Urban Land Cover Mapping Research Articles

Automated Generation of Urban Land Cover Maps and Their Enhancement and Regularization

Automated Generation of Urban Land Cover Maps and Their Enhancement and Regularization

Detailed height mapping of trees and buildings (HiTAB) in Chicago and its implications to urban climate studies

Abstract The vertical dimensions of urban morphology, specifically the heights of trees and buildings, exert significant influence on wind flow fields in urban street canyons and the thermal environment of the urban fabric, subsequently affecting the microclimate, noise levels, and air quality. Despite their importance, these critical attributes are less commonly available and rarely utilized in urban climate models compared to planar land use and land cover data. In this study, we explicitly mapped the height of trees and buildings (HiTAB) across the city of Chicago at 1 m spatial resolution using a data fusion approach. This approach integrates high-precision light detection and ranging (LiDAR) cloud point data, building footprint inventory, and multi-band satellite images. Specifically, the digital terrain and surface models were first created from the LiDAR dataset to calculate the height of surface objects, while the rest of the datasets were used to delineate trees and buildings. We validated the derived height information against the existing building database in downtown Chicago and the Meter-scale Urban Land Cover map from the Environmental Protection Agency, respectively. The co-investigation on trees and building heights offers a valuable initiative in the effort to inform urban land surface parameterizations using real-world data. Given their high spatial resolution, the height maps can be adopted in physical-based and data-driven urban models to achieve higher resolution and accuracy while lowering uncertainties. Moreover, our method can be extended to other urban regions, benefiting from the growing availability of high-resolution urban informatics globally. Collectively, these datasets can substantially contribute to future studies on hyper-local weather dynamics, urban heterogeneity, morphology, and planning, providing a more comprehensive understanding of urban environments.

Very fine spatial resolution urban land cover mapping using an explicable sub-pixel mapping network based on learnable spatial correlation

Sub-pixel mapping is the prevailing approach for dealing with the mixed pixel effect in urban land use/land cover classification, by reconstructing the sub-pixel-scale distribution inside each mixed-pixel based on spatial autocorrelation. However, 1) traditional spatial autocorrelation is limited to a local window, which cannot model the teleconnection between two locations or objects that are far apart and 2) autocorrelation is based on the idea of “the more proximate, the more similar”, which relies on a distance-weight decay parameter and cannot characterize the rich variety of mutual information in spatially heterogenous areas in urban. In this research, we develop and demonstrate a learnable correlation-based sub-pixel mapping (LECOS) method. 1) We use the “mutual retrieval” mechanism of the self-attention operation to model teleconnections that enable more distant locations or objects to be mutually correlated and 2) we design a parameter-free “self-attention in self-attention” operation to learn adaptively the diverse global correlation patterns between pixel and sub-pixel. The learned spatial correlations are then used for reasoning the sub-pixel-scale distribution of each class. We validated our method on the most challenging public datasets of urban scenes, which exhibit considerable spatial heterogeneity with complex structures and broken objects. The learned building-tree, building-road and road-tree correlation patterns contributed most to the sub-pixel reconstruction result of the urban scenes, consistent with in-situ reference data. We further explored the model's explicability in a large-area of several metropolises in China, by mapping land cover in these cities at a 2 m very fine spatial resolution using 10 m Sentinel-2 input images, and found that the derived result not only revealed rich urban spatial heterogeneity, but also that the learned correlation was indicative of urban pattern dynamics, suggesting the potential for greater understanding of issues such as urban fairness, accessibility, human exposure and sustainability.

Urban Land Use and Land Cover Mapping

The Brazilian urbanization process produced a complex urban space, with a variety of urban land use and cover as a result. The study of these forms through a classification system is essential, but most current systems don’t capture this complexity, condensing it. Urban forms are difficult to distinguish and classify, resulting in the need for a system with a high degree of detail, for a more accurate urban planning. The objective of this work is to propose a classification system for urban land use and cover, which can demonstrate the multiplicities through remote sensing, using data from CBERS 04A satellite. The methodology surveys the visual elements of remote sensing images, through visual interpretation, relating them to each proposed use and cover class. With this, a classification system was developed that covers the urban space in 17 classes, being an effective way to raise information about the different urban forms.

