Urban Mapping Research Articles

Urban Particulate Matter Hazard Mapping and Monitoring Site Selection in Nablus, Palestine

Few air pollution studies have been applied in the State of Palestine and all showed an increase in particulate matter concentrations above WHO guidelines. However, there is no clear methodology for selecting monitoring locations. In this study, a methodology based on GIS and locally calibrated low-cost sensors was tested. A GIS-based weighted overlay summation process for the potential sources of air pollution (factories, quarries, and traffic), taking into account the influence of altitude and climate, was used to obtain an air pollution hazard map for Nablus, Palestine. To test the methodology, eight locally calibrated PM sensors (AirUs) were deployed to measure PM2.5 concentrations for 55 days from 7 January to 2 March 2022. The results of the hazard map showed that 82% of Nablus is exposed to a high and medium risk of PM pollution. Sensors’ readings showed a good match between the hazard intensity and PM concentrations. It also shows an elevated PM2.5 concentrations above WHO guidelines in all areas. In summary, the overall average for PM2.5 in the Nablus was 48 µg/m3. This may indicate the effectiveness of mapping methodology and the use of low-cost, locally calibrated sensors in characterizing air quality status to identify the potential remediation options.

Towards urban flood susceptibility mapping using data-driven models in Berlin, Germany

Identifying urban pluvial flood-prone areas is necessary but the application of two-dimensional hydrodynamic models is limited to small areas. Data-driven models have been showing their ability to map flood susceptibility but their application in urban pluvial flooding is still rare. A flood inventory (4333 flooded locations) and 11 factors which potentially indicate an increased hazard for pluvial flooding were used to implement convolutional neural network (CNN), artificial neural network (ANN), random forest (RF) and support vector machine (SVM) to: (1) Map flood susceptibility in Berlin at 30, 10, 5, and 2 m spatial resolutions. (2) Evaluate the trained models' transferability in space. (3) Estimate the most useful factors for flood susceptibility mapping. The models' performance was validated using the Kappa, and the area under the receiver operating characteristic curve (AUC). The results indicated that all models perform very well (minimum AUC = 0.87 for the testing dataset). The RF models outperformed all other models at all spatial resolutions and the RF model at 2 m spatial resolution was superior for the present flood inventory and predictor variables. The majority of the models had a moderate performance for predictions outside the training area based on Kappa evaluation (minimum AUC = 0.8). Aspect and altitude were the most influencing factors on the image-based and point-based models respectively. Data-driven models can be a reliable tool for urban pluvial flood susceptibility mapping wherever a reliable flood inventory is available.

Research on the Division Method of Signal Control Sub-Region Based on Macroscopic Fundamental Diagram

The macroscopic fundamental diagram (MFD) provides a method to evaluate macro traffic operation through micro traffic parameters, which can be applied to traffic control to prevent traffic congestion transfer and improve road network efficiency. However, due to the large scale of the urban road network as well as the complex temporal and spatial distribution of road congestion, the application of the MFD for signal control first requires the partition of the urban road network. Based on the analysis of MFD partition purposes, a set of MFD partition methods based on graph theory was designed. Firstly, graph theory was used to transform the urban road network; secondly, the minimum spanning tree method was used to divide the urban traffic network map. Moreover, the attribution of the link between connected regions is determined. Our method can solve the problem of ambiguous intersection ownership, and the road sections belonging to the same road in opposite directions are separated. This method has the ability to control the size of the area by limiting the number of intersections; Finally, the evaluation index of regional clustering results was drawn. To achieve the research objective, we collected and processed vehicle information data from the Xuzhou car-hailing platform to obtain traffic density information. Then, we selected an area with sufficient data and a large enough road network. The empirical value range of the regional control value was obtained by comparing the values of multiple groups of measurement data k and evaluation indexes. In this process, it was found that during the period of flat peak and peak transition, while the regional average traffic density changes, the uniformity of traffic density first decreases and then increases. The traffic density uniformity of the signal control area can be improved by controlling the size of the signal control area. We obtained the empirical value range of the regional control value k by comparing the values of multiple groups of measurement data k and evaluation indexes. Then, we compared them with the two kinds of traditional partition algorithms and improved multiple dichotomy algorithms. Our method improves road network balance by 5% over existing methods.

