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Solar cadastres enable calculating and visualizing solar energy potential data on urban surfaces (i.e., roofs, facades, and ground). At high latitudes, knowing the solar potential of vertical surfaces is important due to the solar geometry (i.e., low sun angle). This study provides insights into correlations between neighbourhood morphology and solar energy potential. It moves from the acquisition of the three-dimensional urban surface mode of Trondheim (Norway) to the estimation of the solar potential on urban surfaces with data post-processing and visualization at the neighbourhood scale. The results show that facades' irradiance spread emphasizes high-density neighbourhoods influence on solar potential.

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]

Roof plane segmentation is an essential step in the process of 3D building reconstruction from airborne laser scanning (ALS) point clouds. The existing approaches either rely on human intervention to select the appropriate input parameters for different data‐sets or they are not automatic and efficient. To tackle these issues, an improved multi‐task pointwise network is proposed to simultaneously segment instances (that is, individual roof planes) and semantics (that is, groups of roof planes with similar geometric shapes) in point clouds. PointNet++ is used as a backbone network to extract robust features in the first step. The features from semantics branch are then added to the instance branch to facilitate the learning of instance embeddings. After that, a feature fusion module is added to the semantics branch to acquire more discriminative features from the backbone network. To increase the accuracy of semantic predictions, fused semantic features of the points belonging to the same instance are aggregated together. Finally, a mean‐shift clustering algorithm is employed on instance embeddings to produce the instance predictions. Furthermore, a new roof data‐set (called RoofNTNU) is established by taking ALS point clouds as training data for automatic and more general segmentation. Experiments on the new roof data‐set show that the method achieves promising segmentation results: the mean precision (mPrec) of 96.2% for the instance segmentation task and mean accuracy (mAcc) of 94.4% for the semantic segmentation task. [ABSTRACT FROM AUTHOR]

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]

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]

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]

Since cities have become more complex and some large cities are likely to be polycentric, a better understanding of cities requires a clear topology that reveals how city centers are spatially distributed and interacted with. The identification of a city center that aims to find the accurate location of the city center or delineate the city center with a precise boundary becomes vital. This work attempts to achieve this by using a new type of movement data generated from location-based social networks, whereby three different methods are deployed for clustering and compared regarding identification of city centers and delineation of their boundaries. Experiments show that city centers with precise boundaries can be identified by using the proposed approach with location-based social network data. Furthermore, the results show that the three methods for clustering have different advantages and disadvantages during the process of city center identification, and thus seem to be suitable for cities with different urban structures. [ABSTRACT FROM AUTHOR]