Identification of Urban Slums Using Classification Algorithms—A Geospatial Approach

Priyadarshini, K. Nivedita; Sivashankari, V.; Shekhar, Sulochana

doi:10.1007/978-3-030-37393-1_21

Cited by 1 publication

(2 citation statements)

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“…of construction identification. A comparison of pixel-based and object-based methods in this study produced similar results to those reported by Priyadarshini et al [26], in that object-based methods were found to be more accurate in identifying new constructions, with the SVM method giving the highest accuracy for classifying the land cover in Sentinel-2 images. The results in this study were also similar to those reported by Phiri et al [23], who compared different preparation methods for land cover maps using Sentinel-2 images.…”

Section: Discussionsupporting

confidence: 87%

“…Boonpook et al [24] concluded that UAV images are a suitable way to identify new constructions around rivers, while Liu et al [25] found that the integration of Digital Surface Models (DSM) and UAV images increases the accuracy of construction identification. Priyadarshini et al [26] identified suburban areas around a city using UAV images and the random forest, maximum likelihood, Mahalanobis and neural net algorithms. Their study showed that object-based methods are more accurate than other methods in identifying constructions.…”

Section: Introductionmentioning

confidence: 99%

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Investigating the Ability to Identify New Constructions in Urban Areas Using Images from Unmanned Aerial Vehicles, Google Earth, and Sentinel-2

et al. 2022

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One of the main problems in developing countries is unplanned urban growth and land use change. Timely identification of new constructions can be a good solution to mitigate some environmental and social problems. This study examined the possibility of identifying new constructions in urban areas using images from unmanned aerial vehicles (UAV), Google Earth and Sentinel-2. The accuracy of the land cover map obtained using these images was investigated using pixel-based processing methods (maximum likelihood, minimum distance, Mahalanobis, spectral angle mapping (SAM)) and object-based methods (Bayes, support vector machine (SVM), K-nearest-neighbor (KNN), decision tree, random forest). The use of DSM to increase the accuracy of classification of UAV images and the use of NDVI to identify vegetation in Sentinel-2 images were also investigated. The object-based KNN method was found to have the greatest accuracy in classifying UAV images (kappa coefficient = 0.93), and the use of DSM increased the classification accuracy by 4%. Evaluations of the accuracy of Google Earth images showed that KNN was also the best method for preparing a land cover map using these images (kappa coefficient = 0.83). The KNN and SVM methods showed the highest accuracy in preparing land cover maps using Sentinel-2 images (kappa coefficient = 0.87 and 0.85, respectively). The accuracy of classification was not increased when using NDVI due to the small percentage of vegetation cover in the study area. On examining the advantages and disadvantages of the different methods, a novel method for identifying new rural constructions was devised. This method uses only one UAV imaging per year to determine the exact position of urban areas with no constructions and then examines spectral changes in related Sentinel-2 pixels that might indicate new constructions in these areas. On-site observations confirmed the accuracy of this method.

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Section: Discussionsupporting

confidence: 87%