Classification Of Urban Areas Research Articles

Multiscale Urban Functional Zone Recognition Based on Landmark Semantic Constraints

The classification of urban functional areas is important for understanding the characteristics of urban areas and optimizing the utilization of urban land resources. Existing related methods have improved accuracy. However, they neglect cognitive differences amongst humans in the different scales of regional functions. Moreover, how to build the correlations of cross-scale characteristics is still unresolved when realizing the classification of multiscale urban functional zones. To resolve these problems, a transportation analysis zone involving urban buildings as research units is created and these units are described by geometric and functional characteristics using multiple data sources. Then, a hierarchical clustering model is built for the recognition of urban functional areas at varying scales with landmark semantic constraints. In the experiments, Shanghai served as the study area, and multiscale zones were created using different levels of road networks considering the constraint correlation of the significance between cross-scale maps. The experiential results show the proposed method has excellent performance and optimizes the functional zone classification at different scales. This study not only enriches the multiscale urban functional area-recognition methods but also can be used in other aspects, like cartographic generalization or spatial analysis.

Measuring the Convergence and Divergence in Urban Street Perception among Residents and Tourists through Deep Learning: A Case Study of Macau

In today’s context of flourishing tourism, the development of urban tourism leads to a continuous influx of population. Existing empirical evidence highlights the interaction between tourists’ and residents’ perception of urban spaces and the local society and living spaces. This study, focusing on Macau, utilizes the region’s streetscape images to construct a deep learning-based model for quantifying the urban street perception of tourists and local residents. To obtain more refined perceptual evaluation data results, during the training phase of the model, we intentionally categorized tourist activities into natural landscape tours, historical sightseeing, and entertainment area visits, based on the characteristics of the study area. This approach aimed to develop a more refined perception evaluation method based on the classification of urban functional areas and the types of urban users. Further, to improve the streetscape environment and reduce visitor and resident dissatisfaction, we delved into the differences in perception between tourists and residents in various functional urban areas and their relationships with different streetscape elements. This study provides a foundational research framework for a comprehensive understanding of residents’ and tourists’ perceptions of diverse urban street spaces, emphasizing the importance of exploring the differentiated perceptions of streetscapes held by tourists and residents in guiding scientific urban tourism development policies and promoting social sustainability in cities, particularly those where tourism plays a significant role.

Fine Classification of Urban Tree Species Based on UAV-Based RGB Imagery and LiDAR Data

Rapid and accurate classification of urban tree species is crucial for the protection and management of urban ecology. However, tree species classification remains a great challenge because of the high spatial heterogeneity and biodiversity. Addressing this challenge, in this study, unmanned aerial vehicle (UAV)-based high-resolution RGB imagery and LiDAR data were utilized to extract seven types of features, including RGB spectral features, texture features, vegetation indexes, HSV spectral features, HSV texture features, height feature, and intensity feature. Seven experiments involving different feature combinations were conducted to classify 10 dominant tree species in urban areas with a Random Forest classifier. Additionally, Plurality Filling was applied to further enhance the accuracy of the results as a post-processing method. The aim was to explore the potential of UAV-based RGB imagery and LiDAR data for tree species classification in urban areas, as well as evaluate the effectiveness of the post-processing method. The results indicated that, compared to using RGB imagery alone, the integrated LiDAR and RGB data could improve the overall accuracy and the Kappa coefficient by 18.49% and 0.22, respectively. Notably, among the features based on RGB, the HSV and its texture features contribute most to the improvement of accuracy. The overall accuracy and Kappa coefficient of the optimal feature combination could achieve 73.74% and 0.70 with the Random Forest classifier, respectively. Additionally, the Plurality Filling method could increase the overall accuracy by 11.76%, which could reach 85.5%. The results of this study confirm the effectiveness of RGB imagery and LiDAR data for urban tree species classification. Consequently, these results could provide a valuable reference for the precise classification of tree species using UAV remote sensing data in urban areas.

Hyperspectral Image Analysis using LSTM and 2D CNN and its Application in Remote Sensing

The land cover classification in urban areas is described in this research work. The use of hyperspectral image analysis is growing in popularity because it performs better than conventional machine learning techniques. Hypercubes, a type of three-dimensional dataset with two spatial dimensions and one spectral dimension, make up the Hyperspectral imaging (HSI). An overview of HSI's uses in remote sensing applications and the methods for classifying it are given in this research. In the field of HSI, numerous experiments are conducted with various deep learning methods for analysis and classification. The main components of this research is convolutional neural network (CNN)and long short-term memory (LSTM) that shows to be more effective than alternative models. In this case, spectral and spatial features are extracted using CNN and LSTM, respectively, and the results are then classified using support vector machines (SVM). The datasets utilized in this study were gathered using a ROSIS sensor/spectrometer at Pavia University and Indian Pines.

