Impervious Surface Extraction Research Articles

A new classification scheme for urban impervious surface extraction from UAV data

A new classification scheme for urban impervious surface extraction from UAV data

Optical–SAR Data Fusion Based on Simple Layer Stacking and the XGBoost Algorithm to Extract Urban Impervious Surfaces in Global Alpha Cities

This study proposes a fusion approach to enhancing urban remote sensing applications by integrating SAR (Sentinel-1) and optical (Landsat-8) satellite datasets. The fusion technique combines feature-based fusion and simple layer stacking (SLS) to improve the accuracy of urban impervious surface (UIS) extraction. SAR textures and modified indices are used for feature extraction, and classification is performed using the XGBoost machine learning algorithm in Python and Google Earth Engine. The study focuses on four global cities (New York, Paris, Tokyo, and London) with heterogeneous climatic zones and urban dynamics. The proposed method showed significant results. The accuracy assessment using random validation points shows an overall accuracy of 86% for UIS classification with the SLS method, outperforming single-data classification. The proposed approach achieves higher accuracy (86%) compared to three global products (ESA, ESRI, and Dynamic World). New York exhibits the highest overall accuracy at 88%. This fusion approach with the XGBoost classifier holds potential for new applications and insights into UIS mapping, with implications for environmental factors such as land surface temperature, the urban heat island effect, and urban pluvial flooding.

Comparison of Random Forest and XGBoost Classifiers Using Integrated Optical and SAR Features for Mapping Urban Impervious Surface

The integration of optical and SAR datasets through ensemble machine learning models shows promising results in urban remote sensing applications. The integration of multi-sensor datasets enhances the accuracy of information extraction. This research presents a comparison of two ensemble machine learning classifiers (random forest and extreme gradient boost (XGBoost)) classifiers using an integration of optical and SAR features and simple layer stacking (SLS) techniques. Therefore, Sentinel-1 (SAR) and Landsat 8 (optical) datasets were used with SAR textures and enhanced modified indices to extract features for the year 2023. The classification process utilized two machine learning algorithms, random forest and XGBoost, for urban impervious surface extraction. The study focused on three significant East Asian cities with diverse urban dynamics: Jakarta, Manila, and Seoul. This research proposed a novel index called the Normalized Blue Water Index (NBWI), which distinguishes water from other features and was utilized as an optical feature. Results showed an overall accuracy of 81% for UIS classification using XGBoost and 77% with RF while classifying land use land cover into four major classes (water, vegetation, bare soil, and urban impervious). However, the proposed framework with the XGBoost classifier outperformed the RF algorithm and Dynamic World (DW) data product and comparatively showed higher classification accuracy. Still, all three results show poor separability with bare soil class compared to ground truth data. XGBoost outperformed random forest and Dynamic World in classification accuracy, highlighting its potential use in urban remote sensing applications.

Extracting urban impervious surface based on optical and SAR images cross-modal multi-scale features fusion network

ABSTRACT Monitoring the spatiotemporal distribution of urban impervious surface is an essential indication for measuring the urbanization process. Optical and synthetic aperture radar (SAR) images are key data sources in urban impervious surface extraction. Because cities are highly heterogeneous scenes, using a single data source to extract urban impervious surface encounters a bottleneck in accuracy enhancement due to the limitation of a single-modal feature representation, which can be helped to overcome by fusing the two data. However, existing researches have primarily done fusion directly by layer stacking for urban impervious surface, without taking into account the modal differences between optical and SAR (optical-SAR) images, and thus cannot better realize complementarity between the two. As a result, this study proposes a cross-modal multi-scale features fusion segmentation network (CMFFNet) for optical-SAR images for urban impervious surface extraction. A cross-modal features fusion (CMFF) module is designed in the proposed CMFFNet to fully exploit the complementary information of optical-SAR images. Additionally, we propose a multi-scale features fusion (MSFF) module to fuse multi-scale features of optical-SAR images, taking into account the multi-scale characteristics of urban impervious surface. The results of the experiments demonstrate that the proposed CMFFNet outperforms current mainstream methods for extracting impervious surface.

