ISPRS Int. J. Geo-Inf., Volume 10, Issue 10 (October 2021) – 82 articles

Geospatial linked data are an emerging domain, with growing interest in research and the industry. There is an increasing number of publicly available geospatial linked data resources, which can also be interlinked and easily integrated with private and industrial linked data on the web. The present paper introduces Geo-L, a system for the discovery of RDF spatial links based on topological relations. Experiments show that the proposed system improves state-of-the-art spatial linking processes in terms of mapping time and accuracy, as well as concerning resources retrieval efficiency and robustness. Full article

Soil moisture is a critical component for Earth science studies, and Synthetic Aperture Radar (SAR) data have high potential for retrieving soil moisture using backscattering models. In this study, polarimetric SAR (PALSAR: Phased Array type L-band Synthetic Aperture Radar) data and polarimetric decompositions including span, entropy/H/alpha, and anisotropy, in combination with surface properties resulting from field and laboratory measurements, are used to categorize the natural surface condition and discriminate the backscatter parameter in the test site for applying the inversion soil moisture retrieval. The work aims to introduce the better of two examined models in the research for soil moisture retrieval over the bare land and sparse vegetation in arid regions. After soil moisture retrieval using the two different models, the results of comparison and validation by field measurement of soil moisture have shown that the Oh model has a more realiable accuracy for soil moisture mapping, although it was very difficult to find the best model due to different characteristics in land cover. It seems the inversion model, with the field observation and polarimetric SAR data, has a good potential for extracting surface natural conditions such as surface roughness and soil moisture; however, over- and under-estimation are observed due to land cover variability. The estimation of accurate roughness and moisture data for each type of land cover can increase the accuracy of the results. Full article

Travel blogs are a significant source for modeling human travelling behavior and characterizing tourist destinations owing to the presence of rich geospatial and thematic content. However, the bulk of unstructured text requires extensive processing for an efficient transformation of data to knowledge. Existing works have studied tourist places, but results lack a coherent outline and visualization of the semantic knowledge associated with tourist attractions. Hence, this work proposes place semantics extraction based on a fusion of content analysis and natural language processing (NLP) techniques. A weighted-sum equation model is then employed to construct a points of interest graph (POI graph) that integrates extracted semantics with conventional frequency-based weighting of tourist spots and routes. The framework offers determination and visualization of massive blog text in a comprehensible manner to facilitate individuals in travel decision-making as well as tourism managers to devise effective destination planning and management strategies. Full article

Continuous growth of building energy consumption CO₂ emission (BECCE) threatens urban sustainable development. Urban form is an important factor affecting BECCE. Compactness is a significant urban morphological characteristic. There is currently a lack of research on the effect of urban three-dimensional (3D) compactness on BECCE. To clarify the research value of 3D compactness, we investigated whether 3D compactness has a stronger impact on BECCE than two-dimensional (2D) compactness. A total of 288 buildings of the People’s Bank of China (PBOC) were divided into 5 zones according to building climate demarcation. As BECCE is affected mainly by four aspects (socioeconomic condition, building features, macroclimate, and urban form), the BECCE driven by urban form (BECCE-f) in each zone was calculated firstly using the partial least square regression model. Normalized compactness index (NCI) and normalized vertical compactness index (NVCI) were calculated with Python to quantify urban 2D and 3D compactness within a 1 km buffer of PBOC buildings. The mean NCI and NVCI values of each zone were adopted as 2D and 3D compactness of this zone. Gray correlation analysis of the five zones showed that the connection between the NVCI and BECCE-f is stronger than that between NCI and BECCE-f. Based on this, we believe that the emphasis of later research should be shifted to urban 3D form, not just 2D elements. 3D form can describe the real urban form in a more accurate and detailed manner. Emphasizing 3D morphological characteristics in studies of the relationship between urban form and building energy performance is more meaningful and valuable than only considering 2D characteristics. The impact mechanism of urban form on BECCE-f should also be analyzed from the perspective of 3D form. This study also provides beneficial solutions to building energy saving and low-carbon building construction. Full article

