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The regions of East Asia, as well as South Asia and the Middle East are rich in cultural heritage of grottoes where a large number of Buddhist niches exist. Three-dimensional (3D) semantic modelling enriches 3D geometric models with an understanding of the historical and cultural value of cultural heritage, facilitating interoperability and analysis beyond mere visualization. 3D models with semantic information act as essential digital infrastructure for heritage management, knowledge dissemination and simulation analysis in cultural heritage. However, due to the lack of standardized data model for the grotto domain, it is difficult to exchange information, share knowledge and the advancement of spatial analysis and simulation. In order to fill the above-mentioned gap, this study develops a data model for niches as a CityGML Application Domain Extension (ADE) based on the CityGML 3.0 standard. In this ADE, niche components and their attributes are defined on two semantic levels, i.e., niche structural component and niche component member. A famous niche in China belonging to the World Heritage was selected as a case study to demonstrate the integration of geometries, semantics and attributes, illustrating that the extended ADE module complies with the CityGML 3.0 standard. This study provides novel insights into the 3D semantic modelling of niches as well as expands the applications scope of CityGML standard within the cultural heritage sector. [ABSTRACT FROM AUTHOR]

Floods are one of the most serious natural disasters that can cause huge societal and economic losses. Extensive research has been conducted on topics like flood monitoring, prediction, and loss estimation. In these research fields, flood velocity plays a crucial role and is an important factor that influences the reliability of the outcomes. Traditional methods rely on physical models for flood simulation and prediction and could generate accurate results but often take a long time. Deep learning technology has recently shown significant potential in the same field, especially in terms of efficiency, helping to overcome the time-consuming associated with traditional methods. This study explores the potential of deep learning models in predicting flood velocity. More specifically, we use a Multi-Layer Perceptron (MLP) model, a specific type of Artificial Neural Networks (ANNs), to predict the velocity in the test area of the Lundesokna River in Norway with diverse terrain conditions. Geographic data and flood velocity simulated based on the physical hydraulic model are used in the study for the pre-training, optimization, and testing of the MLP model. Our experiment indicates that the MLP model has the potential to predict flood velocity in diverse terrain conditions of the river with acceptable accuracy against simulated velocity results but with a significant decrease in training time and testing time. Meanwhile, we discuss the limitations for the improvement in future work. [ABSTRACT FROM AUTHOR]

OpenStreetMap (OSM) as one of the most successful projects of Volunteered Geographical Information (VGI) has attracted millions of contributors to work together and produces massive open geographical data. However, the co-work does not always run smoothly since mapping can involve conflicted understandings of the reality. In this paper, we investigate behaviors of mapping related to territorial disputes to reveal the characteristics of contributions and examine the contradictions between ground truth as the vision of OSM and the theory of critical cartography. We perform our experiments from the perspectives of entities, changesets, and contributors using the full history data of OSM. The experiments show that territorial-dispute-related contributions have substantially different characteristics from various aspects but they cannot be treated as outliers either, considering that most contributors do not focus on disputed boundaries. Interpreting OSM data as a converging state to ground truth or equally opinions can both be inaccurate. We also find that mapping disputes may not be absolutely negative in a VGI project. We perform quantitative, large-scale (global) analysis of dispute-related mapping. The results show that territorial-dispute-related contributions and contributors are different from contributions and contributors in general. Territorial-dispute-related mapping is not an independent phenomenon for OSM. The contributors make much more disputes-unrelated contributions. Dispute-related entities have more (divergent) versions than normal boundaries, attract more participants, and are more semantically complete, especially for names. Dispute-related changesets generally attract more discussions. The spatial distribution of the dispute-related changesets is consistent with real-world territorial disputes and very different from that of all boundary-related changesets and all changesets. Contributors who participate in dispute-related contributions are generally more active. These users tend to have a special interest in boundaries but most of them do not focus on disputed boundaries. [ABSTRACT FROM AUTHOR]

Roof vertex information is vital for 3D roof structures. Reconstructing 3D roof structures from point cloud data using traditional methods remains a challenge because their extracted roof vertices are affected by uncertainty and additional errors from roof plane segmentation and supplementary sub-steps for extracting primitives. In this study, instead of segmenting roof planes and then extracting primitives based on them, a flexible rule-based method is proposed to directly detect the vertices of building roofs from point cloud data without the requirement of training data. The point cloud data is first voxelized with a dominant direction-based rotation. Based on the different features of the interior roof points and vertices, rules for voxel filtering and structure line determination are defined to extract the roof vertices. The experimental results on a custom dataset in Trondheim, Norway demonstrate that the proposed method can effectively and accurately extract roof vertices from point cloud data. The comparative experimental results with an unfine-tuned deep learning-based method on custom and benchmark datasets with different point densities further show that the proposed method has good generalization and can adapt to changes of datasets. [ABSTRACT FROM AUTHOR]