Impact of the perceived sensory dimensions of an atypical environment on perceived environmental restorativeness (Impacto de las dimensiones sensoriales percibidas de un ambiente atípico sobre la percepción de restauración ambiental)

ABSTRACT This study documents the restorative influence of an atypical natural environment characterized in terms of its ecosystem as Tamaulipan Thornscrub (TT), considering a classification of urban green areas based on eight perceived sensory dimensions (PSD). A sample of 444 university students (62% women; age range 18–29) evaluated the restorative potential of a set of images from the TT ecosystem classified in one of the following PSD categories: Diverse, Natural, Open, Cohesive, Social, Cultural, Sheltered and Serene. Analysis conducted using non-parametric repeated measures tests showed that Diverse and Serene PSD are significantly more restorative than the other dimensions, while Natural and Cohesive are the least restorative. The TT ecosystem overall was perceived to possess moderate global restorative potential. The interaction between PSD, the TT ecosystem, perceived environmental restorativeness and its contribution to the promotion of public health is discussed. Finally, the use of certain PSD in green areas is suggested along with the consideration of studying specific eco-biological factors.

A Conditional Generative Adversarial Network for urban area classification using multi-source data

A Conditional Generative Adversarial Network for urban area classification using multi-source data

Classification and Development Potential Evaluation of Urban Metro Station Areas Based on Extended Node-Place Model-- A case study of Nanjing Metro Line 1

In the process of changing from the extensive mode to the compact mode of the urban space development in China, metro has solved the problem of high-capacity and rapid transportation in cities. The planning of metro stations needs to be investigated in the context of station types in the process of the urban development. In the research, combined with the development examples of stations and surrounding areas along Nanjing Subway Line 1, the factors such as the classification of station types, the current development potential assessment and so on were investigated based on the extended "node-place" model. And then the scientific and reasonable optimization and adjustment strategies for stations and surrounding areas were proposed, which provides reference for the subsequent planning strategies and development and construction of metro stations and surrounding areas.

Identification of Urban Jobs–Housing Sites Based on Online Car-Hailing Data

With the development of cities, the organization of jobs–housing space is becoming more complex, and the rapid, effective identification of both residences and workplaces is crucial to sustainable urban development. The long time series of online car-hailing data conveys a large amount of activity trajectory information about urban populations, which can represent the social functions of urban areas, including workplaces and residences. This paper constructs a jobs–housing site identification model based on human activity characteristics. This model uses a time series dataset of online car hailing that characterizes the changes in regional passenger flow and implements the similarity measure and semi-supervised learning of time series to determine the classification of urban areas. Then, the jobs–housing factor method is introduced to extract the jobs–housing characteristics of different regions, which achieves the jobs–housing site identification. Finally, the empirical analysis of Chengdu city shows that the proposed model method can effectively mine the distribution of urban jobs–housing sites. The identification results are consistent with the actual situation, and the combination of the time series similarity and the jobs–housing feature variable improves the identification effect, providing a new way of thinking about urban jobs–housing space research.

Optimized Feature-Level Fusion of Hyperspectral Thermal and Visible Images in Urban Area Classification

Optimized Feature-Level Fusion of Hyperspectral Thermal and Visible Images in Urban Area Classification

Vegetation Classification in Urban Areas by Combining UAV-Based NDVI and Thermal Infrared Image