The most important protective measure in the pandemic process is a vaccine. The logistics and administration of the vaccine are as important as its production. The increasing diffusion of electronic devices containing geo-referenced information generates a large production of spatial data that are essential for risk management and impact mitigation, especially in the case of disasters and pandemics. Given that vaccines will be administered to the majority of people, it is inevitable to establish vaccination centres outside hospitals. Site selection of vaccination centres is a major challenge for the health sector in metropolitan cities due to the dense population and high number of daily cases. A poor site selection process can cause many problems for the health sector, workforce, health workers, and patients. To overcome this, a three-step solution approach is proposed: (i) determining eight criteria using from the experience of the advisory committee, (ii) calculating criterion weights using Analytic Hierarchy Process (AHP), and performing spatial analysis of criteria using Geographic Information System (GIS), (iii) assigning potential vaccination centres by obtaining a suitability map and determining service areas. A case study is performed for Bağcılar, Istanbul district, using the proposed methodology. The results show that the suitable areas are grouped in three different areas of the district. The proposed methodology provides an opportunity to execute a scientific and strategic vaccination programme and to create a map of suitable vaccination centres for the countries. Full article

Moving into the third decade of the 21st century, smart cities are becoming a vital concept of advancement of the quality of life. Without any doubt, cities today can generate data of high velocity which can be used in plethora of applications. The wind flow inside a city is an area of several studies which span from pedestrian comfort and natural ventilation to wind energy yield. We propose a Visual Analytics platform based on a server-client web architecture capable of identifying areas with high wind energy potential by employing 3D technologies and Open Geospatial Consortium (OGC) standards. The assessment of a whole city or sub-regions will be supported by integrating Computational Fluid Dynamics (CFD) outcomes with historical wind sensor readings. The results, in 3D space, of such analysis could be used by a wide audience, including city planners and citizens, for locating installation points of small-scale horizontal or vertical axis wind turbines in an urban area. A case study in an urban quarter of Stuttgart is used to evaluate the interactiveness of the proposed workflow. The results show an adequate performance, although there is a lot of room for improvement in future work. Full article

Since many Wi-Fi routers can currently transmit two-band signals, we aimed to study dual-band Wi-Fi to achieve better positioning results. Thus, this paper proposes a fingerprint positioning method for dual-band Wi-Fi based on Gaussian process regression (GPR) and the K-nearest neighbor (KNN) algorithm. In the offline stage, the received signal strength (RSS) measurements of the 2.4 GHz and 5 GHz signals at the reference points (RPs) are collected and normalized to generate the online dual-band fingerprint, a special fingerprint for dual-band Wi-Fi. Then, a dual-band fingerprint database, which is a dedicated fingerprint database for dual-band Wi-Fi, is built with the dual-band fingerprint and the corresponding RP coordinates. Each dual-band fingerprint constructs its positioning model with the GPR algorithm based on itself and its neighborhood fingerprints, and its corresponding RP coordinates are the label of this model. The neighborhood fingerprints are found by the spatial distances between RPs. In the online stage, the measured RSS values of dual-band Wi-Fi are used to generate the online dual-band fingerprint and the 5 GHz fingerprint. Due to the better stability of the 5 GHz signal, an initial position is solved with the 5 GHz fingerprint and the KNN algorithm. Then, the distances between the initial position and model labels are calculated to find a positioning model with the minimum distance, which is the optimal positioning model. Finally, the dual-band fingerprint is input into this model, and the output of this model is the final estimated position. To evaluate the proposed method, we selected two scenarios (A and B) as the test area. In scenario A, the mean error (ME) and root-mean-square error (RMSE) of the proposed method were 1.067 and 1.331 m, respectively. The ME and RMSE in scenario B were 1.432 and 1.712 m, respectively. The experimental results show that the proposed method can achieve a better positioning effect compared with the KNN, Rank, Coverage-area, and GPR algorithms. Full article

The relief degree of land surface (RDLS) was often calculated to describe the topographic features of a region. It is a significant factor in designing urban street networks. However, existing studies do not clarify how RDLS affects the distribution of urban street networks. We used a Python package named OSMnx to extract the street networks of different cities in China. The street complexity metrics information (i.e., street grain, connectedness, circuity, and street network orientation entropy) were obtained and analyzed statistically. The results indicate that street network exhibits more complexity in regions with high RDLS. Further analysis of the correlation between RDLS and street network complexity metrics indicates that RDLS presents the highest correlation with street network circuity; that is, when RDLS is higher, the routes of an urban street network is more tortuous, and the additional travel costs for urban residents is higher. This study enriches and expands research on street networks in China, providing a reference value for urban street network planning. Full article