Evaluation of Urban Vegetation Phenology Using 250 m MODIS Vegetation Indices

The dynamics of urban vegetation phenology play an important role in influencing human activities. Previous studies have shown high-resolution remote sensing as a tool for urban vegetation mapping, but the low temporal resolution of these data limits their use for phenological modeling. Therefore, it is of great significance to evaluate Moderate Resolution Imaging Spectroradiometer (MODIS) imagery for urban vegetation phenology monitoring. Here, we extracted the start and end of growing season (SOS and EOS) in urban ecosystems based on both the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) from the 250 m MODIS vegetarion indices product (MOD13Q1). Then the accuracies of the satellite-derived SOS and EOS were evaluated through comparing phenological observations at 18 ground sites. Results showed that SOS was most consistent with the prime of leaf unfolding date and EOS was most consistent with the beginning of leaf coloring date. Overall, EVI was found to have stronger predictive strength than NDVI in detecting urban vegetation phenology in terms of both higher correlation coef- ficients and lower root-mean-square errors. In addition, the dynamic threshold method was more accurate in deriving SOS, while the double logistic method had relatively higher accuracy in deriving EOS .

What would be better for urban mapping in the Klang Valley? SPOT or Sentinel-1

Klang Valley is undergoing rapid urbanization. Many cities in Klang Valley are developing to cope with the trend for migration to more urbanized areas. At the current pace and magnitude of urbanization, land expansion is one of the most visible, irreversible forms of land use and land cover change. Thus, Remote Sensing is regarded as an essential tool in these contexts that are important for urban studies and planning. In the past, many studies have relied on optical Remote Sensing rather than synthetic aperture radar (SAR) data. Some have argued that SAR is a poor choice because of its speckle noise and requires complex pre-processing, but countries in the tropics, where clouds are a big problem, cannot always rely on optical. The present work utilizes the interferometric coherence method with the C-band datasets acquired from the Sentinel-1 and compares it with the vegetation index method on the SPOT datasets for 2016 and 2021. Once adequately processed and classified, the SAR images resulted in significant textures on urban areas with respect to the map generated from vegetation index of SPOT images. Hence, these findings may prove beneficial in developing an effective urban planning strategy and policy formulation for the Klang Valley.

Leveraging the use of labeled benchmark datasets for urban area change mapping and area estimation: a case study of the Washington DC–Baltimore region

ABSTRACT Worldwide economic development and population growth have led to unprecedented changes in urban land use in the twenty-first century. As satellite data become available at higher spatial (3–10 m) and temporal (1–3 days) resolution, new opportunities arise to map and quantify urban area changes. While deep learning (DL) models have recently shown great performance when dealing with satellite data, their training requires a lot of labeled data which are not necessarily available at global scale. Satellite benchmark datasets, commonly used to advance methods, provide labeled data, but are rarely used for mapping and area estimation outside the training data. In this study, we aim to utilize the Sentinel-2-based benchmark dataset, Onera Satellite Change Detection (OSCD), to train a DL model and analyze its performance at local scale to map urban land use changes, estimate area of changes and provide characterization of changes. We apply the model over the Washington DC–Baltimore area for 2018–2019. We show that in just one year almost 1% of the total urban area underwent changes with the majority coming from the construction of commercial buildings, followed by residential buildings. Almost 10% of changes were attributed to the construction of new or renovation of existing schools.

High-Resolution Urban Air Quality Mapping for Multiple Pollutants Based on Dense Monitoring Data and Machine Learning.

Spatially explicit urban air quality information is important for urban fine-management and public life. However, existing air quality measurement methods still have some limitations on spatial coverage and system stability. A micro station is an emerging monitoring system with multiple sensors, which can be deployed to provide dense air quality monitoring data. Here, we proposed a method for urban air quality mapping at high-resolution for multiple pollutants. By using the dense air quality monitoring data from 448 micro stations in Lanzhou city, we developed a decision tree model to infer the distribution of citywide air quality at a 500 m × 500 m × 1 h resolution, with a coefficient of determination (R2) value of 0.740 for PM2.5, 0.754 for CO and 0.716 for SO2. Meanwhile, we also show that the deployment density of the monitoring stations can have a significant impact on the air quality inference results. Our method is able to show both short-term and long-term distribution of multiple important pollutants in the city, which demonstrates the potential and feasibility of dense monitoring data combined with advanced data science methods to support urban atmospheric environment fine-management, policy making, and public health studies.