Vegetation has become very important decision-making information in promoting tasks such as urban regeneration, urban planning, environment, and landscaping. In the past, the vegetation index was calculated by combining images of various wavelength regions mainly acquired from the Landsat satellite’s TM or ETM+ sensor. Recently, a technology using UAV-based multispectral images has been developed to obtain more rapid and precise vegetation information. NDVI is a method of calculating the vegetation index by combining the red and near-infrared bands, and is currently the most widely used. In this study, NDVI was calculated using UAV-based multispectral images to classify vegetation. However, among the areas analyzed using NDVI, there was a problem that areas coated with urethane, such as basketball courts and waterproof coating roofs, were classified as vegetation areas. In order to examine these problems, the reflectance of each land cover was investigated using the ASD FieldSpec4 spectrometer. As a result of analyzing the spectrometer measurements, the NDVI values of basketball courts and waterproof coating roofs were similar to those of grass with slightly lower vegetation. To solve this problem, the temperature characteristics of the target site were analyzed using UAV-based thermal infrared images, and vegetation area was analyzed by combining the temperature information with NDVI. To evaluate the accuracy of the vegetation classification technology, 4409 verification points were selected, and kappa coefficients were analyzed for the method using only NDVI and the method using NDVI and thermal infrared images. Compared to the kappa coefficient of 0.830, which was analyzed by applying only NDVI, the kappa coefficient, which was analyzed by combining NDVI and thermal infrared images, was 0.934, which was higher. Therefore, it is very effective to apply a technology that classifies vegetation by combining NDVI and thermal infrared images in urban areas with many urethane-coated land cover such as basketball courts or waterproof coating roofs.

Has urbanization slowed down in Pakistan?

ABSTRACT Pakistan is the seventh largest contributor to world urban growth and exhibits high levels of urbanization. The recent 2017 Population Census results show a slowing of urban growth. We question whether this apparent slowdown reflects lowering of the rate of natural increase and migration, or is the result of a disconnect between the administrative definition of urban and actual reality. Alternative estimates presented here suggest that the 2017 Census may have underestimated urbanization by as much as 22.2 per cent, that is, the actual level of urbanization may be 44.5 per cent compared to 36.4 per cent reported in the census. We decompose the urban growth to assess the relative impact of natural increase, reclassification and migration and we utilize alternative methods of classification of urban areas to assess urbanization levels for Pakistan and its provinces. Continuing high levels of urban fertility and natural increase are the major contributors to urban growth. Internal migration is the next biggest contributor, however directions of movements may be changing. These findings have implications for the forthcoming 2022 census to improve the enumeration of urban areas and for urban planning to take advantage of the beneficial effects of building connectivity between small, medium, and large cities.

REDEFINING URBAN-RURAL BOUNDARIES FROM THE DIGITAL DISPARITY PERSPECTIVE

There is no international consensus on a comprehensive criterion to classify human settlements into urban and rural settlements. Different national criteria are used for delineating the borders between the two types of settlements. The main components of the criteria are population size, population density, population economic activity, administrative and legal and services and facilities. Whether all these criteria have been used or some of them, an outright socio-cultural and economic distinction between the two categories of settlements have developed over years around the world. The widely used virtual space during the pandemic provided people with access to facilities and services and enabled them to work for places that usually require their physical presence. The literature has not yet covered this point. Therefore, this paper aims at revisiting the classification of urban and rural areas in the COVID-19 aftermath. Through desk work and employing qualitative and quantitative research approaches, secondary data was collected from published relevant journals, reports, books, and websites. Content and comparative analysis for analysing qualitative data and content and quantitative comparative analysis and tabulation were used to carry out the research. This paper suggested that the world is in a transitional period towards full urban status. During this period, accessibility to virtual space can be used as a comprehensive criterion for calcifying human settlements into urban and rural.

ELSET: Design of an Ensemble Deep Learning Model for improving satellite image Classification Efficiency via Temporal Analysis

Satellite images with multispectral and panchromatic layers may contain a variety of information about the ROI (region of interests). Each layer displays distinct regional characteristics, allowing researchers to identify unique locations. Numerous machine learning algorithms are capable of identifying crop types, land cover types, soil types, etc. Researchers frequently use deep learning classifiers such as FCNN (Fully Connected Neural Network), RNN (Recurrent Neural Network), Q-learning, etc. as a result of their varying performance, applicability, complexity, and implementation costs. These networks are too general for classifications of application-specific satellite images. This paper proposes an improved deep learning model for real-time region-based satellite image classification in application-specific environments. ELSET captures large-scale temporal datasets using Google Earth Engine and removes outliers from these datasets using application-specific segmentation model filters. Image sets are segmented by an upgraded CNN model including VGGNet 19, GoogLeNet, and ResNet V2. VGGNet 19 outperforms other classification techniques for crop type and crop damage detection, GoogLeNet for land cover and forest cover classification, and ResNet V2 for classification of water bodies and urban areas. Combining these algorithms enables the identification of image layered regions with high precision and moderate complexity. Due to these techniques, the proposed model is capable of crop type recognition accuracy of 98.9%, land type identification accuracy of 95.4%, and water & urban cover area classification accuracy of 96.5%. The proposed model was evaluated at various locations and times with consistent precision. The precision, recall, AUC, and latency of the model were compared to state-of-the-art techniques. The proposed model demonstrated 5%, 8%, and 5.9% greater accuracy, recall, and AUC than current models. Due to increased categorization, the suggested model has significantly more latency than conventional alternatives. This paper provides researchers with strategies for context-specific satellite image classification that reduce this delay without compromising model performance levels.