The current application of local climate zones (LCZs) often ends with (inter)zonal comparation of land surface temperature (LST) or air temperature (AT). LST evaluation employs an enhanced concept of LCZs together with cluster analysis for LCZs grouped based on LST. The paper attempts to combine them into a complex approach derived from the case study on a medium-sized Central European city (Hradec Králové, the Czech Republic). In particular, the paper addresses the following. (i) The relation of LST and AT, when the daily course of temperature profile ranging clear off the surface up to 2 m was fitted by a rational 2D function. The obtained equation enables derivation of the AT from LST and vice versa. (ii) The differences in thermal response of LCZs based on LST or AT, where the highest average LST and average maximum LST show LCZs 10, 2, 3 and 8, i.e., with a significant proportion of artificial surfaces. The cluster of LCZs with a significant representation of vegetation, LCZs 9, B, D, A and G, have significantly lower LST. (iii) The contribution of LCZs to understanding of LST/AT relation and whether their specific relation could be expected in particular LCZs, when subsequent interaction assessment of LST and AT revealed statistically their significant correlation in LCZs for certain cases. Full article

Taxi waiting times is an important criterion for taxi passengers to choose appropriate pick-up locations in urban environments. How to predict the taxi waiting time accurately at a certain time and location is the key solution for the imbalance between the taxis’ supplies and demands. Considering the life schedule of urban residents and the different functions of geogrid regions, the research developed in this paper introduces a spatio-temporal schedule-based neural network for urban taxi waiting time prediction. The approach integrates a series of multi-source data from taxi trajectories to city points of interest, different time frames and human behaviors in the city. We apply a grid-based and functional structuration of an urban space that provides a lower-level data representation. Overall, the neural network model can dynamically predict the waiting time of taxi passengers in real time under some given spatio-temporal constraints. The experimental results show that the granular-based grids and spatio-temporal neural network can effectively predict and optimize the accuracy of taxi waiting times. This work provides a decision support for intelligent travel predictions of taxi waiting time in a smart city. Full article

Despite the trending studies on smart city development, how to evaluate the smartness of a city remains unclear. This research aimed to design a smart city evaluation system, named the City Intelligence Quotient (CityIQ) evaluation system, which considers both the hard (e.g., physical infrastructure) and soft sides (e.g., citizens’ perspectives) of smart city development. Based on the two-level structure of the CityIQ evaluation system (i.e., five dimensions and twenty indicators), a list of keywords was defined for automated information scraping in leading social media platforms to obtain volunteered geographic information. Semantic analysis was then used to update the CityIQ evaluations in a timely manner. Fifteen major cities in the Yangtze River Delta region, China, were selected for the empirical study, in which their smartness indices were calculated, traced and compared. Finally, suggestions for collaborative smart agglomerations were put forward. With the CityIQ evaluation system, policy makers can be informed of up-to-date changes in urban smartness levels and, thus, design context-specific collaborative policies to promote smart agglomerations. Full article

The question of whether each building of housing estate has equal access to nearby social service resources (e.g., public transportation service, catering, entertainment, etc.) is a major concern of citizens. This paper takes Wuhan as a case to explore the equality in social service resource sharing of the housing estate at a microscopic level by analyzing the accessibility of each building under different travel patterns. To estimate the accessibility of each building, we developed a novel model with multi-travel modes and residential suitability evaluation of residents. The specific values of the parameters involved in the proposed model were extracted from the multi-source spatial data such as social media data, census data, point of interest, and road network data. These data were acquired from multiple platforms, e.g., Gaode map, OSM (OpenStreetMap), and GeoQ. We chose three types of districts in the city of Wuhan, including the old central district, new central district, and suburban district. We applied the proposed model to assess the accessibility of communities in these districts. Based on the results, we further analyzed whether and to what extent the distribution of each building in urban communities is equitable for social service resource sharing in China. Full article