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Extracting precise location information from microblogs is a crucial task in many applications, particularly in disaster response, revealing where damages are, where people need assistance, and where help can be found. A crucial prerequisite to location extraction is place name extraction. In this paper, we present GazPNE: a hybrid approach to place name extraction which fuses rules, gazetteers, and deep learning techniques without requiring any manually annotated data. The core of the approach is to learn the intrinsic characteristics of multi-word place names with deep learning from gazetteers. Specifically, GazPNE consists of a rule-based system to select n-grams from the microblogs that potentially contain place names, and a C-LSTM model that decides if the selected n-gram is a place name or not. The C-LSTM is trained on 388.1 million examples containing 6.8 million positive examples with US and Indian place names extracted from OpenStreetMap and 381.3 million negative examples synthesized by rules. We evaluate GazPNE against the SoTA on a manually annotated 4,500 tweet dataset which contains 9,026 place names from three foods: 2016 in Louisiana (US), 2016 in Houston (US), and 2015 in Chennai (India). GazPNE achieves SotA performance on the test data with an F1 of 0.84. [ABSTRACT FROM AUTHOR]

Public medical facilities that are closely related to the health of residents have been recognised as one of the most crucial elements in sustainable urban planning. For the sake of social equality of medical services (especially for emergency medical conditions), the spatial distributions of medical resources need to be accurately measured and continuously optimized. This study presents an effective method to examine night emergency hospital visit and analyse its spatiotemporal characteristics using float car data (FCD). By extracting the hospital service areas, the two-step floating catchment area (2SFCA) methodology was improved to calculate hospital accessibility. Then, the balance between hospital accessibility and population density was analysed. In addition, we investigated the relationship between individual hospital choice preferences and hospital level and analysed several factors that affect individual choices. These results help us understand the special requirements and need of emergency hospital travel in cities and identify areas where medical resources are scarce. They can be used as guidance for urban hospital planning and construction. And the approach of hospital access behaviour investigation and the improved 2SFCA method can also provide insights for other activity-based travel behaviour research. [ABSTRACT FROM AUTHOR]

Façade parsing is an essential process before the 3-D modeling of digital or virtual 3-D city models. The existing grammar-based approaches for façade parsing rely on strong prior knowledge but can obtain façade parts with better structure. Pixelwise-segmentation-based approaches achieve façade parsing with much less knowledge but the resulting structure of façade parts is normally incomplete. Both these approaches are restricted by their high reliance on the data set. Therefore, they cannot be applied for façade parsing with complex scenes. To address this issue, we built a large street-level data set by taking Mapillary images as the training data for more general scenes. At the same time, we propose a new pipeline based on convolutional neural network (CNN) that combines pixelwise segmentation and global object detection to achieve better results for facade parsing. Our pipeline can be applied to façade images after rectification and street-level façade images with complex scenes. The result of the ablation study demonstrates that the design of our pipeline is effective. We test our pipeline on the classic ECP2011 data set and our new large street-level data set. Our pipeline achieves state-of-the-art results for both the data sets: an accuracy of 98.2% and the mean average precision (mAP) of 98.8% on the ECP2011 data set as well as the mAP of 81.1% for façade parts parsing on our street-level data set. [ABSTRACT FROM AUTHOR]

Determining the location of a building's entrance is crucial to location-based services, such as wayfinding for pedestrians. Unfortunately, entrance information is often missing from current mainstream map providers such as Google Maps. Frequently, automatic approaches for detecting building entrances are based on street-level images that are not widely available. To address this issue, we propose a more general approach for inferring the main entrances of public buildings based on the association between spatial elements extracted from OpenStreetMap. In particular, we adopt three binary classification approaches, weighted random forest, balanced random forest, and smooth-boost to model the association relationship. There are two types of features considered in the classification: intrinsic features derived from building footprints and extrinsic features derived from spatial contexts, such as roads, green spaces, bicycle parking areas, and neighboring buildings. We conducted extensive experiments on 320 public buildings with an average perimeter of 350 m. The experimental results showed that the locations of building entrances estimated by the weighted random forest and balanced random forest models have a mean linear distance error of 21 m and a mean path distance error of 22 m, ruling out 90% of the incorrect locations of the main entrance of buildings. [ABSTRACT FROM AUTHOR]

