Detection of Slums from Very High-Resolution Satellite Images Using Machine Learning Algorithms: A Case Study of Fustat Area in Cairo, Egypt

Salem, Muhammad Shahir Hakimy; Naoki, Tsurusaki; Eissa, Ahmed; Osman, T. A.

doi:10.5109/4102491

Cited by 5 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Informal settlements are characterized by organic and irregular road networks or paths [51]. Only a limited number of studies have integrated the detection of road networks in distinguishing informal from formal settlements [26,52,53]. The geometric characteristics of informal settlement land use features have been investigated using the asymmetry of sub-objects [21,38].…”

Section: Detection Of Informal Settlement Using Settlement-level Indi...mentioning

confidence: 99%

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

Mudau,

Mhangara

2023

Urban Science

View full text Add to dashboard Cite

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.

show abstract

Section: Detection Of Informal Settlement Using Settlement-level Indi...mentioning

confidence: 99%

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

Mudau,

Mhangara

2023

Urban Science

View full text Add to dashboard Cite

show abstract

“…3) can all be achieved by ensuring access to safe and affordable housing and basic services (target 11.1) [14]. Similarly, Table 1 below shows the interlinkages between self targets (SDG 11) and the targets of other goals [15].…”

Section: Interlinkages Between Sdg 11 and Other Targetsmentioning

confidence: 99%

Modeling and prediction of the achievement level with related goals for SDG 11: Sustainable Cities and Communities

Baskaran¹,

Velkennedy²,

Murugan³

et al. 2022

Proceedings of International Exchange and Innovation Conference on Engineering &Amp; Sciences (IEICES)

View full text Add to dashboard Cite

United Nations has announced the current decade as a 'decade of action' to deliver the 2030 agenda successfully. The 17 Sustainable Development Goals (SDG) achievement level of all the nations varies. Almost 70% of the world's population is expected to settle in the urban region by 2050. So SDG 11, the goal of developing a resilient and sustainable city, was focused on in the study. Since the targets of each goal are interrelated to others, the progress in each goal will also reflect on the achievement scores of different goals. An attempt was made in this study to develop a model to predict the achievement status of SDG 11 based on the performance of the country's related goals. The Random Forest machine learning algorithm was used in developing the prediction model with the KNIME analytics platform, which attained overall prediction accuracy up to 83.67%.

show abstract

“…The technology used in monitoring is one of the most important factors for accurate identification of these areas, which were not widely available in the government's attempts presented [10], where the process of monitoring the deteriorated areas was carried out through a subjective manual field survey based on personal opinions; which takes a lot of efforts, time and costs [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…Despite using satellite-based remote sensing techniques in monitoring the deteriorated areas, those authorities relied on a limited number of urban/physical indicators, such as the pattern of the irregular street network and the type of compactness of urban fabric [11,12]. This resulted in a poor accuracy in detecting these areas.…”

Section: Introductionmentioning

confidence: 99%

Spatial Monitoring and Classifying of Urban Deterioration in the Egyptian Cities Using Geographic Information System (GIS) Approach: A Case Study of Mansoura City, Egypt

Osman¹,

Tahoon²,

Shehata³

et al. 2022

cea

View full text Add to dashboard Cite

Detection of Slums from Very High-Resolution Satellite Images Using Machine Learning Algorithms: A Case Study of Fustat Area in Cairo, Egypt

Cited by 5 publications

References 0 publications

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

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

Modeling and prediction of the achievement level with related goals for SDG 11: Sustainable Cities and Communities

Spatial Monitoring and Classifying of Urban Deterioration in the Egyptian Cities Using Geographic Information System (GIS) Approach: A Case Study of Mansoura City, Egypt

Contact Info

Product

Resources

About