Indoor maps lay the foundation for most indoor location-based services (LBS). Building Information Modeling (BIM) data contains multiple dimensional computer-aided design information. Some studies have utilized BIM data to automatically extract 3D indoor maps. A complete 3D indoor map consists of both floor-level maps and cross-floor paths. Currently, the floor-level indoor maps are mainly either grid-based maps or topological maps, and the cross-floor path generation schemes are not adaptive to building elements with irregular 3D shapes. To address these issues, this study proposes a novel scheme to extract an accurate 3D indoor map with any shape using BIM data. Firstly, this study extracts grid-based maps from BIM data and generates the topological maps directly through the grid-based maps using image thinning. A novel hybrid indoor map, termed Grid-Topological map, is then formed by the grid-based maps and topological maps jointly. Secondly, this study obtains the cross-floor paths from cross-floor building elements by a four-step process, namely X-Z projection, boundary extraction, X-Z topological path generation, and path-BIM intersection. Finally, experiments on eight typical types of cross-floor building elements and three multi-floor real-world buildings were conducted to prove the effectiveness of the proposed scheme, the average accuracy rates of the evaluated paths are higher than 88%. This study will advance the 3D indoor maps generation and inspire the application of indoor maps in indoor LBS, indoor robots, and 3D geographic information systems. Full article

The real-time kinematic positioning technique (RTK) and visual–inertial odometry (VIO) are both promising positioning technologies. However, RTK degrades in GNSS-hostile areas, where global navigation satellite system (GNSS) signals are reflected and blocked, while VIO is affected by long-term drift. The integration of RTK and VIO can improve the accuracy and robustness of positioning. In recent years, smartphones equipped with multiple sensors have become commodities and can provide measurements for integrating RTK and VIO. This paper verifies the feasibility of integrating RTK and VIO using smartphones, and we propose an improved algorithm to integrate RTK and VIO with better performance. We began by developing an Android smartphone application for data collection and then wrote a Python program to convert the data to a robot operating system (ROS) bag. Next, we established two ROS nodes to calculate the RTK results and accomplish the integration. Finally, we conducted experiments in urban areas to assess the integration of RTK and VIO based on smartphones. The results demonstrate that the integration improves the accuracy and robustness of positioning and that our improved algorithm reduces altitude deviation. Our work can aid navigation and positioning research, which is the reason why we open source the majority of the codes at our GitHub. Full article

Integration development of urban agglomeration is important for regional economic research and management. In this paper, a method was proposed to study the integration development of urban agglomeration by trajectory gravity model. It can analyze the gravitational strength of the core city to other cities and characterize the spatial trajectory of its gravitational direction, expansion, etc. quantitatively. The main idea is to do the fitting analysis between the urban axes and the gravitational lines. The correlation coefficients retrieved from the fitting analysis can reflect the correlation of two indices. For the different cities in the same year, a higher value means a stronger relationship. There is a clear gravitational force between the cities when the value above 0.75. For the most cities in different years, the gravitational force between the core city with itself is increasing by years. At the same time, the direction of growth of the urban axes tends to increase in the direction of the gravitational force between cities. There is a clear tendency for the trajectories of the cities to move closer together. The proposed model was applied to the integration development of China Liaoning central urban agglomeration from 2008 to 2016. The results show that cities are constantly attracted to each other through urban gravity. Full article

The aim of the paper is to identify a suitable method for the construction of a 3D city model from stereo satellite imagery. In order to reach this goal, it is necessary to build a workflow consisting of three main steps: (1) Increasing the geometric resolution of the color images through the use of pan-sharpening techniques, (2) identification of the buildings’ footprint through deep-learning techniques and, finally, (3) building an algorithm in GIS (Geographic Information System) for the extraction of the elevation of buildings. The developed method was applied to stereo imagery acquired by WorldView-2 (WV-2), a commercial Earth-observation satellite. The comparison of the different pan-sharpening techniques showed that the Gram–Schmidt method provided better-quality color images than the other techniques examined; this result was deduced from both the visual analysis of the orthophotos and the analysis of quality indices (RMSE, RASE and ERGAS). Subsequently, a deep-learning technique was applied for pan sharpening an image in order to extract the footprint of buildings. Performance indices (precision, recall, overall accuracy and the F₁measure) showed an elevated accuracy in automatic recognition of the buildings. Finally, starting from the Digital Surface Model (DSM) generated by satellite imagery, an algorithm built in the GIS environment allowed the extraction of the building height from the elevation model. In this way, it was possible to build a 3D city model where the buildings are represented as prismatic solids with flat roofs, in a fast and precise way. Full article