Fine-Grained Urban Flow Inference via Normalizing Flow (Student Abstract)

Fine-grained urban flow inference (FUFI) aims to infer the coarse-grained (CG) urban flow map to the corresponding fine-grained (FG) one, which plays an important role in efficient traffic monitoring and management in smart cities. In FUFI, the CG map can be obtained with only a small number of monitoring devices, greatly reducing the overhead of deploying devices and the costs of maintenance, labor, and electricity. Existing FUFI methods are mainly based on techniques from image super-resolution (SR) models, which cannot fully consider the influence of external factors and face the ill-posed problem in SR tasks. In this paper, we propose UFI-Flow, a novel approach for addressing the FUFI problem by learning the conditional distributions of CG and FG map pairs. Given the CG map and the latent variables, the corresponding FG map is inferred by invertible transformations. In addition, an augmented distribution fusion mechanism is further proposed to constrain the urban flow distribution within the influence of external factors. We provide a new large-scale real-world FUFI dataset and show that UFI-Flow significantly outperforms the strong baselines.

A Novel Approach for Automatic Urban Surface Water Mapping with Land Surface Temperature (AUSWM)

The principal difficulty in extracting urban surface water using remote-sensing techniques is the influence of noise from complex urban environments. Although various methods exist, there are still many sources of noise interference when extracting urban surface water, and automatic cartographic methods with long time series are especially scarce. Here, we construct an automatic urban surface water extraction method from the combination of traditional water index, urban shadow index (USI), and land surface temperature (LST) by using the Google Earth Engine cloud computing platform and Landsat imagery. The three principal findings derived from the application of the method were as follows. (i) In comparison with autumn and winter, LST in spring and summer could better distinguish water from high-reflection ground objects, shadows, and roads and roofs covered by asphalt. (ii) The overall accuracy of Automated Water Extraction Index (AWEIsh) in Zhengzhou was 77.5% and the Kappa coefficient was 0.55; with consideration of the USI and LST, the overall accuracy increased to 96.0% and the Kappa coefficient increased to 0.92. (iii) During 1990–2020, the area of urban surface water in Zhengzhou increased, with an evident trend in expansion from 11.51 km2 in 2008 to 49.28 km2 in 2020. Additionally, possible omissions attributable to using 30m-resolution imagery to extract urban water areas were also discussed. The method proposed in this study was proven effective in eliminating the influence of noise in urban areas, and it could be used as a general method for high-accuracy long-term mapping of urban surface water.

Flood risk map from hydrological and mobility data: A case study in São Paulo (Brazil)

AbstractCities increasingly face flood risk primarily due to extensive changes of the natural land cover to built‐up areas with impervious surfaces. In urban areas, flood impacts come mainly from road interruption. This article proposes an urban flood risk map from hydrological and mobility data, considering the megacity of São Paulo, Brazil, as a case study. We estimate the flood susceptibility through the Height Above the Nearest Drainage algorithm; and the potential impact through the exposure and vulnerability components. We aggregate all variables into a regular grid and then classify the cells of each component into three classes: Moderate, High, and Very High. All components, except the flood susceptibility, have few cells in the Very High class. The flood susceptibility component reflects the presence of watercourses, and it has a strong influence on the location of those cells classified as Very High.

Https://habibiaislamicus.com/index.php/hirj/article/view/252

Masjid is an important place maker, shaping and reshaping its neighborhood. The role of masjid ranges to an all-encompassing place having impact on all activities of life in a society. Seoul central Majid of South Korea was built in 1976 as the first Masjid in modern history of South Korea. Since 1990s the immediate surroundings of the Masjid revitalized by itself in the without any governmental interventions. This study explored the existing neighborhood of the masjid and examined the urban maps form 1970 and 2020 to find out the impact of masjid on the revitalization of its neighborhood. The results shows that the urban form remained unchanged to a major extant, but the activities of the neighborhood changed to those related with the Muslim community. The study also presented the conditions, and events which paved the way for the re-generation process. The Masjid in this case acted as socio-economic modifier for its .