Analysis of urban spatial structures is an effective way to explain and solve increasingly serious urban problems. However, many of the existing methods are limited because of data quality and availability, and usually yield inaccurate results due to the unclear description of urban social functions. In this paper, we present an investigation on urban social function based spatial structure analysis using building footprint data. An improved turning function (TF) algorithm and a self-organizing clustering method are presented to generate the variable area units (VAUs) of high-homogeneity from building footprints as the basic research units. Based on the generated VAUs, five spatial metrics are then developed for measuring the morphological characteristics and the spatial distribution patterns of buildings in an urban block. Within these spatial metrics, three models are formulated for calculating the social function likelihoods of each urban block to describe mixed social functions in an urban block, quantitatively. Consequently, the urban structures can be clearly observed by an analysis of the spatial distribution patterns, the development trends, and the hierarchy of different social functions. The results of a case study conducted for Munich validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Building façades can feature different patterns depending on the architectural style, functionality, and size of the buildings; therefore, reconstructing these façades can be complicated. In particular, when semantic façades are reconstructed from point cloud data, uneven point density and noise make it difficult to accurately determine the façade structure. When investigating façade layouts, Gestalt principles can be applied to cluster visually similar floors and façade elements, allowing for a more intuitive interpretation of façade structures. We propose a novel model for describing façade structures, namely the layout graph model, which involves a compound graph with two structure levels. In the proposed model, similar façade elements such as windows are first grouped into clusters. A down-layout graph is then formed using this cluster as a node and by combining intra- and inter-cluster spacings as the edges. Second, a top-layout graph is formed by clustering similar floors. By extracting relevant parameters from this model, we transform semantic façade reconstruction to an optimization strategy using simulated annealing coupled with Gibbs sampling. Multiple façade point cloud data with different features were selected from three datasets to verify the effectiveness of this method. The experimental results show that the proposed method achieves an average accuracy of 86.35%. Owing to its flexibility, the proposed layout graph model can deal with different types of façades and qualities of point cloud data, enabling a more robust and accurate reconstruction of façade models. [ABSTRACT FROM AUTHOR]

Semantically rich maps are the foundation of indoor location‐based services. Many map providers such as OpenStreetMap and automatic mapping solutions focus on the representation and detection of geometric information (e.g., shape of room) and a few semantics (e.g., stairs and furniture) but neglect room usage. To mitigate the issue, this work proposes a general room tagging method for public buildings, which can benefit both existing map providers and automatic mapping solutions by inferring the missing room usage based on indoor geometric maps. Two kinds of statistical learning‐based room tagging methods are adopted: traditional machine learning (e.g., random forests) and deep learning, specifically relational graph convolutional networks (R‐GCNs), based on the geometric properties (e.g., area), topological relationships (e.g., adjacency and inclusion), and spatial distribution characteristics of rooms. In the machine learning‐based approach, a bidirectional beam search strategy is proposed to deal with the issue that the tag of a room depends on the tag of its neighbors in an undirected room sequence. In the R‐GCN‐based approach, useful properties of neighboring nodes (rooms) in the graph are automatically gathered to classify the nodes. Research buildings are taken as examples to evaluate the proposed approaches based on 130 floor plans with 3,330 rooms by using fivefold cross‐validation. The experiments conducted show that the random forest‐based approach achieves a higher tagging accuracy (0.85) than R‐GCN (0.79). [ABSTRACT FROM AUTHOR]

In landmark-based way-finding, determining the most salient landmark from several candidates at decision points is challenging. To overcome this problem, current approaches usually rely on a linear model to measure the salience of landmarks. However, linear models are not always able to establish an accurate quantitative relationship between the attributes of a landmark and its perceived salience. Furthermore, the numbers of evaluated scenes and of volunteers participating in the testing of these models are often limited. With the aim of overcoming these gaps, we propose learning a non-linear salience model by means of genetic programming. We compared our proposed approach with conventional algorithms by using photographs of two hundred test scenes collected from two shopping malls. Two hundred volunteers who were not in these environments were asked to answer questionnaires about the collected photographs. The results from this experiment showed that in 76% of the cases, the most salient landmark (according to the volunteers' perception) was correctly predicted by our proposed approach. This accuracy rate is considerably higher than the ones achieved by conventional linear models. [ABSTRACT FROM AUTHOR]

More than 10 years have passed since the coining of the term volunteered geographic information (VGI) in 2007. This article presents the results of a review of the literature concerning VGI. A total of 346 articles published in 24 international refereed journals in GIScience between 2007 and 2017 have been reviewed. The review has uncovered varying levels of popularity of VGI research over space and time, and varying interests in various sources of VGI (e.g. OpenStreetMap) and VGI-related terms (e.g. user-generated content) that point to the multi-perspective nature of VGI. Content-wise, using latent Dirichlet allocation (LDA), this study has extracted 50 specific research topics pertinent to VGI. The 50 topics have been subsequently clustered into 13 intermediate topics and three overarching themes to allow a hierarchical topic review. The overarching VGI research themes include (1) VGI contributions and contributors, (2) main fields applying VGI, and (3) conceptions and envisions. The review of the articles under the three themes has revealed the progress and the points that demand attention regarding the individual topics. This article also discusses the areas that the existing research has not yet adequately explored and proposes an agenda for potential future research endeavors. [ABSTRACT FROM AUTHOR]