A spatial co-location pattern denotes a subset of spatial features whose instances frequently appear nearby. High influence co-location pattern mining is used to find co-location patterns with high influence in specific aspects. Studies of such pattern mining usually rely on spatial distance for measuring nearness between instances, a method that cannot be applied to an influence propagation process concluded from epidemic dispersal scenarios. To discover meaningful patterns by using fruitful results in this field, we extend existing approaches and propose a mining framework. We first defined a new concept of proximity to depict semantic nearness between instances of distinct features, thus applying a star-shaped materialized model to mine influencing patterns. Then, we designed attribute descriptors to perceive attributes of instances and edges from time series data, and we calculated the attribute weights via an analytic hierarchy process, thereby computing the influence between instances and the influence of features in influencing patterns. Next, we constructed influencing metrics and set a threshold to discover high influencing patterns. Since the metrics do not satisfy the downward closure property, we propose two improved algorithms to boost efficiency. Extensive experiments conducted on real and synthetic datasets verified the effectiveness, efficiency, and scalability of our method. Full article

The effect of the plum rain weather event on cycling trips reflects the climate resilience of the public bicycle system. However, quantitative studies regarding the impact of plum rain on public bicycle users and corresponding spatial heterogeneity have not been paid much attention. This paper explores the spatial pattern of affected levels from the perspective of cyclist number, place semantics and riding distance. Corresponding public bicycle trips in normal weather are predicted by spatial-temporal random forest prediction. GIS neighborhood statistics and clustering algorithms are adapted to analyze and visualize the affected levels using origin-destination data of public bicycle trips and point of interest data of city public facilities. It is proved that there is an obvious spatial difference in affected levels by plum rain from three dimensions. In the dimension of the number of cyclists, the docking stations with different affected levels are distributed across the whole urban area. In the place semantic dimension, the docking stations with high affected levels show a clustered zonal distribution in the city center. In the dimension of cycling distance, the docking stations with high affected levels are mainly distributed in the periphery of the central urban area. The study theoretically expands the impact mechanism of environment and active transport. It is beneficial for the early monitoring, warning and assessment of climate change risks for public bicycle planning and management. Full article

At the end of the 20th century, the phenomenon of urban shrinkage received widespread attention, with population decline as its core characteristic. In 2020, the Taiwanese population had negative growth and faced a low fertility rate and an aging population. This study used exploratory spatial data analysis to identify shrinking cities in Taiwan based on census data and population registers. The results indicated that Taiwan has 11 shrinking counties and 202 shrinking towns. Urban shrinkage occurred in the 1980s and continued from the suburbanization stage to the re-urbanization stage. Five types of spatial patterns in the 11 shrinking counties were observed. In the majority of the shrinking counties, towns with high population densities were unable to avoid shrinkage. A global spatial autocorrelation analysis indicated that shrinkage and non-shrinkage have become increasingly apparent at the town level since 2005. A local spatial autocorrelation analysis indicates that the spatial clustering of towns with population growth or decline from 2000 to 2020 has changed. Based on each town’s development, a two-step cluster analysis was conducted in which all towns were divided into four categories. Shrinking towns exist in each category, but with a different proportion. Based on the results of two-step cluster analysis combined with spatial analysis, this study discovered that both urbanization and suburbanization cause shrinkage in Taiwan, but the affected localities are distinct. For most shrinking counties, their spatial model indicates a relationship between shrinking and the urbanization of their towns. Keelung City and Chiayi City have the most potential to reverse the shrinkage. This study helps authorities better manage growth and implement regional revitalization. Full article