Examining effect of super-resolution on AVIRIS-NG data: A precursor to generation of large-scale urban material and natural cover maps

The two-phase Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRIS-NG) campaign over India has generated datasets of numerous natural and artificial surroundings with high signal-to-noise ratios and higher spatial and spectral consistencies. However, the first phase AVIRIS-NG datasets of urban areas have 8.1 m spatial resolution, which is insufficient for producing level-4 urban material and natural cover maps required to model Earth’s processes at the micro-scale or constantly monitor urban processes. The current work addresses this drawback by demonstrating a part of the proposed proof-of-concept approach on the AVIRIS-NG dataset of Ahmedabad, India, wherein super-resolution (SR) is proposed to enhance the hyperspectral image’s spatial resolution and preserve its rich spectral content at the targeted spatial scale before utilizing it for producing detailed urban land cover maps. A step-wise account of the SR process has been provided herein to aid the reader in reproducing these experiments. Super-resolved products generated using four SR algorithms have been compared and validated using a composite framework based on processing time, qualitative visual investigation of selected image patches and spectral profiles, re-interpretation of Wald’s reduced resolution protocol and full-reference image quality metrics. Results suggest the AVIRIS-NG dataset’s successful spectral and spatial fidelity at a larger spatial scale, albeit varying in each super-resolved output. Sparse regression and natural prior (SRP), and anchored neighbourhood regression (ANR) produce the best and poorest super-resolved outputs among the tested SR algorithms. Although gradient profile prior (GPP) generated super-resolved product is the second best, it is also the most computationally expensive algorithm. An additional yardstick in the form of a spectral similarity analysis of randomly selected unknown spectral signatures on super-resolved output against a reference spectral library has been proposed to ascertain spectral content preservation further before utilizing the super-resolved product for urban information extraction. This exercise’s findings reveal the presence of the target urban land cover feature at the unknown pixels with matching scores of greater than 0.5 in both best and worst super-resolved products, thereby advocating the extension of the proposed proof-of-concept to recently launched spaceborne hyperspectral missions’ datasets.

Satellite Image Processing Using Radiometric Resolution & Spectral Resolution

Abstract: This paper presents a detailed comparison of various image processing techniques for analysing satellite images. The satellite images are large in size, acquired from long distances and are affected by noise and other environmental conditions. Hence it is necessary to process them so that they can be used by the researchers for analysis. Spectral resolution basically is to measure changes in things that impact our environments like water quality or vegetation etc. Satellite images are widely used in many real time applications such as in agriculture land detection, navigation and in geographical information systems. In this paper, a review of spectral resolution requirements for urban mapping evaluated how spectral resolution of high-spatial resolution optical remote sensing data influences detailed mapping of urban land cover. A comprehensive regional spectral library and low altitude data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) were used to characterize the spectral properties of urban land cover.

An Urban Scheme for the ECMWF Integrated Forecasting System: Global Forecasts and Residential CO2 Emissions

AbstractThe impact of urbanization on local weather patterns affects over half the global population. Global numerical weather prediction systems have reached a resolution at which urban conurbations can be spatially resolved, justifying their representation within land surface parameterizations with the aim of improving local predictions. Additionally, real‐time atmospheric monitoring of trace gas emissions can utilize weather variables relevant for urban areas. We investigated whether a simple single‐layer urban canopy scheme can be used within a global forecast model to jointly improve predictions of near‐surface weather variables and residential CO2 emissions. The scheme has been implemented in the Integrated Forecast System used operationally at the European Centre for Medium‐Range Weather Forecasts running at ∼9 km horizontal resolution. First, we selected a suitable urban land cover map (ECOCLIMAP‐SG) based on comparisons with regional data and land surface temperature MODIS retrievals. The urban scheme is verified by providing improved 2 m temperature (∼10%) and 10 m wind (∼17%) RMSE values for both summer and winter months around urban environments. The influence of the scheme was most noticeable at night. Additionally, we have implemented a simple temperature‐dependent residential emissions model to calculate real‐time CO2 heating emissions. These were validated against existing offline products, national reporting and by comparing atmospheric simulations with total column CO2 observations. The results show an improved temporal variability of emissions, which arise from synoptic scale temperature changes. Given the improved predictability from the urban scheme for both weather and emissions, it will be operationally implemented in an upcoming model cycle.