MSTCA-Net: A Novel Impervious Surface Extraction Method Based on a Multistage Transformer

MSTCA-Net: A Novel Impervious Surface Extraction Method Based on a Multistage Transformer

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

This study utilizes multi-sensor satellite images and machine learning methodology to analyze urban impervious surfaces, with a particular focus on Nanchang, Jiangxi Province, China. The results indicate that combining multiple optical satellite images (Landsat-8, CBERS-04) with a Synthetic Aperture Radar (SAR) image (Sentinel-1) enhances detection accuracy. The overall accuracy (OA) and kappa coefficients increased from 84.3% to 88.3% and from 89.21% to 92.55%, respectively, compared to the exclusive use of the Landsat-8 image. Notably, the Random Forest algorithm, with its unique dual-random sampling technique for fusing multi-sensor satellite data, outperforms other machine learning methods like Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Classification and Regression Trees (CARTs), Maximum Likelihood Classification (Max-Likelihood), and Minimum Distance Classification (Min-Distance) in impervious surface extraction efficiency. With additional satellite images from 2015, 2017, and 2020, the impervious surface changes are tracked in the Nanchang metropolitan region. From 2015 to 2021, they record a notable increase in impervious surfaces, signaling a quickened urban expansion. This study observes several impervious surface growth patterns, such as a tendency to concentrate near rivers, and larger areas in the east of Nanchang. While the expansion was mainly southward from 2015 to 2021, by 2021, the growth began spreading northward around the Gan River basin.

Identification of Waterlogging-Prone Areas in Nanning from the Perspective of Urban Expansion

The objective of this study was to discern the spatial and temporal patterns of areas in Nanning that are susceptible to waterlogging, particularly during various phases of urban expansion. Furthermore, this study presents a proposal outlining strategies aimed at preventing and controlling waterlogging. These strategies are based on the integration of the concepts of sponge city and resilient city construction. This study employed remote sensing (RS) and geographic information system (GIS) techniques to provide technical support. The supervised classification method and normalized difference index method were utilized to compare and extract impervious surfaces in Nanning from 2013 to 2020. The present investigation utilized the acquired impervious surfaces to compute the fractal dimension as a weighting factor, incorporating a digital elevation model (DEM) for the purpose of conducting a hydrological analysis in ArcGIS. Based on the findings of the study, several conclusions can be derived. The following conclusions can be drawn from the study: (1) The fractal dimension of Nanning varied over the study period, with values of 1.32, 1.41, and 1.58 in 2013, 2017, and 2020, respectively. The distribution of impervious surfaces showed a decreasing trend from the city center to the periphery. Urban planning and construction activities have significantly influenced the distribution of impervious surfaces, resulting in a progressively more complex and unstable structure. (2) From 2013 to 2020, the urban expansion fractal dimension increased from 1.32 to 1.58, indicating a decrease in the stability of impervious surfaces. The areas with higher concentrations of impervious surfaces coincided with frequent waterlogging-prone areas. Furthermore, the distribution of waterlogging-prone points transformed from a concentrated pattern to a scattered one. (3) In terms of waterlogging prevention and control strategies, the old urban areas are recommended to be transformed into sponge city projects, and the new development areas are planned, designed and implemented with the concept of “resilience”.

A 10-m resolution impervious surface area map for the greater Mekong subregion from remote sensing images

High-resolution and multi-temporal impervious surface area maps are crucial for capturing rapidly developing urbanization patterns. However, the currently available relevant maps for the greater Mekong subregion suffer from coarse resolution and low accuracy. Addressing this issue, our study focuses on the development of accurate impervious surface area maps at 10-m resolution for this region for the period 2016–2022. To accomplish this, we present a new machine-learning framework implemented on the Google Earth Engine platform that merges Sentinel-1 Synthetic Aperture Radar images and Sentinel-2 Multispectral images to extract impervious surfaces. Furthermore, we also introduce a training sample migration strategy that eliminates the collection of additional training samples and automates multi-temporal impervious surface area mapping. Finally, we perform a quantitative assessment with validation samples interpreted from Google Earth. Results show that the overall accuracy and kappa coefficient of the final impervious surface area maps range from 92.75% to 92.93% and 0.854 to 0.857, respectively. This dataset provides comprehensive measurements of impervious surface coverage and configuration that will help to inform urban studies.