Quantitative analysis of the differences and the exploration of the evolution models of different loess landform types are greatly important to the in-depth understanding of the evolution process and mechanism of the loess landforms. In this research, several typical loess landform areas in the Chinese Loess Plateau were selected, and the object-oriented image analysis (OBIA) method was employed to identify the basic loess landform types. Three-dimensional (3D) landscape pattern indices were introduced on this foundation to measure the morphological and structural features of individual loess landform objects in more detail. Compared with the traditional two-dimensional (2D) landscape pattern indices, the indices consider the topographic features, thereby providing more vertical topographic information. Furthermore, the evolution modes between different loess landform types were discussed. Results show that the OBIA method achieved satisfying classification results with an overall accuracy of 88.12%. There are evident differences in quantitative morphological indicators among loess landform types, especially in indicators such as total length of edge, mean patch size, landscape shape index, and edge dimension index. Meanwhile, significant differences are also found in the combination of loess landform types corresponding to different landform development stages. The degree of surface erosion became increasingly significant as loess landforms developed, loess tableland area rapidly reduced or even vanished, and the dominant loess landform types changed to loess ridge and loess hill. Hence, in the reconstruction and management of the Loess Plateau, the loess tableland should be the key protected loess landform type. These preliminary results are helpful to further understand the development process of loess landforms and provide a certain reference for regional soil and water conservation. Full article

Crisis maps play a significant role in emergency responses. Users are challenged to interpret a map rapidly in emergencies, with limited visual information-processing resources and under time pressure. Therefore, cartographic techniques are required to facilitate their map cognition. In this study, we analyzed the exogenous and endogenous disruptions that users needed to overcome when they were reading maps. The analysis results suggested that cartographers’ taking the stressors into consideration could promote the cognitive fit between cartographers and users, improving map cognition and spatial information supply–demand matching. This paper also elaborates the course of map visual information processing and related graphic variables to visual attention attributes. To improve the users’ map cognition in time-critical emergency situations, crisis map design principles and a methodology were proposed. We developed three fire emergency rescue road maps and performed two evaluations to verify the effectiveness of the principles. Our experiments showed that the principles could effectively facilitate the users’ rapid map perception and proper understanding, by reducing their cognitive load, and could improve the quality of the crisis maps to some extent. Full article

The outbreak of COVID-19 has constantly exposed health care workers (HCWs) around the world to a high risk of infection. To more accurately discover the infection differences among high-risk occupations and institutions, Hubei Province was taken as an example to explore the spatiotemporal characteristics of HCWs at different scales by employing the chi-square test and fitting distribution. The results indicate (1) the units around the epicenter of the epidemic present lognormal distribution, and the periphery is Poisson distribution. There is a clear dividing line between lognormal and Poisson distribution in terms of the number of HCWs infections. (2) The infection rates of different types of HCWs at multiple geospatial scales are significantly different, caused by the spatial heterogeneity of the number of HCWs. (3) With the increase of HCWs infection rate, the infection difference among various HCWs also gradually increases and the infection difference becomes more evident on a larger scale. The analysis of the multi-scale infection rate and statistical distribution characteristics of HCWs can help government departments rationally allocate the number of HCWs and personal protective equipment to achieve distribution on demand, thereby reducing the mental and physical pressure and infection rate of HCWs. Full article

The paradigms of taxis and ride-hailing, the two major players in the personal mobility market, are compared systematically and empirically in a unified spatial–temporal context. Supported by real field data from Xiamen, China, this research proposes a three-fold analytical framework to compare their mobilities, including (1) the spatial distributions of departures and arrivals by rank–size and odds ratio analysis, (2) the statistical characteristics of trip distances by spatial statistics and considering distance-decay effect, and (3) the meta-patterns inherent in the mobility processes by nonnegative tensor factorization. Our findings suggest that taxis and ride-hailing services share similar spatial patterns in terms of travel demand, but taxi demand heterogenizes more quickly with changes in population density. Additionally, the relative balance between the taxi industry and ride-hailing services shows opposite trends inside and outside Xiamen Island. Although the trip distances have similar statistical properties, the spatial distribution of the median trip distances reflects different urban structures. The meta-patterns detected from the origin–destination-time system via tensor factorization suggest that taxi mobilities feature exclusive nighttime intensities, whereas ride-hailing exhibits more prominent morning peaks on weekdays. Although ride-hailing contributes significantly to cross–strait interactions during daytime, there is a lack of efficient services to maintain such interactions at night. Full article