Exploring the Addition of Airborne Lidar-DEM and Derived TPI for Urban Land Cover and Land Use Classification and Mapping

The classification and mapping accuracy of urban land cover and land use has always been a critical topic and several auxiliary data have been used to improve the classification accuracy. However, to the best of our knowledge, there is limited knowledge of the addition of airborne Light Detection and Ranging (lidar)-Digital Elevation Model (DEM) and Topographic Position Index (TPI) for urban land cover and land use classification and mapping. The aim of this study was to explore the addition of airborne lidar-DEM and derived TPI to reflect data of Landsat Operational Land Imager (OLI) in improving the classification accuracy of urban land cover and land use map- ping. Specifically, this study explored the mapping accuracies of urban land cover and land use classifications derived using: 1) standalone Landsat OLI satellite data; 2) Landsat OLI with acquired airborne lidar-DEM ; 3) Landsat OLI with TPI ; and 4) Landsat OLI with airborne lidar-DEM and derived TPI. The results showed that the addition of airborne lidar-DEM and TPI yielded the best overall urban land cover and land use classification accuracy of about 88%. The findings in this study demonstrated that both lidar-DEM and TPI had a positive impact in improving urban land cover and land use classification.

Multi-Level Dynamic Analysis of Landscape Patterns of Chinese Megacities during the Period of 2016–2021 Based on a Spatiotemporal Land-Cover Classification Model Using High-Resolution Satellite Imagery: A Case Study of Beijing, China

In today’s accelerating urbanization process, timely and effective monitoring of land-cover dynamics, landscape pattern analysis, and evaluation of built-up urban areas (BUAs) have important research significance and practical value for the sustainable development, planning and management, and ecological protection of cities. High-spatial-resolution remote sensing (HRRS) images have the advantages of high-accuracy Earth observations, covering a large area, and having a short playback period, and they can objectively and accurately provide fine dynamic spatial information about the land cover in urban built-up areas. However, the complexity and comprehensiveness of the urban structure have led to a single-scale analysis method, which makes it difficult to accurately and comprehensively reflect the characteristics of the BUA landscape pattern. Therefore, in this study, a joint evaluation method for an urban land-cover spatiotemporal-mapping chain and multi-scale landscape pattern using high-resolution remote sensing imagery was developed. First, a pixel–object–knowledge model with temporal and spatial classifications was proposed for the spatiotemporal mapping of urban land cover. Based on this, a multi-scale district–BUA–city block–land cover type map of the city was established and a joint multi-scale evaluation index was constructed for the multi-scale dynamic analysis of the urban landscape pattern. The accuracies of the land cover in 2016 and 2021 were 91.9% and 90.4%, respectively, and the kappa coefficients were 0.90 and 0.88, respectively, indicating that the method can provide effective and reliable information for spatial mapping and landscape pattern analysis. In addition, the multi-scale analysis of the urban landscape pattern revealed that, during the period of 2016–2021, Beijing maintained the same high urbanization rate in the inner part of the city, while the outer part of the city kept expanding, which also reflects the validity and comprehensiveness of the analysis method developed in this study.

Urban land cover mapping with Sentinel-2: a spectro-spatio-temporal analysis

Because of its high spatial resolution (10 m and 20 m), rich spectral information (10 spectral bands), and short revisit period (5 days), Sentinel-2 provides new opportunities for earth observation. However, in urban environments, data limitations in the spatial and spectral dimensions constrain Sentinel-2’s performance: (i) the spatial resolution of Sentinel-2 is not sufficient to resolve the heterogeneous urban landscapes, and the prevalence of mixed pixels lowers the performance of image classifiers; (ii) the spectral resolution of Sentinel-2 is not able to fully account for the spectral variability of urban surface materials, which increases the probability that image classifiers mislabels urban land covers. Also, the 5-day temporal resolution makes Sentinel-2 image data suitable for time series analysis, but its contribution to urban land cover mapping still needs to be quantified. This study evaluated Sentinel-2’s performance in mapping urban land covers by mitigating the effect of spectral variability (using FDA, Fisher Discriminant Analysis), improving the spatial resolution of images (using UnFuSen2, a state-of-art Sentinel-2 image fusion approach), and utilizing temporal and spectral characteristics from image time series. Overall, we found that the image time series processed by UnFuSen2 enables the classifiers of k nearest neighbor (KNN), maximum likelihood (MLC), and random forests (RF) to perform the best, and multiple endmember spectra mixture analysis (MESMA) is suitable for classifying image time series that have been jointly processed by FDA and UnFuSen2. Besides, we found a significant contribution of spring and summer imagery to the improvement of land cover mapping accuracy in the Brussels Capital Region.