An investigation into the health-promoting behaviors and their associated factors in 18-29 year-old youth of Khorramabad city in 2020

Health-promoting behaviors and healthy lifestyle are the main strategies to preserve people's health. Considering the importance of the youth's health in promoting the society's health, the present study was conducted to investigate the health-promoting behaviors and their associated factors in 18-29year-old youth of Khorramabad city in 2020. In this cross-sectional study, 358 individuals aged 18 to 29 years living in Khorramabad city in the first half of 2020 were included. This research sample was selected using a stratified multistage random sampling method. First, Khorramabad city was divided into three regions: north, central, and south regions based on the urban map. Then, two therapeutic health centers, two recreation centers, two educational centers, two business centers, and two religious cultural centers were randomly selected from each region. After that, the eligible samples were selected to enter the study by applying the convenience sampling method from each of these centers proportional to the calculated sample size. The sample size was estimated to be 358 people. The data was analyzed using SPSS software version 19.0. Descriptive statistical methods, such as mean, standard deviation and ratios, and frequency percentages were used to describe the terms. In addition, independent-t test, one-way analysis of variance (ANOVA), and linear regression analysis were used to analyze data. The mean age of the study participants was 24.6 ± 2.6 years. Of the total participants in the study, 217 (60.6%) were male and the rest were female. The total mean score of health-promoting behaviors in participants of the study was 122.6±16.13 (achievable range: 52-208). Overall, the status of health-promoting behaviors was undesirable in 12% of participants, moderate in 87.4%, and desirable in 0.6%. In univariate analysis, the difference in the total score of health-promoting behaviors was statistically significant between the subjects by gender (PV = 0.002), marital status (0.049), education level (0.001), occupation (<0.001) and history of regular smoking (<0.001).In linear regression analysis, gender, marital status, education level, and occupation were the most important predictors of health-promoting behaviors in the participants of this study. Considering the pivotal role of youth in society, proper planning seems necessary to improve their performance in the area of health promotion.

Mapping global urban greenspace: An analysis based on open land-cover data

Urban greenspace plays a positive role in improving the urban living environment, and thus extensive studies have paid attention to the mapping of urban greenspace for various applications. Recently, open and high-resolution land-cover (LC) data have been viewed as essential sources for urban greenspace mapping. However, few studies have performed a global-scale analysis; moreover, as there are commonly different LC types in an LC dataset, few studies have investigated how the definition or selection of different LC types as greenspace impacts urban greenspace mapping. Therefore, this study aims to investigate the spatial pattern of urban greenspace at a global scale and the impact on selecting different LC types for urban greenspace mapping. We employed two global open datasets (FROM-GLC10 and GHS-UCDB) and identified 13,079 global urban areas. Various LC types that potentially relate to urban greenspace and three different selections of LC types were analyzed. Results show that: 1) The spatial pattern of global urban greenspace may vary with the use of different selections; 2) The impact of whether or not to select each LC type as urban greenspace relates to not only the LC type itself but also the geographic locations. The findings may benefit urban planners and designers to select proper LC type(s) for urban greenspace studies.

HYBRID AERIAL SENSOR DATA AS BASIS FOR A GEOSPATIAL DIGITAL TWIN

Abstract. More and more cities declare themselves to be a smart city or plan to be the same. Smart cities require a solid data source as basis for all further actions and the urban digital twin is the basis on which all information is collected and analysed. The urban digital twin is much more than just a 3D city model, but often this together with GIS data is the starting point for the urban digital twin. The basis of the urban digital twin is formed by geospatial data in the form of the geospatial digital twin. The digital twin hereby acts as a kind of hub into which all relevant and available information is included and analysed. To generate a geospatial digital twin aerial sensors that collect multiple data simultaneously, hybrid sensors, are perfectly suited for this task. In aerial data acquisition a new era started with the introduction of the first real hybrid sensor systems, like the Leica CityMapper-2. Hybrid in this context means the combination of an (oblique) camera system with a topographic LiDAR into an integrated aerial mapping system. By combining these complimentary sub-systems into one system the weaknesses of the one system could be compensated by using the alternative data source. An example is the mapping of low-light urban canyons, where image-based systems mostly produce unreliable results. For an LiDAR sensor the geometrical reconstruction of these areas is straight forward and leads to accurate results. The paper gives a detailed overview over the development and technical characteristics of hybrid sensor systems. The process of data acquisition is discussed and strategies for hybrid urban mapping are proposed. Furthermore, the paper provides insights into the advantage of LiDAR data for the 3D Mesh generation for urban modelling and on the possibilities to generate new products from the combination of the single products with the help of GeoAI. Finally, the use and some use cases of the hybrid sensor data and the derived products in the context of the urban digital twin is discussed and with the infinite loop of data, analysis, and action it is shown, that all data from the urban digital twin can only be a snapshot at a given point in time and the data recording and analysis is a permanent loop.