Semantic information in 3D building models is of vital importance for various applications in terms of smart cities. To infer the semantic information and localize the components on building facades, this article proposes a novel approach to model facades with semantics by constructing hierarchical topological graphs. This method utilizes the topological characteristics of building facades. In the first‐layer layout graph, the algorithm takes the nearest cluster as the vertex and the distance between components as the edge. Thus, a topology graph is generated for the facade. The proposed algorithm is divided into three steps. First, the topology graph is obtained by calculating the spacing between the components. It is reasonable to calculate the topological graph by encoding the topological edges. If this calculation is not effective, the topology is justified by adjusting the spacing between components. Finally, the vertices in the graph are used to repair the occluded parts of the facade. In the second‐layer graph, a grid is constructed according to the first‐layer graph. Then, the attributes of the nodes are used to reconstruct the facade. The experimental results show that this method has a high accuracy of 90% and that the average time consumption is 6 s. [ABSTRACT FROM AUTHOR]

High-Accuracy and high-efficiency 3-D sensing and associated data processing techniques are urgently needed for today’s roadway inventory, infrastructure health monitoring, autonomous driving, connected vehicles, urban modeling, and smart cities. 3D geospatial data acquired by digital photogrammetry or laser scanning or LiDAR systems have become one of the most critical data sources to support the above-mentioned applications. While progress has been made to applying 3D sensory data to those applications related to intelligent transportation systems (ITS), such as road network extraction, platform localization, obstacle avoidance, high-definition map generation, and transportation infrastructure inventory, many essential questions remain regarding the processing and understanding such massive 3D datasets in ITS-related applications. The authors have selected four articles for review in this Special issue. A summary of these articles is outlined below. [ABSTRACT FROM AUTHOR]

Satellite images are widely applied in humanitarian mapping that labels buildings, roads, and so on for humanitarian aid and economic development. However, the labeling now is mostly done by volunteers. In this paper, we utilize deep learning to solve humanitarian mapping tasks of a mobile software named MapSwipe. The current deep learning techniques, e.g., convolutional neural network (CNN), can recognize ground objects from satellite images but rely on numerous labels for training for each specific task. We solve this problem by fusing multiple freely accessible crowdsourced geographic data and propose an active learning-based CNN training framework named MC-CNN to deal with the quality issues of the labels extracted from these data, including incompleteness (e.g., some kinds of object are not labeled) and heterogeneity (e.g., different spatial granularities). The method is evaluated with building mapping in South Malawi and road mapping in Guinea with level-18 satellite images provided by Bing Map and volunteered geographic information from OpenStreetMap, MapSwipe, and OsmAnd. The results based on multiple metrics, including Precision, Recall, F1 Score, and area under the receiver operating characteristic curve, show that MC-CNN can fuse the crowdsourced labels for higher prediction performance and be successfully applied in MapSwipe for humanitarian mapping with 85% labor saved and an overall accuracy of 0.86 achieved. [ABSTRACT FROM AUTHOR]

Currently, very few roof shape information for complex buildings is available on OSM. Moreover, additional data requirements (e.g. 3D point clouds) limit the applicability of many roof reconstruction approaches. To mitigate this issue, we propose an approach to roof shape recommendations for complex buildings by exploring the inherited characteristics of building footprints: the disclosure of rectangles combinations in a partition of footprints and the symmetrical features of footprints. First, it decomposes a complex footprint into rectangles by using an advanced minimal non-overlapping cover algorithm. Second, a graph-based symmetry detection algorithm is proposed to identify all the symmetrical sub-clusters in partitions. Then, a set of selection rules are defined to rank partitions, and the best ones are chosen for roof shape recommendation. Finally, a set of combination rules and a symmetry rule are defined. It enables to evaluate the probability of a footprint being a certain combination of roof shapes. Experimental results show the growth of the probability of correctly recommending roof shapes for single rectangles and buildings from a prior probability of 17-45% and from a prior probability of 0.29-14.3%, removing 60% and 93% of the incorrect roof shape options, respectively. [ABSTRACT FROM AUTHOR]