Extraction and Spatiotemporal Evolution Analysis of Impervious Surface and Surface Runoff in Main Urban Region of Hefei City, China

The biophysical composition index (BCI)-based linear spectral mixture model (LSMM) is used in this study to extract the impervious surface (IS), vegetation, and soil coverage of the main urban region (MUR) of Hefei City over the 2001–2021 period. In addition, the Soil Conservation Service-Curve Number (SCS-CN) model is first applied to simulate the surface runoff (SR) in the MUR of Hefei City over the past 21 years, then assessed for simulation accuracy using typical waterlogging points in the study area. On this basis, the spatiotemporal evolution of IS and SR and their relationships in the MUR of Hefei City are investigated and discussed in this study. The obtained results showed that (1) the root-mean-square error (RMSE), mean absolute error (MAE), and systematic error (SE) values of the BCI index-based LSMM are smaller than those of the LSMM, demonstrating a higher extraction accuracy of urban IS extraction of the BCI index-based LSMM. (2) The IS area of the MUR of Hefei City exhibits an increasing trend from 107.555 km2 in 2001 to 387.660 km2 in 2021. In addition, the change rate and change intensity values indicate an increasing–decreasing–increasing trend. The highest change rate and change intensity values are 24.839 km2/year and 23.094%, respectively, and were observed in the 2001–2005 period. (3) The simulated SR (165–195 mm) in the MUR of Hefei City demonstrates an increasing trend in the 2001–2021 period at a rainfall intensity value of 200 mm/d. In addition, the simulated SR amount in the central area exhibits slight changes, while that in the surrounding areas shows substantial variations. (4) The distribution of IS and SR in the MUR of Hefei City reveals strong directional variations, which are all affected by geographical conditions. The IS coverage and SR show high positive correlation coefficients in different years. (5) The present study provides primary data for effective urban planning, water resources management and regulation, and disaster prevention and mitigation in Hefei City, as well as a scientific reference for future studies on urban IS, SR, and their quantitative relationships in other regions.

Mapping 30m China annual impervious surface from 1992 to 2021 via a multi-level classification approach

ABSTRACT Accurately mapping impervious surface has great significance and application for urbanization monitoring, urban sustainable planning, ecosystem protection, etc. In this study, an automatic and high-performance multi-level classification approach based on time-series similarity, spectral feature, and logical reasoning was proposed to accurately map China's annual impervious surface dataset (CAID) from 1992 to 2021 using Landsat time-series images. Firstly, the synthesized image of 30-year night-time light maximum value was used to mask the areas where humans are inactive, which will preliminarily screen out the impervious surface and mitigate the computational volume. Then, similarity measurement based on dynamic time warping was performed, respectively, for periodic index series, which were weighted by an amplitude scale factor. Furthermore, similarity measurement, spectral features, and logical reasoning of long-time-series land cover type were combined to construct a multi-level approach to mapping impervious surface. Finally, for the computational efficiency of pixel-level time-series classification, we built a distributed parallel high-performance computing framework to fully utilize local computing resources for efficiently developing CAID. Using 58,839 validation samples based on stratified random sampling, the overall accuracy and F1-score of CAID were 90.05% and 88.49%. A comparison with five existing 30 m impervious surface products further indicated that our CAID achieved better impervious surface extraction accuracy and was capable of finely delineating impervious surface landscapes. Therefore, it was concluded that our CAID is an accurate and credible product that can provide an important data base for urbanization and urban-related research in China.