UrbanWatch: A 1-meter resolution land cover and land use database for 22 major cities in the United States

Very-high-resolution (VHR) land cover and land use (LCLU) is an essential baseline data for understanding fine-scale interactions between humans and the heterogeneous landscapes of urban environments. In this study, we developed a Fine-resolution, Large-area Urban Thematic information Extraction (FLUTE) framework to address multiple challenges facing large-area, high-resolution urban mapping, including the view angle effect, high intraclass and low interclass variation, and multiscale land cover types. FLUTE builds upon a teacher-student deep learning architecture, and includes two new feature extraction modules – Scale-aware Parsing Module (SPM) and View-aware Embedding Module (VEM). Our model was trained with a new benchmark database containing 52.43 million labeled pixels (from 2014 to 2017 NAIP airborne Imagery) to capture diverse LCLU types and spatial patterns. We assessed the credibility of FLUTE by producing a 1-meter resolution database named UrbanWatch for 22 major cities across the conterminous United States. UrbanWatch contains nine LCLU classes – building, road, parking lot, tree canopy, grass/shrub, water, agriculture, barren, and others, with an overall accuracy of 91.52%. We have further made UrbanWatch freely accessible to support urban-related research, urban planning and management, and community outreach efforts: https://urbanwatch.charlotte.edu.

Recognizing Building Group Patterns in Topographic Maps by Integrating Building Functional and Geometric Information

Recognizing building group patterns is fundamental to numerous fields, such as urban landscape evaluation, social analysis, and map generalization. Despite the increasing number of algorithms available for building group pattern recognition, there is still a lack of satisfactory grouping results due to insufficient information and only geometric features being provided to recognition methods. This study aims to provide a novel building grouping method that combines building function and geometric information. We specifically focus on the process of recognizing building groups in topographic maps as a prerequisite to subsequent map generalization. First, the building functions are inferred using the dynamic time warping (DTW) algorithm based on Tencent user density data and POIs (points of interest). Then, two types of constrained Delaunay triangulations (CDTs) are created for each building block, from which several spatial indices, such as the continuity index (SCI), direction, and distance of every two adjacent buildings, are derived. Finally, each building block is modeled as a graph on the grounds of derived matrices and building function information, and a graph segmentation approach is proposed to extract building groups. A case study is conducted to test the proposed approach. The experimental results indicate that the proposed approach can produce satisfactory results, given that the correctness value is above 81.63% for our study area. Comparative studies reveal that the method without building function information is an ineffective grouping method when buildings with different functions are close to each other. In addition, generalization results derived from the proposed method are more in line with those of maps for daily use, as they provide users with more accurate spatial divisions of urban buildings.

A PILOT STUDY OF URBAN POI MAPPING USING CROWDSOURCED STREET-LEVEL IMAGERY AND DEEP LEARNING

Abstract. Point-of-interest (POI) data contains rich semantic and spatial information, having a wide range of applications including land use, transport planning and driving navigation. However, urban POI mapping traditionally requires a lot of manpower and material resources, which only few institutions or enterprises can afford to. With the increasing amount of street-level imagery, it is possible to directly extract POI-related information from such data and automatically map the distribution of urban POIs. In the pilot study, we mainly focused on extracting POIs from billboards in street-level imagery. Firstly, the you only look once (YOLO) algorithm was considered to locate billboards in the imagery, then an optical character recognition (OCR) model was adopted to extract POI-related semantic information from the detected billboard, and finally the extracted semantic text was further processed to obtain POI results. The preliminary study shows that it is a promising way of mapping urban POIs from crowdsourced street-level data using deep learning techniques.