Urban land use land cover mapping in tropical savannah using Landsat-8 derived normalized difference vegetation index (NDVI) threshold

Generation of land use/land cover map at different spatial scales using satellite remote sensing data has been in practice as far back as early 1970s. Since then, research focus has been on the development of classification steps and improving the quality of the resulting maps. In recent times, the demand for detailed high accuracy land-use and land-cover (LULC) data has been on the increase due to the growing complexity of earth processes, while, at the same time, processing step is becoming more complex. This paper explores Landsat 8 derived normalized difference vegetation index (NDVI) threshold for the purpose of simplifying land cover classification process. NDVI images of January, May and December, 2018, representing dry, wet and harmattan seasons were generated. Thereafter, NDVI values corresponding to the location of a set of training data representing the target urban land covers (water, built-up area, soil, grassland and shrub) were extracted. Using the statistics of the extracted values, NDVI threshold for the respective land cover type were determined for the classification process. Finally, the classification accuracy was evaluated using the unbiased matrix coefficient technique which produced overall accuracy of 71.3%, 46.4% and 75.6% at 95% confidence limit for the months of January, May and December of the year review respectively. The result has shown that NDVI threshold is a simple and practical alternative to obtain LULC map at a reasonable time with a few data.

UAV-Based Multi-Sensor Data Fusion for Urban Land Cover Mapping Using a Deep Convolutional Neural Network

Accurate and up-to-date land cover classification information is essential for many applications, such as land-use change detection, global environmental change, and forest management, among others. Unoccupied aerial systems (UASs) provide the advantage of flexible and rapid data acquisition at low cost compared to conventional platforms, such as satellite and airborne systems. UASs are often equipped with high spatial resolution cameras and/or light detection and ranging (LiDAR). However, the high spatial resolution imagery has a high information content, which makes land cover classification quite challenging. Recently, deep convolutional neural networks (DCNNs) have been effectively applied to remote sensing applications, which overcome the drawback of traditional techniques. In this research, a low-cost UAV-based multi-sensor data fusion model was developed for land cover classification based on a DCNN. For the purpose of this research, two datasets were collected at two different urban locations using two different UASs. A DCNN model, based on U-net with Resnet101 as a backbone, was used to train and test the fused image/LiDAR data. The maximum likelihood and support vector machine techniques were used as a reference for classifier comparison. It was shown that the proposed DCNN approach improved the overall accuracy of land cover classification for the first dataset by 15% compared to the reference classifiers. In addition, the overall accuracy of land cover classification improved by 7%, and the precision, recall, and F-measure improved by 18% when the fused image/LiDAR data were used compared to the images only. The trained DCNN model was also tested on the second dataset, and the obtained results were largely similar to those of the first dataset.

Assessing Multi-Temporal Global Urban Land-Cover Products Using Spatio-Temporal Stratified Sampling

In recent years, the availability of multi-temporal global land-cover datasets has meant that they have become a key data source for evaluating land cover in many applications. Due to the high data volume of the multi-temporal land-cover datasets, probability sampling is an efficient method for validating multi-temporal global urban land-cover maps. However, the current accuracy assessment methods often work for a single-epoch dataset, and they are not suitable for multi-temporal data products. Limitations such as repeated sampling and inappropriate sample allocation can lead to inaccurate evaluation results. In this study, we propose the use of spatio-temporal stratified sampling to assess thematic mappings with respect to the temporal changes and spatial clustering. The total number of samples in the two stages, i.e., map and pixel, was obtained by using a probability sampling model. Since the proportion of the area labeled as no change is large while that of the area labeled as change is small, an optimization algorithm for determining the sample sizes of the different strata is proposed by minimizing the sum of variance of the user’s accuracy, producer’s accuracy, and proportion of area for all strata. The experimental results show that the allocation of sample size by the proposed method results in the smallest bias in the estimated accuracy, compared with the conventional sample allocation, i.e., equal allocation and proportional allocation. The proposed method was applied to multi-temporal global urban land-cover maps from 2000 to 2010, with a time interval of 5 years. Due to the spatial aggregation characteristics, the local pivotal method (LPM) is adopted to realize spatially balanced sampling, leading to more representative samples for each stratum in the spatial domain. The main contribution of our research is the proposed spatio-temporal sampling approach and the accuracy assessment conducted for the multi-temporal global urban land-cover product.