Impervious Surface Mapping Based on Remote Sensing and an Optimized Coupled Model: The Dianchi Basin as an Example

Accurately extracting impervious surfaces (IS) and continuously monitoring their dynamics are crucial practices for promoting sustainable development in regional ecological environments and resources. In this context, we conducted experiments to extract IS of the Dianchi Lake Basin by utilizing various features extracted from remote sensing images and applying three different machine learning algorithms. Through this process, we obtained the optimal combination of features and a machine learning algorithm. Utilizing this model, our objective is to map the evolution of IS in the Dianchi Lake Basin, from 2000 to 2022, and analyze its dynamic changes. Our results showed the following: (1) The optimal model for IS extraction in the Dianchi Lake Basin was IMG-SPESVM based on the support vector machine, remote sensing images, and spectral features. (2) From 2000 to 2022, the spatial distribution and shape of the IS in the Dianchi Lake Basin changed significantly, but they all developed in the area around Dianchi Lake. (3) From 2000 to 2015, the rate of expansion of IS gradually accelerated, while from 2015 to 2022, it contracted. (4) From 2000 to 2022, the center of mass of IS moved to the northeast, and the standard deviation ellipse shifted greatly in the south–north direction. (5) Natural factors negatively affected the expansion of IS, while social factors positively affected the distribution of the IS.

An Object-Oriented Deep Multi-Sphere Support Vector Data Description Method for Impervious Surfaces Extraction Based on Multi-Sourced Data

The effective extraction of impervious surfaces is critical to monitor their expansion and ensure the sustainable development of cities. Open geographic data can provide a large number of training samples for machine learning methods based on remote-sensed images to extract impervious surfaces due to their advantages of low acquisition cost and large coverage. However, training samples generated from open geographic data suffer from severe sample imbalance. Although one-class methods can effectively extract an impervious surface based on imbalanced samples, most of the current one-class methods ignore the fact that an impervious surface comprises varied geographic objects, such as roads and buildings. Therefore, this paper proposes an object-oriented deep multi-sphere support vector data description (OODMSVDD) method, which takes into account the diversity of impervious surfaces and incorporates a variety of open geographic data involving OpenStreetMap (OSM), Points of Interest (POIs), and trajectory GPS points to automatically generate massive samples for model learning, thereby improving the extraction of impervious surfaces with varied types. The feasibility of the proposed method is experimentally verified with an overall accuracy of 87.43%, and its superior impervious surface classification performance is shown via comparative experiments. This provides a new, accurate, and more suitable extraction method for complex impervious surfaces.

Emerging Issues in Mapping Urban Impervious Surfaces Using High-Resolution Remote Sensing Images

Urban impervious surface (UIS) is a key parameter in climate change, environmental change, and sustainability. UIS extraction has been evolving rapidly in the past decades. However, high-resolution impervious surface mapping is a long-term need. There is an urgent requirement for impervious surface mapping from high-resolution remote sensing imagery. In this paper, we compare current extraction methods in terms of extraction units and extraction models and summarize their strengths and limitations. We discuss the challenges in impervious surface estimation from high spatial resolution remote sensing imagery in terms of selection of spatial resolution, spectral band, and extraction method. The uncertainties caused by clouds and snow, shadows, and vegetation occlusion are also analyzed. Automated sample labeling and remote sensing domain knowledge are the main directions in impervious surface extraction using deep learning methods. We should also focus on using continuous time series of high-resolution imagery and multi-source satellite imagery for dynamic monitoring of impervious surfaces.