Monthly precipitation data during the period of 1970 to 2019 obtained from the Meteorological, Climatological and Geophysical Agency database were used to analyze regionalized precipitation regimes in Yogyakarta, Indonesia. There were missing values in 52.6% of the data, which were handled by a hybrid random forest approach and bootstrap method (RF-Bs). The present approach addresses large missing values and also reduces the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) in the search for the optimum minimal value. Cluster analysis was used to classify stations or grid points into different rainfall regimes. Hierarchical clustering analysis (HCA) of rainfall data reveal the pattern of behavior of the rainfall regime in a specific region by identifying homogeneous clusters. According to the HCA, four distinct and homogenous regions were recognized. Then, the principal component analysis (PCA) technique was used to homogenize the rainfall series and optimally reduce the long-term rainfall records into a few variables. Moreover, PCA was applied to monthly rainfall data in order to validate the results of the HCA analysis. On the basis of the 75% of cumulative variation, 14 factors for the Dry season and the Rainy season, and 12 factors for the Inter-monsoon season, were extracted among the components using varimax rotation. Consideration of different groupings into these approaches opens up new advanced early warning systems in developing recommendations on how to differentiate climate change adaptation- and mitigation-related policies in order to minimize the largest economic damage and taking necessary precautions when multiple hazard events occur. Full article

The urban ecological environment is related to human health and is one of the most concerned issues nowadays. Hence, it is essential to detect and then evaluate the urban ecological environment. However, the conventional manual detection methods have many limitations, such as the high cost of labor, time, and capital. The aim of this paper is to evaluate the urban ecological environment more conveniently and reasonably, thus this paper proposed an ecological environment evaluation method based on remote sensing and a projection pursuit model. Firstly, a series of criteria for the urban ecological environment in Shanghai City are obtained through remote sensing technology. Then, the ecological environment is comprehensively evaluated using the projection pursuit model. Lastly, the ecological environment changes of Shanghai City are analyzed. The results show that the average remote sensing ecological index of Shanghai in 2020 increased obviously compared with that in 2016. In addition, Jinshan District, Songjiang District, and Qingpu District have higher ecological environment quality, while Hongkou District, Jingan District, and Huangpu District have lower ecological environment quality. In addition, the ecological environment of all districts has a significant positive spatial autocorrelation. These findings suggest that the ecological environment of Shanghai has improved overall in the past five years. In addition, Hongkou District, Jingan District, and Huangpu District should put more effort into improving the ecological environment in future, and the improvement of ecological environment should consider the impact of surrounding districts. Moreover, the proposed weight setting method is more reasonable, and the proposed evaluation method is convenient and practical. Full article

The classification and recognition of the shapes of buildings in map space play an important role in spatial cognition, cartographic generalization, and map updating. As buildings in map space are often represented as the vector data, research was conducted to learn the feature representations of the buildings and recognize their shapes based on graph neural networks. Due to the principles of graph neural networks, it is necessary to construct a graph to represent the adjacency relationships between the points (i.e., the vertices of the polygons shaping the buildings), and extract a list of geometric features for each point. This paper proposes a deep point convolutional network to recognize building shapes, which executes the convolution directly on the points of the buildings without constructing the graphs and extracting the geometric features of the points. A new convolution operator named TriangleConv was designed to learn the feature representations of each point by aggregating the features of the point and the local triangle constructed by the point and its two adjacency points. The proposed method was evaluated and compared with related methods based on a dataset consisting of 5010 vector buildings. In terms of accuracy, macro-precision, macro-recall, and macro-F1, the results show that the proposed method has comparable performance with typical graph neural networks of GCN, GAT, and GraphSAGE, and point cloud neural networks of PointNet, PointNet++, and DGCNN in the task of recognizing and classifying building shapes in map space. Full article

