Using Local Knowledge and Remote Sensing in the Identification of Informal Settlements in Riyadh City, Saudi Arabia

Alrasheedi, Khlood Ghalib; Dewan, Ashraf; El‐Mowafy, Ahmed

doi:10.3390/rs15153895

Cited by 6 publications

(4 citation statements)

References 60 publications

(111 reference statements)

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“…The unique topography and urban morphology on the Najd plateau make Riyadh prone to flooding [5]. Steep slopes, wadi systems, and urban sprawl create complex flood risk scenarios that go beyond traditional management strategies [6].…”

Section: Introductionmentioning

confidence: 99%

A Weighted Overlay Analysis for Assessing Urban Flood Risks in Arid Lands: A Case Study of Riyadh, Saudi Arabia

Alharbi

2024

Water

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Urban flooding poses a significant problem in arid regions, where unusual rainfall events can lead to disastrous consequences. This study used geographic information systems (GIS) to identify and predict potential flood risk zones in Riyadh City, Saudi Arabia. A comprehensive flood risk map was created by integrating various datasets, including digital elevation models (DEM), lithology, precipitation patterns, slope, and drainage density. Through a weighted overlay analysis, Riyadh was segmented into zones with different levels of flood susceptibility, ranging from low to very high risk. The study area spans 905 km2. Of this total area, 32% is at a low risk of flooding, while 31%, 24%, and 12% of the area are at a moderate, high, and very high risk of flooding, respectively. The findings of the study were validated through field surveys conducted before and after recent rainfall events. These validation efforts corroborated the high-risk areas predicted by the model, thereby ensuring the reliability of the map for future urban planning and risk mitigation strategies.

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Section: Introductionmentioning

confidence: 99%

A Weighted Overlay Analysis for Assessing Urban Flood Risks in Arid Lands: A Case Study of Riyadh, Saudi Arabia

Alharbi

2024

Water

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“…An increasing number of researchers [4,[8][9][10] are working on slum mapping using remotely sensed imagery. Distinct physical characteristics are vital to extract slums from RS imagery [11,12]. For the most part, buildings are low and tightly packed in slum communities with fewer paved roads and green space [13].…”

Section: Introductionmentioning

confidence: 99%

“…For example, Alrasheedi, K.G. et al [12] combined object-oriented analysis and expert knowledge to map slums in Riyadh City, Saudi Arabia, and obtained an overall accuracy of 94%. Williams, T.K.-A.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, Verma et al [36] used a CNN model to predict slums in Mumbai, and their prediction task took 8 h on the VHR/HR imagery. Existing studies using HR/VHR imagery [8,12,13,23,37] typically extract slums in a small portion of a city. These studies have difficulties in large areas, such as in city-wide-scale, national-scale or even global-scale mapping tasks, owing to the small ground swath and high prices of such images.…”

Section: Introductionmentioning

confidence: 99%

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Mapping Slums in Mumbai, India, Using Sentinel-2 Imagery: Evaluating Composite Slum Spectral Indices (CSSIs)

Peng,

Lu,

et al. 2023

Remote Sensing

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Accurate geographic data of slums are important for handling urban poverty issues. Previous slum mapping studies using high-resolution or very-high-resolution (HR/VHR) remotely sensed (RS) images are commonly not suitable for city-wide scale tasks. This study aims to efficiently generate a slum map on a city-wide scale using freely accessed multispectral medium-resolution (MR) Sentinel-2 images. Composite slum spectral indices (CSSIs) were initially proposed based on the shapes of spectral profiles of slums and nonslums and directly represent slum characteristics. Specifically, CSSI-1 denotes the normalized difference between the shortwave infrared bands and the red edge band, while CSSI-2 denotes the normalized difference between the blue band and the green band. Furthermore, two methods were developed to test the effectiveness of CSSIs on slum mapping, i.e., the threshold-based method and the machine learning (ML)-based method. Experimental results show that the threshold-based method and the ML-based method achieve intersection over unions (IoU) of 43.89% and 54.45% in Mumbai, respectively. The accuracies of our methods are comparable to or even higher than the accuracies reported by existing methods using HR/VHR images and transfer learning. The threshold-based method exhibits a promising performance in mapping slums larger than 5 ha, while the ML-based method refines mapping accuracies for slum pockets smaller than 5 ha. The threshold-based method and the ML-based method produced the slum map in Mumbai in 2 and 28 min, respectively. Our methods are suitable for rapid large-area slum mapping owing to the high data availability of Sentinel-2 images and high computational efficiency.

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Combining Local Knowledge with Object-Based Machine Learning Techniques for Extracting Informal Settlements from Very High-Resolution Satellite Data

Alrasheedi,

Dewan,

El-Mowafy

2024

Earth Syst Environ

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The classification of informal settlements using very high-resolution (VHR) satellite data and expert knowledge has proven very useful for urban planning. The objective of this work was to improve the accuracy of informal settlement classification within the city of Riyadh, Saudi Arabia. The analysis incorporated the use of expert knowledge (EK). Twenty unique indicators relevant to informal settlements were identified by experts familiar with these areas, and incorporated into the image classification process. Object-based image analysis (OBIA) was then used to extract informal settlement indicators from a VHR image. These indicators were used to classify the image utilising two machine learning (ML) algorithms, random forest (RF) and support vector machine (SVM) methods. A VHR image (e.g., Worldview 3) of the city was employed. A total of 6,000 sample points were randomly generated, with 1800 used for training the VHR image. The classification process was able to clearly distinguish the formal settlement areas from informal areas, road networks, vacant blocks, shaded areas, and vegetation features. The object-based RF technique provided an overall accuracy of 96% (kappa value of 95%), while OB-SVM provided an accuracy of 95% (kappa of 91%). The results demonstrated that object-based ML methods such as RF and SVM, when combined with EK, can effectively and efficiently distinguish informal settlements from other urban features. This technique has the potential to be very useful for mapping informal settlements.

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Using Local Knowledge and Remote Sensing in the Identification of Informal Settlements in Riyadh City, Saudi Arabia

Cited by 6 publications

References 60 publications

A Weighted Overlay Analysis for Assessing Urban Flood Risks in Arid Lands: A Case Study of Riyadh, Saudi Arabia

A Weighted Overlay Analysis for Assessing Urban Flood Risks in Arid Lands: A Case Study of Riyadh, Saudi Arabia

Mapping Slums in Mumbai, India, Using Sentinel-2 Imagery: Evaluating Composite Slum Spectral Indices (CSSIs)

Combining Local Knowledge with Object-Based Machine Learning Techniques for Extracting Informal Settlements from Very High-Resolution Satellite Data

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