Modeling the geographic distribution of tourists at a tourist destination is crucial when it comes to enhancing the destination’s resilience to disasters and crises, as it enables the efficient allocation of limited resources to precise geographic locations. Seldom have existing studies explored the geographic distribution of tourists through understanding the mechanisms behind it. This article proposes to couple maximum entropy modeling with geotagged social media data to determine the geographic distribution of tourists in order to facilitate disaster and crisis management at tourist destinations. As one of the most popular tourist destinations in the United States, San Diego was chosen as the study area to demonstrate the proposed approach. We modeled the tourist geographic distribution in the study area by quantifying the relationship between the distribution and five environmental factors, including land use, land parcel, elevation, distance to the nearest major road and distance to the nearest transit stop. The geographic distribution’s dependency on and sensitivity to the environmental factors were uncovered. The model was subsequently applied to estimate the potential impacts of one simulated tsunami disaster and one simulated traffic breakdown due to crisis events such as a political protest or a fire hazard. As such, the effectiveness of the approach has been demonstrated with specific disaster and crisis scenarios. [ABSTRACT FROM AUTHOR]

Location-based social networks (LBSN) have provided new possibilities for researchers to gain knowledge about human spatiotemporal behavior, and to make predictions about how people might behave through space and time in the future. An important requirement of successfully utilizing LBSN in these regards is a thorough understanding of the respective datasets, including their inherent potential as well as their limitations. Specifically, when it comes to predictions, we must know what we can actually expect from the data, and how we could maximize their usefulness. Yet, this knowledge is still largely lacking from the literature. Hence, this work explores one particular aspect which is the theoretical predictability of LBSN datasets. The uncovered predictability is represented with an interval. The lower bound of the interval corresponds to the amount of regular behaviors that can easily be anticipated, and represents the correct predication rate that any algorithm should be able to achieve. The upper bound corresponds to the amount of information that is contained in the dataset, and represents the maximum correct prediction rate that cannot be exceeded by any algorithms. Three Foursquare datasets from three American cities are studied as an example. It is found that, within our investigated datasets, the lower bound of predictability of the human spatiotemporal behavior is 27%, and the upper bound is 92%. Hence, the inherent potentials of the dataset for predicting human spatiotemporal behavior are clarified, and the revealed interval allows a realistic assessment of the quality of predictions and thus of associated algorithms. Additionally, in order to provide further insight into the practical use of the dataset, the relationship between the predictability and the check-in frequencies are investigated from three different perspectives. It was found that the individual perspective provides no significant correlations between the predictability and the check-in frequency. In contrast, the same two quantities are found to be negatively correlated from temporal and spatial perspectives. Our study further indicates that the heavily frequented contexts and some extraordinary geographic features such as airports could be good starting points for effective improvements of prediction algorithms. In general, this research provides novel knowledge regarding the nature of the LBSN dataset and practical insights for a more reasonable utilization of the dataset. [ABSTRACT FROM AUTHOR]

Repetitive structures of a building share features in terms of geometries and appearance and, therefore, the 3D information for these structures can be transferred from one specification to another for the purpose of 3D modeling and reconstruction once they are identified as repetitive structures. In this paper, a novel approach is proposed for the detection of the repetitive structures specified by the polygons of a building’s footprints. Instead of directly operating on the polygon in 2D space, the polygon is converted into a bend angle function representation in 1D space, whereby an extrusion is represented as a closed polygon intersected by thex-axis and located above it, while an intrusion is represented as a closed polygon below thex-axis. In this way, a polygon of a footprint is decomposed into a number of extrusions and intrusions which can in turn be processed. The task of detecting any repetitive structures specified in a building’s footprints then becomes the task of clustering the intersected polygons in the bend angle function space. The extrusions/intrusions which can be placed in the same clusters can be regarded as repetitive structures. Experiments show that this proposed approach can detect repetitive structures with different sizes, orientations and complexities. [ABSTRACT FROM PUBLISHER]

Purpose: The goal of this study was to develop a plasmid-based lux bio-reporter for use to obtain in vivo images of Brucella suis vaccine strain 2 (B.suis S2) infection with high resolution and good definition.Procedures: The pBBR-lux (pBBR1MCS-2-lxCDABE) plasmid that carries the luxCDABE operon was introduced into B. suis S2 by electroporation yielding B. suis S2-lux. The spatial and temporal transit of B. suis S2 in mice and guinea pigs was monitored by bioluminescence imaging.Results: The plasmid pBBR-lux is stable in vivo and does not appear to impact the virulence or growth of bacteria. This sensitive luciferase reporter could represent B. suis S2 survival in real time. B. suis S2 mainly colonized the lungs, liver, spleen, and uterus in mice and guinea pigs as demonstrated by bioluminescence imaging.Conclusion: The plasmid-based lux bioreporter strategy can be used to obtain high resolution in vivo images of B. suis S2 infection in mice and guinea pigs. [ABSTRACT FROM AUTHOR]