Multimodal Fusion of Mobility Demand Data and Remote Sensing Imagery for Urban Land-Use and Land-Cover Mapping

This paper aims at exploring the potentiality of the multimodal fusion of remote sensing imagery with information coming from mobility demand data in the framework of land-use mapping in urban areas. After a discussion on the function of mobility demand data, a probabilistic fusion framework is developed to take advantage of remote sensing and transport data, and their joint use for urban land-use and land-cover applications in urban and surrounding areas. Two different methods are proposed within this framework, the first based on pixelwise probabilistic decision fusion and the second on the combination with a region-based multiscale Markov random field. The experimental validation is conducted on a case study associated with the city of Genoa, Italy.

Multiscale and efficient recognition of land cover by using both decomposition and deep visual feature analysis

As we know that when we use the colour bands that are pure in nature it will help some of the public authorities to update the land information. It will be a sensing platform so it will take less time and the cost we will be spending will not be much heavy, in that manner aircraft system will be used that should be unmanned. Here we will be using a method for optical aerial imagery that will come under land use classification with decision tree. In the beginning the ownership parcel map will come under play for taking the land cover information through maximum likelihood classifier. Then, to make the relation between land usage and land coverage we will generate the decision tree. A well structured parcel map will be produced by the merits of geographical characteristics of parcels.Hence the aftereffects of the spatial resolution under the classification approach will be discussed completely by sampling them again from the 20 then 50 then 100cm. This land use can be widely used in landscape and urban planning because of the flexible classification method. Using aerial images the object oriented approach for image based analysis is used for urban land cover mapping.

Comparison of Layer-stacking and Dempster-Shafer Theory-based Methods Using Sentinel-1 and Sentinel-2 Data Fusion in Urban Land Cover Mapping

ABSTRACT Data fusion has shown potential to improve the accuracy of land cover mapping, and selection of the optimal fusion technique remains a challenge. This study investigated the performance of fusing Sentinel-1 (S-1) and Sentinel-2 (S-2) data, using layer-stacking method at the pixel level and Dempster-Shafer (D-S) theory-based approach at the decision level, for mapping six land cover classes in Thu Dau Mot City, Vietnam. At the pixel level, S-1 and S-2 bands and their extracted textures and indices were stacked into the different single-sensor and multi-sensor datasets (i.e. fused datasets). The datasets were categorized into two groups. One group included the datasets containing only spectral and backscattering bands, and the other group included the datasets consisting of these bands and their extracted features. The random forest (RF) classifier was then applied to the datasets within each group. At the decision level, the RF classification outputs of the single-sensor datasets within each group were fused together based on D-S theory. Finally, the accuracy of the mapping results at both levels within each group was compared. The results showed that fusion at the decision level provided the best mapping accuracy compared to the results from other products within each group. The highest overall accuracy (OA) and Kappa coefficient of the map using D-S theory were 92.67% and 0.91, respectively. The decision-level fusion helped increase the OA of the map by 0.75% to 2.07% compared to that of corresponding S-2 products in the groups. Meanwhile, the data fusion at the pixel level delivered the mapping results, which yielded an OA of 4.88% to 6.58% lower than that of corresponding S-2 products in the groups.

Combined Effects of Artificial Surface and Urban Blue-Green Space on Land Surface Temperature in 28 Major Cities in China

