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

Abstract

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.

Identifying and Monitoring Gardens in Urban Areas Using Aerial and Satellite Imagery

“…The object-oriented methods used in this research, from the most to the least accurate, include SVM, RF, KNN, and Bayes. Therefore, the SVM method was used to classify aerial images in this research [21]. In the present study, the DSM image and NDVI were used to increase the accuracy of aerial image classification.…”

“…In this way, it is possible to effectively identify new constructions with the lowest cost and time for image processing. The full description of this method is provided in the study by Aliabad et al [21].…”

“…Compared to satellite images, the images prepared using UAVs have advantages that include better spatial resolution, the ability to take pictures whenever needed, and the ability to take pictures in any weather conditions, such as clouds [18,19]. Through the fusion of satellite images and UAV images, it is possible to achieve images with high spatial and spectral resolution [20,21].…”

Evaluation of the ability to measure morphological structures of plants obtained from tissue culture applying image processing techniques

“…(Sitanggang, 2023) which stated that the KNN method is more accurate than the SVM method in the classi cation. Arabi Aliabad et al (2022) consider KNN and SVM among object-oriented classi cation methods more accurate than others. Phiri et al (2020) used the results of 25 studies in classifying Sentinel-2 images and reported that the SVM and Bayes method has better accuracy for classi cation purposes.…”