Matching road networks is an essential step for data enrichment and data quality assessment, among other processes. Conventionally, road networks from two datasets are matched using a line-based approach that checks for the similarity of properties of line segments. In this article, a polygon-based approach is proposed to match the OpenStreetMap road network with authority data. The algorithm first extracts urban blocks that are central elements of urban planning and are represented by polygons surrounded by their surrounding streets, and it then assigns road lines to edges of urban blocks by checking their topologies. In the matching process, polygons of urban blocks are matched in the first step by checking for overlapping areas. In the second step, edges of a matched urban block pair are further matched with each other. Road lines that are assigned to the same matched pair of urban block edges are then matched with each other. The computational cost is substantially reduced because the proposed approach matches polygons instead of road lines, and thus, the process of matching is accelerated. Experiments on Heidelberg and Shanghai datasets show that the proposed approach achieves good and robust matching results, with a precision higher than 96% and a F1-score better than 90%. [ABSTRACT FROM PUBLISHER]

This study proposes a novel approach for extracting multilane roads from urban road networks in OpenStreetMap (OSM) data sets as functional high-level roads, thereby allowing comparative analyses to determine the differences between this functional hierarchy and other hierarchies. OSM road networks have high levels of detail and complex structures, but they also have large numbers of duplicated lines for the same road features, which leads to difficulties and low efficiency when extracting multilane roads using conventional methods based on the analysis and operations of line segments. To overcome these deficiencies, a polygon-based method is proposed that is based on shape analysis and Gestalt theory, which treats polygons surrounded by roads as operating elements. First, shape descriptors are calculated for each polygon in networks and are used for classification. Second, candidate multilane polygons are classified as seeds based on all the polygons used as shape descriptors by a support vector machine. Finally, based on the seed polygons, a region-growing method is proposed that connects and fills the multilane features according to Gestalt theory. An experiment using OSM data from different urban networks verified the validity of the proposed method. The method achieved good and effective extraction performance, regardless of the complexity and duplication of data sets. Thus, a comparative analysis with high-level roads extracted based on road type attributes and structural analysis was performed to demonstrate the differences between the constructed road levels and other hierarchies. [ABSTRACT FROM PUBLISHER]

In this paper, a novel REY zeolite, modified with rare earth through short process of preparation technology and rare earth location technology was introduced. Compared with routine REY zeolite, micro-activity test (MAT) conversion of the novel REY increased by 18 % at least. The catalytic cracking performances of the catalysts were tested on a fixed-fluid-bed bench unit. The results showed that when compared with CAT-old, the conversion of CAT-new catalyst increased by 1.60 % and heavy oil yield decreased by 0.23 %; the total liquid and light oil yield increased by 1.01 and 0.55 %, respectively. Motor octane number and research octane number of gasoline produced by CAT-new sample have increased 0.1 and 1.0 unit, respectively. Therefore, it has good application prospect and remarkable economic benefit. [ABSTRACT FROM AUTHOR]

This letter is dedicated to a generic approach for the automated detection and classification of man-made objects in urban corridors from point clouds acquired by vehicle-borne mobile laser scanning (MLS). The approach is designed based on a priori knowledge in urban areas: 1) man-made objects feature geometric regularity such as vertical planar structures (e.g., building facades), whereas vegetation reveals huge diversity in shape and point distribution and 2) different types of urban man-made objects can be characterized by the point extension and the height above the ground level. Therefore, MLS-based point clouds are first divided into three layers with respect to the vertical height. In each layer, seed points of man-made objects are indicated by a line filter in the footprints of off-ground objects, which is generated by binarizing the spatial accumulation map of the point clouds. These seed points are further classified by examining in which layers the seed points of objects are found. Finally, points belonging to respective objects can be retrieved based on the classified seed points. The experiments show that various man-made objects on both sides of the street can be well detected, with a detection rate of up to 83%. For the classification of detected urban objects, overall accuracy of 92.37% can be achieved. [ABSTRACT FROM PUBLISHER]

