperature Variations Using Earth Observation at the Holy Sites in Makkah, Saudi Arabia

“…Generally, the results of the spatial distribution of LST, NDVI, and NDBI from Table 2 reveal little heterogeneity in LST due to the use of different materials in each holy site. The most widely known and frequently used land cover metric is NDVI, which calculates the percentage of vegetation in a pixel [36,37]. Ref.…”

Object-Based Informal Settlement Mapping in Google Earth Engine Using the Integration of Sentinel-1, Sentinel-2, and PlanetScope Satellite Data

“…Being inexorably a reflection of the "urbanization of poverty" [2], informal settlements are characterized by dense housing, made up of sub-standard, heterogeneous construction materials, which, when coupled with their characteristic location on floodvulnerable areas, exacerbate residents' risk and vulnerability to natural hazards such as flood events [3]. With this type of housing playing host to approximately one billion dwellers globally [4], the United Nations has prioritized informal settlement improvements in the 2030 Sustainable Development Goals [5,6]. However, despite these stipulated targets, informal settlements continue to grow [6].…”

“…Recently, object-based image analysis (OBIA) or geographic object-based image analysis (GEOBIA) has been applied more frequently in capturing heterogeneity in fragmented urban landscapes for informal settlement identification [5,6,[16][17][18]. The strength of objectoriented approaches (OOA) for informal settlement analysis is in its capability to incorporate spectral, spatial and contextual characteristics of an image, which intensify the potential to capture informal settlement morphological diversities [17,21].…”

“…In a further progression of this work, taking advantage of machine learning, they synergistically combined with OBIA to improve informal settlement mapping in Saudi Arabia, achieving enhanced overall accuracy of 91% from GeoEye-1 imagery [16]. An attempt to integrate data from two sensors for OBIA was presented by Pratomo et al [5], combining GeoEye-1 and Landsat data in the Middle Eastern environment, achieving overall Random Forests (RF) classification accuracy of 95%. These past efforts focused on the ontological framework suggested by Kohli et al [23] using a range of informal settlement indicators for segmentation at different scale levels.…”

“…The authors demonstrated the benefits of integrating data from all three sensors, yielding accuracy levels of 91% and 85%, respectively. Research endeavors that incorporated PL imagery reported improved classification of subtle features [32,34,43], presenting opportunities for semantic abstraction of small deprivation pockets, identification of which is inherently difficult [5]. According to Bwangoy et al [44], classification of multi-source satellite data yields higher classification accuracy relative to the accuracy achieved by a single source data.…”