Extraction of impervious surface with Landsat based on machine learning in Chengdu urban, China

In the process of urbanization, materials such as steel and concrete are widely used in urban construction such as road paving and house building, resulting in an increase in impervious surface area in cities. Rapid urbanization in China today has led to a huge increase in impervious surface area, which has increased the risk of urban waterlogging. Therefore, there are more reasons to conduct research on impervious surface and explore the relationship between impervious surface and urban waterlogging. This research has been done to obtain the optimal model for extracting impervious surface in Chengdu urban area and to explore the correlation between impervious surface changes and urban waterlogging in the same area. In order to obtain the optimal model, five different machine learning and deep learning algorithms such as Naive Bayes (NB), Convolutional Neural Network (CNN), Support Vector Machine (SVM), Capsules Network (CapsNet) and Random Forest (RF) have been used to extract the urban impervious surface in this study. The accuracy of the classification models is evaluated using the overall accuracy (OA), kappa coefficient (KC), and macro F1 (F1). It is found that CNN is the best option to extract the urban impervious surface of Chengdu with an OA of 0.7809. In addition, based on the optimal model to extract the impervious surface of Chengdu, a partial correlation analysis was conducted between urban waterlogging and urban impervious in the study area, it was concluded that there was a positive correlation between the two with a partial correlation coefficient of 0.979.

Urban Impervious Surface Extraction Based on Deep Convolutional Networks Using Intensity, Polarimetric Scattering and Interferometric Coherence Information from Sentinel-1 SAR Images

Urban impervious surface area is a key indicator for measuring the degree of urban development and the quality of an urban ecological environment. However, optical satellites struggle to effectively play a monitoring role in the tropical and subtropical regions, where there are many clouds and rain all year round. As an active microwave sensor, synthetic aperture radar (SAR) has a long wavelength and can penetrate clouds and fog to varying degrees, making it very suitable for monitoring the impervious surface in such areas. With the development of SAR remote sensing technology, a more advanced and more complex SAR imaging model, namely, polarimetric SAR, has been developed, which can provide more scattering information of ground objects and is conducive to improving the extraction accuracy of impervious surface. However, the current research on impervious surface extraction using SAR data mainly focuses on the use of SAR image intensity or amplitude information, and rarely on the use of phase and polarization information. To bridge this gap, based on Sentinel-1 dual-polarized data, we selected UNet, HRNet, and Deeplabv3+ as impervious surface extraction models; and we input the intensity, coherence, and polarization features of SAR images into the respective impervious surface extraction models to discuss their specific performances in urban impervious surface extraction. The experimental results show that among the intensity, coherence, and polarization features, intensity is the most useful feature in the extraction of urban impervious surface based on SAR images. We also analyzed the limitations of extracting an urban impervious surface based on SAR images, and give a simple and effective solution. This study can provide an effective solution for the spatial-temporal seamless monitoring of an impervious surface in cloudy and rainy areas.

Composite extraction index to enhance impervious surface information in remotely sensed imagery

Fast and accurate impervious surface extraction is important to monitor urban dynamics and ensure people's well-being. In order to improve the extraction accuracy of impervious surface, a composite impervious surface extraction index CBI based on NDBI, NDVI and MNDWI is proposed in the paper. The impervious surface extraction images in different land cover areas were compared with the NDBI and NDISI, respectively, and were assessed by using visual and statistical analysis. The results show that CBI has good robustness, less affected by the type of ground objects and sensors. The Kappa coefficients of CBI are the highest in different sensors and OLI images with different land cover areas, and the overall accuracy is above 91%. In the area with high coverage of vegetation and water, the extraction accuracy of CBI was significantly improved. CBI is more suitable for areas with high coverage of vegetation and water and has an improved Kappa coefficient of 0.1116 than that of the areas containing only a small amount of water and vegetation. As the NDISI algorithm uses the thermal infrared band, when the area of the study area is small, its extraction accuracy is low, indicating that the accuracy of the algorithm is affected by the size of the study area.