In the past two years, several applications of generating three-dimensional (3D) buildings from OpenStreetMap (OSM) have been made available, for instance, OSM-3D, OSM2World, OSM Building, etc. In these projects, 3D buildings are reconstructed using the buildings’ footprints and information about their attributes, which are documented as tags in OSM. Therefore, the quality of 3D buildings relies strongly on the quality of the building footprints data in OSM. This article is dedicated to a quality assessment of building footprints data in OSM for the German city of Munich, which is one of the most developed cities in OSM. The data are evaluated in terms of completeness, semantic accuracy, position accuracy, and shape accuracy by using building footprints in ATKIS (German Authority Topographic–Cartographic Information System) as reference data. The process contains three steps: finding correspondence between OSM and ATKIS data, calculating parameters of the four quality criteria, and statistical analysis. The results show that OSM footprint data in Munich have a high completeness and semantic accuracy. There is an offset of about four meters on average in terms of position accuracy. With respect to shape, OSM building footprints have a high similarity to those in ATKIS data. However, some architectural details are missing; hence, the OSM footprints can be regarded as a simplified version of those in ATKIS data. [ABSTRACT FROM AUTHOR]

CityGML (City Geography Markup Language), the OGC (Open Open Geospatial Consortium) standard on three-dimensional (3D) city modeling, is widely used in an increasing number of applications, because it models a city with rich geometrical and semantic information. The underlying building model differentiates four consecutive levels of detail (LoDs). Nowadays, most city buildings are reconstructed in LoD3, while few landmarks in LoD4. For visualization or other purposes, buildings in LoD2 or LoD1 need to be derived from LoD3 models. But CityGML does not indicate methods for the automatic derivation of the different LoDs. This article presents an approach for deriving LoD2 buildings from LoD3 models which are essentially the exterior shells of buildings without opening objects. This approach treats different semantic components of a building separately with the aim to preserve the characteristics of ground plan, roof, and wall structures as far as possible. The process is composed of three steps: simplifying wall elements, generalizing roof structures, and then reconstructing the 3D building by intersecting the wall and roof polygons. The first step simplifies ground plan with wall elements projected onto the ground. A new algorithm is developed to handle not only simple structures like parallel and rectangle shapes but also complicated structures such as non-parallel, non-rectangular shapes and long narrow angles. The algorithm for generalizing roof structure is based on the same principles; however, the calculation has to be conducted in 3D space. Moreover, the simplified polygons of roof structure are further merged and typified depending on the spatial relations between two neighboring polygons. In the third step, generalized 3D buildings are reconstructed by increasing walls in height and intersecting with roof structures. The approach has been implemented and tested on a number of 3D buildings. The experiments have verified that the 3D building can be efficiently generalized, while the characteristics of wall and roof structure can be well preserved after the simplification. [ABSTRACT FROM AUTHOR]

The OGC standard for 3D city modeling is widely used in an increasing number of applications. It defines five consecutive Levels of Detail (LoD0 to LoD4 with increasing accuracy and structural complexity), in which LoD3 includes all exterior appearances and geometrical details and subsequently requires much storage space. A new LoD is introduced as shell model with the exterior shell of the LoD3 model and the opening objects like windows, doors as well as smaller façade objects are projected onto walls. In this paper, a user survey is presented. The results of this survey show that the shell model can give users almost the same visual impression as the LoD3 model. Furthermroe, algorithms are developed to extract the shell model from LoD3 model. Experiments show that this shell model can reduce up to 90% storage of the original LoD3 model. Therefore, on one hand it can be used as a substitute for a LoD3 model for the visualization on small displays. On the other hand, it can be treated as a sub-level of detail (SLoD3) in CityGML, since it retains almost the same amount of information but requires much less storage space. [ABSTRACT FROM AUTHOR]

In spatial analysis applications, measuring the shape similarity of polygons is crucial for polygonal object retrieval and shape clustering. As a complex cognition process, measuring shape similarity should involve finding the difference between polygons, as objects in observation, in terms of visual perception and the differences of the regions, boundaries, and structures formed by the polygons from a mathematical point of view. In existing approaches, the shape similarity of polygons is calculated by only comparing their mathematical characteristics while not taking human perception into consideration. Aiming to solve this problem, we use the features of context and texture of polygons, since they are basic visual perception elements, to fit the cognition purpose. In this paper, we propose a contour diffusion method for the similarity measurement of polygons. By converting a polygon into a grid representation, the contour feature is represented as a multiscale statistic feature, and the region feature is transformed into condensed grid of context features. Instead of treating shape similarity as a distance between two representations of polygons, the proposed method observes similarity as a correlation between textures extracted by shape features. The experiments show that the accuracy of the proposed method is superior to that of the turning function and Fourier descriptor. [ABSTRACT FROM AUTHOR]

Urban structure is of vital importance to urban planning, transportation, economics and other applications. Since detecting and analyzing urban centers is crucial for understanding urban structure, a large number of studies on urban center extraction have been performed. In this paper, we propose an analysis framework to identify urban centers by using taxi trajectory data. The proposed approach differs from previous methods by employing a novel way to simulate taxi trajectory data with the topographic surface. We extracted pick-up and drop-off spots from taxi trajectory data and employed the localized contour tree method to delineate the boundaries and hierarchies of urban centers. The experiments show that the proposed method can successfully detect urban centers and analyze their temporal patterns in different periods in Shanghai, China. [ABSTRACT FROM AUTHOR]