In-Between the Lines and Pixels: Cartography’s Transition from Tool of the State to Humanitarian Mapping of Deprived Urban Areas

Cartography has been, in its pre-modern and modern production of maps, influential in determining how space and territory is experienced and defined. But advancements in telecommunications and geovisualization software, along with geoinformation systems and geoinformation science (GIS), have transformed cartographic practice from a tool of dominantly state apparatus to a scientific, commercial, and humanitarian enterprise. This is exemplified in the use of remote sensing (RS) techniques to acquire, process, and visualize images of the Earth. In the last decade, RS techniques have increasingly incorporated Artificial Intelligence (e.g., Convolutional Neural Networks) to improve the speed and accuracy of feature extraction and classification in remotely sensed images. This paper will investigate the use of CNNs in the classification of deprived urban areas referred to as “slums” and “informal settlements” in the Global South. Using a postphenomenological methodology, this paper shall analyze the role of classification and use of geoinformation in shaping how deprived urban areas are algorithmically classified. This analysis will reveal that besides the technical opportunities and challenges, attention needs to be given to three ethical areas of concern: how deprived area mapping using AI impacts the agency of communities, how there is a potential lack in the democratization of these RS technologies, and how the privacy and data protection of communities being mapped is endangered.

MM-UrbanFAC: Urban Functional Area Classification Model Based on Multimodal Machine Learning

Most of the classification methods of urban functional areas nowadays are only based on single source data analysis and modeling, which can not make full use of the multi-scale and multi-source data that is easy to obtain. Therefore, this paper proposed a classification model of urban functional areas based on multi-modal machine learning, by analyzing regional remote sensing images and behavior data of visitors in the area, using the combination of supervised methods extracted the deep-seated features and relationships of kinds of data, filtered and merged the overall and local features of the data. The model used dual branch neural network combining SE-ResNeXt and Dual Path Network (DPN) to automatically mined and fused the overall characteristics of multi-source data, and used the designed feature engineering to deeply mine the behavior data of users to obtain more association information, then combined the algorithm based on Gradient Boosting Decision Tree to learn the characteristics of different levels and obtained the classification probability for different levels of features. Finally, we continued to use the algorithm based on the Gradient Boosting Decision Tree to learn the probability distribution of different levels of features to obtain the final prediction results of urban functional area classification. Through the analysis and experimental verification of real data sets, the results showed that MM-UrbanFAC model can effectively integrate the features of multi-modal data. Compared with a single classifier, the integration framework based on gradient lifting tree improved the prediction performance, this method can effectively integrate the results of multiple models and accurately classify urban functional areas, and the model can provide reference for tourism recommendation, urban land planning and urban construction.

Application of the Poverty Equivalent Growth Rate (PEGR) Method (Case Study: Household Income Group in South Sulawesi 2016-2018)

The purpose of this study is to analyze pro-poor growth in South Sulawesi Province with an income dimension approach for the classification of urban and rural areas. This study reveals the poverty situation in South Sulawesi and the extent of the growth of the poor population. In addition, an overview of the effects of growth and distribution effects on changes in poverty is also obtained. Using SUSENAS data from South Sulawesi Province from 2016 to 2018, it was found that growth tends to have a greater impact on reducing poverty in rural areas than in urban areas based on the results of the Shapley decomposition. The results of calculating the degree of pro-poor growth with PEGR, show that income growth tends to be trickle-down from 2016 to 2017. The decline in the poverty rate did occur but the benefits of growth received by the poor were proportionally less than the non-poor. Income growth shows pro poor in the period from 2017 to 2018.