The two most common land cover types in urban areas, artificial surface (AS) and urban blue-green space (UBGS), interact with land surface temperature (LST) and exhibit competitive effects, namely, heating and cooling effects. Understanding the variation of these effects along the AS ratio gradient is highly important for the healthy development of cities. In this study, we aimed to find the critical point of the joint competitive effects of UBGS and AS on LST, and to explore the variability in different climate zones and cities at different development levels. An urban land cover map and LST distribution map were produced using Sentinel-2 images and Landsat-8 LST data, respectively, covering 28 major cities in China. On this basis, the characteristics of water, vegetation, and LST in these cities were analyzed. Moreover, the UBGS (water or vegetation)–AS–LST relationship of each city was quantitatively explored. The results showed that UBGS and AS have a competitive relationship and jointly affect LST; this competition has a critical point (threshold). When the proportion of UBGS exceeds this value, UBGS replaces AS as the dominant variable for LST, bringing about a cooling effect. In contrast, when AS dominates LST, it causes a warming effect. The critical points between AS and water and between AS and vegetation in 28 major cities in China were 80% and 70%, respectively. The critical point showed an obvious zonal difference. Compared with cities in subtropical and temperate climate regions, the critical point of arid cities is higher, and UBGS exhibited better performance at alleviating the urban thermal environment. The critical point of cities with higher development levels is lower than that of cities with lower development levels. Even areas with relatively low AS coverage are prone to high temperatures, and more attention should be paid to improving the coverage of UBGS. Our research results provide a reference for the more reasonable handling of the relationship between urban construction, landscape layout, and temperature control.

Improving Urban Land Cover Mapping with the Fusion of Optical and SAR Data Based on Feature Selection Strategy

Taking the Futian District as the research area, this study proposed an effective urban land cover mapping framework fusing optical and SAR data. To simplify the model complexity and improve the mapping results, various feature selection methods were compared and evaluated. The results showed that feature selection can eliminate irrelevant features, increase the mean correlation between features slightly, and improve the classification accuracy and computational efficiency significantly. The recursive feature elimination-support vector machine (RFE-SVM) model obtained the best results, with an overall accuracy of 89.17% and a kappa coefficient of 0.8695, respectively. In addition, this study proved that the fusion of optical and SAR data can effectively improve mapping and reduce the confusion between different land covers. The novelty of this study is with the insight into the merits of multi-source data fusion and feature selection in the land cover mapping process over complex urban environments, and to evaluate the performance differences between different feature selection methods.

Improving Urban Land Cover Mapping with the Fusion of Optical and SAR Data Based on Feature Selection Strategy

Taking the Futian District as the research area, this study proposed an effective urban land cover mapping framework fusing optical and SAR data. To simplify the model complexity and improve the mapping results, various feature selection methods were compared and evaluated. The results showed that feature selection can eliminate irrelevant features, increase the mean correlation between features slightly, and improve the classification accuracy and computational efficiency significantly. The recursive feature elimination-support vector machine (RFE-SVM) model obtained the best results, with an overall accuracy of 89.17% and a kappa coefficient of 0.8695, respectively. In addition, this study proved that the fusion of optical and SAR data can effectively improve mapping and reduce the confusion between different land covers. The novelty of this study is with the insight into the merits of multi-source data fusion and feature selection in the land cover mapping process over complex urban environments, and to evaluate the performance differences between different feature selection methods.

Segment-based bag of visual words model for urban land cover mapping using polarimetric SAR data

Producing high-resolution urban land cover maps is essential for decision-making and urban management. In this regard, Synthetic Aperture Radar (SAR), especially Polarimetric SAR (PolSAR), has served as a valuable data source to fulfill this task. Direct conversion of low-level features into high-level Land Cover (LC) concept may reduce the final classification accuracy. Therefore, mid-level representation models, such as Bag of Visual Words (BOVW), were employed to resolve the existing semantic gap challenge. In this paper, a Segment-based BOVW (Seg-BOVW) model was developed for urban land cover classification using PolSAR data. To this end, two PolSAR data over San Francisco Bay (SFB) and Flevoland (FL) acquired by RADARSAT-2 were employed to comprehensively evaluate the Seg-BOVW model's performance. First, to exploit the full potential of PolSAR data, 169 low-level features in four categories: (1) original, (2) polarimetric, (3) texture, and (4) decomposition features were extracted. Afterward, a Multi-Objective Genetic Algorithm (MOGA) was implemented to investigate the importance of low-level features for urban land cover mapping. This step resulted in selecting 14 and 6 low-level features, as the high contributing features, for SFB and FL datasets, respectively. The Seg-BOVW model achieved significant overall accuracies of 96.02% and 98.82% for SFB and FL, respectively, indicating the high potential of the proposed method for urban land cover classification. Furthermore, a comparison with other well-known algorithms of Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN) was made, suggesting the capability of the Seg-BOVW model to improve the urban land cover classification results. Finally, the Seg-BOVW model was tested with two other PolSAR datasets acquired with different sensors over SFB to examine its applicability with different datasets.