In China, the traditional taxi industry is conforming to the trend of the times, with taxi drivers working with e-hailing applications. This reform is of great significance, not only for the taxi industry, but also for the transportation industry, cities, and society as a whole. Our goal was to analyze the changes in driving behavior since taxi drivers joined e-hailing platforms. Therefore, this paper mined taxi trajectory data from Shanghai and compared the data of May 2015 with those of May 2017 to represent the before-app stage and the full-use stage, respectively. By extracting two-trip events (i.e., vacant trip and occupied trip) and two-spot events (i.e., pick-up spot and drop-off spot), taxi driving behavior changes were analyzed temporally, spatially, and efficiently. The results reveal that e-hailing applications mine more long-distance rides and new pick-up locations for drivers. Moreover, driver initiative have increased at night since using e-hailing applications. Furthermore, mobile payment facilities save time that would otherwise be taken sorting out change. Although e-hailing apps can help citizens get taxis faster, from the driver's perspective, the apps do not reduce their cruising time. In general, e-hailing software reduces the unoccupied ratio of taxis and improves the operating ratio. Ultimately, new driving behaviors can increase the driver's revenue. This work is meaningful for the formulation of reasonable traffic laws and for urban traffic decision-making. [ABSTRACT FROM AUTHOR]

Similarity measurement is one of the key tasks in spatial data analysis. It has a great impact on applications i.e., position prediction, mining and analysis of social behavior pattern. Existing methods mainly focus on the exact matching of polylines which result in the trajectories. However, for the applications like travel/drive behavior analysis, even for objects passing by the same route the trajectories are not the same due to the accuracy of positioning and the fact that objects may move on different lanes of the road. Further, in most cases of spatial data mining, locations and sometimes sequences of locations on trajectories are most important, while how objects move from location to location (the exact geometries of trajectories) is of less interest. For the abovementioned situations, the existing approaches cannot work anymore. In this paper, we propose a grid aware approach to convert trajectories into sequences of codes, so that shape details of trajectories are neglected while emphasizing locations where trajectories pass through. Experiments with Shanghai Float Car Data (FCD) show that the proposed method can calculate trajectories with high similarity if these pass through the same locations. In addition, the proposed methods are very efficient since the data volume is considerably reduced when trajectories are converted into grid-codes. [ABSTRACT FROM AUTHOR]

With the rapid development of urban traffic, accurate and up-to-date road maps are in crucial demand for daily human life and urban traffic control. Recently, with the emergence of crowdsourced mapping, a surge in academic attention has been paid to generating road networks from spatio-temporal trajectory data. However, most existing methods do not explore changing road patterns contained in multi-temporal trajectory data and it is still difficult to satisfy the precision and efficiency demands of road information extraction. Hence, in this paper, we propose a hybrid method to incrementally extract urban road networks from spatio-temporal trajectory data. First, raw trajectory data were partitioned into K time slices and were used to initialize K-temporal road networks by a mathematical morphology method. Then, the K-temporal road networks were adjusted according to a gravitation force model so as to amend their geometric inconsistencies. Finally, road networks were geometrically delineated using the k-segment fitting algorithm, and the associated road attributes (e.g., road width and driving rule) were inferred. Several case studies were examined to demonstrate that our method can effectively improve the efficiency and precision of road extraction and can make a significant attempt to mine the incremental change patterns in road networks from spatio-temporal trajectory data to help with road map renewal. [ABSTRACT FROM AUTHOR]

As the world's largest crowdsourcing-based street view platform, Mapillary has received considerable attention in both research and practical applications. By February 2019, more than 20,000 users worldwide contributed approximately 6.3 million kilometers of streetscape sequences. In this study, we attempted to get a deep insight into the Mapillary project through an exploratory analysis from the perspective of contributors, including the development of users, the spatiotemporal analysis of active users, the contribution modes (walking, cycling, and driving), and the devices used to contribute. It shows that inequality exists in the distribution of contributed users, similar to that in other volunteered geographic information (VGI) projects. However, the inequality in Mapillary contribution is less than in OpenStreetMap (OSM). Compared to OSM, the other main difference is that the data collection demonstrated obvious seasonal variation because contributions to OSM can be accomplished on a computer, whereas images have to be captured on the streets for Mapillary, and this is considerably affected by seasonal weather. [ABSTRACT FROM AUTHOR]