A Comparison of Different Machine Learning Algorithms in the Classification of Impervious Surfaces: Case Study of the Housing Estate Fort Bema in Warsaw (Poland)

Abstract

The aim of this study is to extract impervious surfaces and show their spatial distribution, using different machine learning algorithms. For this purpose, geoprocessing and remote sensing techniques were used and three classification methods for digital images were compared, namely Support Vector Machines (SVM), Maximum Likelihood (ML) and Random Trees (RT) classifiers. The study area is one of the most prestigious and the largest housing estates in Warsaw (Poland), the Fort Bema housing complex, which is also an exemplary model for hydrological solutions. The study was prepared on the Geographic Information System platform (GIS) using aerial optical images, orthorectified and thus provided with a suitable coordinate system. The use of these data is therefore supported by the accuracy of the resulting infrared channel product with a pixel size of 0.25 m, making the results much more accurate compared to satellite imagery. The results of the SVM, ML and RT classifiers were compared using the confusion matrix, accuracy (Root Mean Square Error /RMSE/) and kappa index. This showed that the three algorithms were able to successfully discriminate between targets. Overall, the three classifiers had errors, but specifically for impervious surfaces, the highest accuracy was achieved with the SVM classifier (the highest percentage of overall accuracy), followed by ML and RT with 91.51%, 91.35% and 84.52% of the results, respectively. A comparison of the visual results and the confusion matrix shows that although visually the RT method appears to be the most detailed classification into pervious and impervious surfaces, the results were not always correct, e.g., water/shadow was detected as an impervious surface. The NDVI index was also mapped for the same spatial study area and its application in the evaluation of pervious surfaces was explained. The results obtained with the GIS platform, presented in this paper, provide a better understanding of how these advanced classifiers work, which in turn can provide insightful guidance for their selection and combination in real-world applications. The paper also provides an overview of the main works/studies dealing with impervious surface mapping, with different methods for their assessment (including the use of conventional remote sensing, NDVI, multisensory and cross-source data, ‘social sensing’ and classification methods such as SVM, ML and RT), as well as an overview of the research results.

Mapping of impervious surfaces with the use of remote sensing imagery: Support Vector Machines classification and GIS-based approach

“…SVM is a powerful tool for image classification because it is able to find the best separating hyperplane that maximizes the distance between different classes. SVM is widely used in impervious surface and land use/land cover (LULC) mapping because it can handle large datasets and complex features [14].…”

Analyzing Land Shape Typologies in South Korean Apartment Complexes Using Machine Learning and Deep Learning Techniques

“…Furthermore, research aimed at enhancing efficiency in architecture and urban management through machine learning includes studies on various approaches. These include investigations into detecting road potholes and their locations [18]; comparing the effectiveness of machine learning algorithms such as support vector machine (SVM), Maximum Likelihood (ML), and Random Trees (RT) for extracting impervious surfaces in residential complexes and illustrating their spatial distribution [19]; and developing a machine learning technique called Building Detection with Shadow Verification (BDSV) based on high-resolution satellite images to automatically detect buildings within urban areas [20]. These studies explore the applicability of machine learning in the field of architecture and urban planning and contribute to solving relevant problems.…”

Detecting Changes in Impervious Surfaces Using Multi-Sensor Satellite Imagery and Machine Learning Methodology in a Metropolitan Area

“…In recent years, the use of advanced machine learning techniques has significantly improved the detection of impervious surfaces from satellite imagery [26,27]. Techniques such as Classification and Regression Trees (CARTs) [28][29][30], the Random Forest (RF) method [31,32], Artificial Neural Networks (ANNs) [33][34][35][36], and Support Vector Machines (SVMs) [37][38][39][40] are examples of these methodologies.…”