Integrating urban morphology and land surface temperature characteristics for urban functional area classification

ABSTRACT The classification of urban functional areas plays an important role in urban planning and resource management. Although previous studies have confirmed that different urban functional areas have different morphological structures and Land Surface Temperature (LST) characteristics, these two types of characteristics have rarely been fully integrated and used for functional area classification. In this paper, a new framework for classifying urban functional areas is proposed by combining urban morphological features and LST features. First, metrics are constructed from three levels, namely, building, road and region, which are used to portray urban morphology; LST is retrieved using thermal infrared remote sensing to reflect LST features with four metrics: the average temperature, maximum temperature, temperature difference and standard deviation of temperature. Then, the functional areas are classified into four categories: service/public land, commercial land, residential land and industrial land. A random forest algorithm is used to effectively fuse the features of these two categories and classify the functional areas. The effectiveness of the proposed framework is tested in the study area of Shenzhen City, Guangdong Province. The results show that the combined classification accuracy of the proposed classification method is 0.85, which is 0.26 higher than that of the classification model based on urban morphology and 0.1 higher than that of the classification model based on LST features. The proposed framework verifies that the integration of LST features into urban functional area classification is reliable and effectively combines urban morphology and LST features for functional area classification.

Fine-Grained Classification of Urban Functional Zones and Landscape Pattern Analysis Using Hyperspectral Satellite Imagery: A Case Study of Wuhan

Landscape mapping and pattern analysis of fine-grained urban functional zones (UFZ) are important in urban planning and urban development. Multi-spectral images have largely enabled only coarse-grained land-use classification of urban areas. By contrast, hyperspectral images have shown potentials to facilitate fine-grained classification of urban areas. In this paper, we evaluated and analyzed the classification of fine-grained UFZ in the central city of Wuhan, Hubei, China, using GaoFen-5 (GF-5) hyperspectral satellite imagery. We first compared the performance of hyperspectral data (GF-5) and multispectral data (Landsat 8) for the classification of fine-grained functional areas of cities by employing two classical classification algorithms and two deep learning methods. We also propose the deep learning-based SSUN-CRF algorithm, to enable better landscape pattern analysis. We then analyzed the landscape pattern of the seven administrative districts in the main urban areas of Wuhan by combining ten landscape indicators, based on the precise UFZ classification results obtained from the hyperspectral data. Experimental results illustrated the following: (1) Compared to multispectral images, hyperspectral images can allow for a more accurate UFZ classification. (2) Deep learning classification algorithms can better exploit hyperspectral image data, with the SSUN-CRF algorithm, in particular, being able to achieve an overall accuracy of 93.86% and a Kappa coefficient of 92.08%. (3) We were able to analyze the landscape pattern of Wuhans main urban areas based on the results of the UFZ classification. The results indicated that hyperspectral remote sensing imagery shows significant potentials in mapping fine-grained urban functional zones.

Detecção automática de áreas urbanas desfavorecidas usando imagens do Google Earth™ de cidades do semiárido brasileiro

Abstract Automatic classification of deprived urban areas provides vital information for implementing pro-poor policies. In this paper, an approach for the classification of these areas in Brazilian cities is presented. Satellite images were obtained free of charge from six cities in the Brazilian semi-arid region using Google Earth Engine software. To assess the discriminative power of census data, data made publicly available by Brazilian Institute of Geography and Statistics (IBGE) were used to train SVM classifiers together with features extracted from images. The image features were extracted using the following approaches: color histograms, LBP histograms, and lacunarity. Four evaluation tests were investigated based on two criteria: use of census data and cross-validation method. Two types of cross-validation were used: 10-fold and leave-one-city-out. The use of census data caused a negative impact on the results. This impact is justified by the criteria on which census tracts are mapped in the country, not only morphological and visually perceptible through satellite images, as opposed to adopted extraction approaches. The best results obtained were average accuracy of 91.81% and average F1-score of 92.27%. This research contributes to the recognition of deprived urban areas and urban socio-spatial dynamics, supporting urban-territorial planning.

Spatial classification of urban land by speculative land value, and MSI satellite imagery using K-MEANS, in Huancayo, Perú

The city of Huancayo, like other intermediate cities in Latin America, faces problems of poorly planned land-use changes and a rapid dynamic of the urban land market. The scarce and outdated information on the urban territory impedes the adequate classification of urban areas, limiting the form of its intervention. The purpose of this research was the adoption of unassisted and mixed methods for the spatial classification of urban areas, considering the speculative land value, the proportion of urbanized land, and other geospatial variables. Among the data collection media, Multi-Spectral Imagery (MSI) from the Sentinel-2 satellite, the primary road system, and a sample of direct observation points, were used. The processed data were incorporated into georeferenced maps, to which urban limits and official slopes were added. During data processing, the K-Means algorithm was used, together with other machine learning and assisted judgment methods. As a result, an objective classification of urban areas was obtained, which differs from the existing planning.