Dynamic Changes, Spatiotemporal Differences, and Ecological Effects of Impervious Surfaces in the Yellow River Basin, 1986–2020

Impervious surfaces (IS) are one of the most important components of the earth’s surface, and understanding how IS have expanded is vital. However, few studies on IS or urbanization have focused on the cradle of the Chinese nation—the Yellow River Basin (YRB). In this study, the Random Forest and Temporal Consistency Check methods were employed to generate long-term maps of IS in the YRB based on Landsat imagery. To explore the dynamics and differences in IS, we developed a spatiotemporal analysis and put forward regional comparisons between different research units of the YRB. We documented the remote sensing-based ecological index (RSEI) in multiple circular zones to discuss the ecological effects of the expansion of IS. The IS extraction strategy achieved excellent performance, with an average overall accuracy of 90.93% and kappa coefficient of 0.79. The statistical results demonstrated that the spatial extent of IS areas in the YRB increased to 18,287.36 km2 in 2020 which was seven times more than that in 1986, at rates of 166 km2/a during 1986–2001, 365 km2/a during 2001–2010, and 1044 km2/a during 2011–2020. Our results indicated that the expansion and densification of IS was slow in core urban areas with high initial IS fraction (ISF), significant in the suburban or rural areas with low initial ISF, and obvious but not significant in the exurb rural or depopulated areas with an initial ISF close to 0. The multiyear RSEI indicated that environmental quality of the YRB had improved with fluctuations. The ecological effects of the impervious expansion slightly differed in urban core areas versus outside these areas. When controlling the urban boundary, more attention should be paid to the rational distribution of ecologically important land. These results provide comprehensive information about IS expansion and can provide references for delineating urban growth boundaries.

CroFuseNet: A Semantic Segmentation Network for Urban Impervious Surface Extraction Based on Cross Fusion of Optical and SAR Images

The fusion of optical and synthetic aperture radar (SAR) images is a promising method to extract urban impervious surface (IS) accurately. Previous studies have shown that the feature-level fusion of optical and SAR images can significantly improve IS extraction. However, they generally use simple layer stacking for features fusion, ignoring the interaction between optical and SAR images. Besides, most of the features they used are shallow features manually extracted, such as texture and geometric features, lacking the use of high-level semantic features of images. The lack of publicly available IS datasets is considered as an obstacle that prevents the extensive use of deep learning models in IS extraction. Therefore, this study first creates an open and accurate IS dataset based on optical and SAR images, and then proposes a semantic segmentation network based on cross fusion of optical and SAR images features, namely CroFuseNet, for IS extraction. In CroFuseNet, we design a cross fusion module (CFM) to fuse features of optical and SAR images to achieve better complementarity between the two types of images. And we propose a multimodal features aggregation (MFA) module to aggregate specific high-level features from optical and SAR images. To validate the proposed CroFuseNet, we compare it with two classical machine learning algorithms and four state-of-the-art deep learning models. The proposed model has the highest accuracy, with OA, MIoU and F1-Score of 97.77%, 0.9495 and 0.9770 respectively. The quantitative and qualitative experimental results demonstrate that the proposed model is superior to these comparative algorithms.

Improving urban impervious surface extraction by synergizing hyperspectral and polarimetric radar data using sparse representation

Accurate extraction of urban impervious surface (UIS) is essential for urban planning and environmental monitoring. However, multispectral remote sensing data for UIS extraction suffers from the inter-class spectral confusions, e.g. UIS and bare soil, and intra-class variations of sub-class UIS. Hyperspectral and full/dual-polarization synthetic aperture radar (full/dual PolSAR) data provide opportunities for reducing such confusions and have potential for fine UIS mapping, i.e., roads, buildings, and grounds. In this study, we first investigated the hyperspectral data (Gaofen-5) capability to reduce the intra/inter-class misclassification in comparison with multispectral data (Landsat-8). Then, we explored contributions of synergistically using full and dual PolSAR (ALOS-2 and Sentinel-1) with hyperspectral and multispectral data using optical-SAR sparse representation classification (OSSRC). Results showed that both the hyperspectral and the SAR polarization features helped better delineation between UIS and bare soil, and sub-class UIS (roads and buildings). The relative contribution of PolSAR was higher in multispectral data than in hyperspectral data, with full PolSAR contributed significantly. The combined hyperspectral and full PolSAR data using OSSRC delivered the best result, with an overall accuracy higher than 90%. The results indicate the promising capability of synergizing hyperspectral and full/dual PolSAR data for improving UIS extraction from advanced satellite data.

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

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.