The profession debates how to encode a categorical variable for input to machine learning algorithms, such as neural networks. A conventional approach is to convert a categorical variable into a collection of binary variables, which causes a burdensome number of correlated variables. TerrSet’s Land Change Modeler proposes encoding a categorical variable onto the continuous closed interval from 0 to 1 based on each category’s Population Evidence Likelihood (PEL) for input to the Multi-Layer Perceptron, which is a type of neural network. We designed examples to test the wisdom of these encodings. The results show that encoding a categorical variable based on each category’s Sample Empirical Probability (SEP) produces results similar to binary encoding and superior to PEL encoding. The Multi-Layer Perceptron’s sigmoidal smoothing function can cause PEL encoding to produce nonsensical results, while SEP encoding produces straightforward results. We reveal the encoding methods by illustrating how a dependent variable gains across an independent variable that has four categories. The results show that PEL can differ substantially from SEP in ways that have important implications for practical extrapolations. If users must encode a categorical variable for input to a neural network, then we recommend SEP encoding, because SEP efficiently produces outputs that make sense. Full article

Today, a building is not just a “body” or a “machine” as defined by modern architecture, but rather an immaterial entity immersed in a digital world where not only its components but also the information associated with it are accounted for. In recent decades, building information modelling (BIM) has made it possible to move from 2D CAD drawings to 3D models capable of supporting different processes and interacting with different disciplines in the AEC industry for storing, documenting and sharing heterogeneous content. It has thus become possible to direct these techniques towards built heritage to investigate new forms of communication and share heritage building information modelling (HBIM) models. This research investigates this evolution in both generative terms (scan-to-BIM process) and cultural and historical terms in order to orient BIM uses towards novel forms of interactivity and immersion between users and models. The author proposes the use of a digital process and the development of VR and AR environments based on a visual programming language (VPL) to improve access to a deeper knowledge of HBIM models and the artefacts and information contained therein. Full article

In recent years, considerable efforts have been made by cultural heritage institutions across the globe to digitise cultural heritage sites, artifacts, historical maps, etc. for digital preservation and online representation. On the other hand, ample research projects and studies have been published that demonstrate the great capabilities of web-geographic information systems (web-GIS) for the dissemination and online representation of cultural heritage data. However, cultural heritage data and the associated metadata produced by many cultural heritage institutions are heterogeneous. To make this heterogeneous data more interoperable and structured, an ever-growing number of cultural heritage institutions are adopting linked data principles. Although the cultural heritage domain has already started implementing linked open data concepts to the cultural heritage data, there are not many research articles that present an easy-to-implement, free, and open-source-based web-GIS architecture that integrates 3D digital cultural heritage models with cloud computing and linked open data. Furthermore, the integration of web-GIS technologies with 3D web-based visualisation and linked open data may offer new dimensions of interaction and exploration of digital cultural heritage. To demonstrate the high potential of integration of these technologies, this study presents a novel cloud architecture that attempts to enhance digital cultural heritage exploration by integrating 3D digital cultural heritage models with linked open data from DBpedia and GeoNames platforms using web-GIS technologies. More specifically, a digital interactive map, 3D digital cultural heritage models, and linked open data from DBpedia and GeoNames platforms were integrated into a cloud-based web-GIS architecture. Thus, the users of the architecture can easily interact with the digital map, visualise 3D digital cultural heritage models, and explore linked open data from GeoNames and DBpedia platforms, which offer additional information and context related to the selected cultural heritage site as well as external web resources. The architecture was validated by applying it to specific case studies of Australian cultural heritage and seeking expert feedback on the system, its benefits, and scope for improvement in the near future. Full article

Understanding how cities evolve through time and how humans interact with their surroundings is a complex but essential task that is necessary for designing better urban environments. Recent developments in artificial intelligence can give researchers and city developers powerful tools, and through their usage, new insights can be gained on this issue. Discovering a high-level structure in a set of observations within a low-dimensional manifold is a common strategy used when applying machine learning techniques to tackle several problems while finding a projection from and onto the underlying data distribution. This so-called latent manifold can be used in many applications such as clustering, data visualization, sampling, density estimation, and unsupervised learning. Moreover, data of city patterns has some particularities, such as having superimposed or natural patterns that correspond to those of the depicted locations. In this research, multiple manifolds are explored and derived from city pattern images. A set of quantitative and qualitative tests are proposed to examine the quality of these manifolds. In addition, to demonstrate these tests, a novel specialized dataset of city patterns of multiple locations is created, with the dataset capturing a set of recognizable superimposed patterns. Full article