Fine-Grained Urban Air Quality Mapping from Sparse Mobile Air Pollution Measurements and Dense Traffic Density

Urban air quality mapping has been widely applied in urban planning, air pollution control and personal air pollution exposure assessment. Urban air quality maps are traditionally derived using measurements from fixed monitoring stations. Due to high cost, these stations are generally sparsely deployed in a few representative locations, leading to a highly generalized air quality map. In addition, urban air quality varies rapidly over short distances (<1 km) and is influenced by meteorological conditions, road network and traffic flow. These variations are not well represented in coarse-grained air quality maps generated by conventional fixed-site monitoring methods but have important implications for characterizing heterogeneous personal air pollution exposures and identifying localized air pollution hotspots. Therefore, fine-grained urban air quality mapping is indispensable. In this context, supplementary low-cost mobile sensors make mobile air quality monitoring a promising alternative. Using sparse air quality measurements collected by mobile sensors and various contextual factors, especially traffic flow, we propose a context-aware locally adapted deep forest (CLADF) model to infer the distribution of NO2 by 100 m and 1 h resolution for fine-grained air quality mapping. The CLADF model exploits deep forest to construct a local model for each cluster consisting of nearest neighbor measurements in contextual feature space, and considers traffic flow as an important contextual feature. Extensive validation experiments were conducted using mobile NO2 measurements collected by 17 postal vans equipped with low-cost sensors operating in Antwerp, Belgium. The experimental results demonstrate that the CLADF model achieves the lowest RMSE as well as advances in accuracy and correlation, compared with various benchmark models, including random forest, deep forest, extreme gradient boosting and support vector regression.

COMPARISON OF MACHINE LEARNING CLASSIFIERS FOR MULTITEMPORAL AND MULTISENSOR MAPPING OF URBAN LULC FEATURES

Abstract. This study compares four machine-learning algorithms comprising of Classification And Regression Trees (CART), Random Forest (RF), Gradient Tree Boosting (GTB) and Support Vector Machine (SVM) for the classification of urban land-use and land-cover (LULC) features. Using multitemporal and multisensor Landsat data from 1984-2020 at 5-year intervals for the Greater Gaborone Planning Area (GGPA) in Botswana, the aim of the study is to determine the performance of the classifiers in the extraction of different urban LULC features as built-up, bare-soil, water, grass, shrubs and forest. The results show that for mapping built-up areas, RF and SVM presented the best results with overall accuracy of 85%. Bare soil is best mapped using RF and CART with accuracy of up to 98%, while SVM and GTB were most suitable for mapping water bodies. The suitable classifiers for mapping the vegetation classes were RF for grass (94.5%), SVM for shrubland (81.5%) and GTB for forest (84.3%). In terms of class specific accuracy, RF achieved the highest performance with average overall accuracy (OA) of 95.9%, SVM (95.8%), GTB (95.6%) and CART (95.1%). The same performance pattern was observed from the F1-score, True Positive Rate (TPR), False Positive Rate (FPR) and Area under ROC curve (AUC) metrices for the class classification accuracies. The overall accuracy for the eight-epoch years were RF (87.8%), SVM (87.5%), GTB (86.4%) and CART (85.3%). To improve on the urban LULC mapping, the study proposes the post-classification feature fusion of the best classifier results.

BUILDING FOOTPRINT EXTRACTION FROM SPACE-BORNE IMAGERY USING DEEP NEURAL NETWORKS

Abstract. One of the important and high-level detailing contained within basemaps is the ‘building feature’. Though pre-trained Deep Learning (DL) models are available for Building Feature Extraction (BFE), they are not efficient in predicting the buildings in other locations. This study explores the need and the major issue of implementing DL models for BFE from Very High Resolution Remote Sensing (VHRS) satellite data for any given area. Though advanced DL models are invented, in order to implement them, huge amount of potential training data is demanded for feed in. the building typologies are highly subjected to the context of study area including soil characteristics, culture/lifestyle/economy, architectural style and the building byelaws. The study believes that availability of enough training data of contextual buildings as one of the concern for effective model training. The study aims to extract the buildings present in the study area from Pleiades 1A (2019) RGB VHRS data using simple Mask R-CNN instance segmentation model which is training on the native contextual buildings. Here, an automated method of generating the location-specific training data for a given area is followed using Google Maps API (2021). The generated training data when trained on a deep learning architecture and predicted by the input data yielded promising results. The prediction accuracy of about 98.41% specificity, 96.20% predictive accuracy and 0.89 F1 score are achieved. The methods adopted assist the planning/governing bodies to accelerate the qualitative urban map preparation.