ISPRS Int. J. Geo-Inf., Volume 8, Issue 9 (September 2019) – 62 articles

In this paper, we propose a method to visualize large-scale colliding point clouds by highlighting their collision areas, and apply the method to visualization of collision simulation. Our method uses our recent work that achieved precise three-dimensional see-through imaging, i.e., transparent visualization, of large-scale point clouds that were acquired via laser scanning of three-dimensional objects. We apply the proposed collision visualization method to two applications: (1) The revival of the festival float procession of the Gion Festival, Kyoto city, Japan. The city government plans to revive the original procession route, which is narrow and not used at present. For the revival, it is important to know whether the festival floats would collide with houses, billboards, electric wires, or other objects along the original route. (2) Plant simulations based on laser-scanned datasets of existing and new facilities. The advantageous features of our method are the following: (1) A transparent visualization with a correct depth feel that is helpful to robustly determine the collision areas; (2) the ability to visualize high collision risk areas and real collision areas; and (3) the ability to highlight target visualized areas by increasing the corresponding point densities. Full article

This paper analyzes the spatial distribution and socioeconomic determinants of social media utilization in 3109 counties of the United States. A theory of determinants was modified from the spatially aware technology utilization model (SATUM). Socioeconomic factors including demography, economy, education, innovation, and social capital were posited to influence social media utilization dependent variables. Spatial analysis was conducted including exploratory analysis of geographic distribution and confirmatory screening for spatial randomness. The determinants were identified through ordinary least squares (OLS) regression analysis. Findings for the nation indicate that the major determinants are demographic factors, service occupations, ethnicities, and urban location. Furthermore, analysis was conducted for the U.S. metropolitan, micropolitan, and rural subsamples. We found that Twitter users were more heavily concentrated in southern California and had a strong presence in the Mississippi region, while Facebook users were highly concentrated in Colorado, Utah, and adjacent Rocky Mountain States. Social media usage was lowest in the Great Plains, lower Midwest, and South with the exceptions of Florida and major southern cities such as Atlanta. Measurements of the overall extent of spatial agglomeration were very high. The paper concludes by discussing the policy implications of the study at the county as well as national levels. Full article

The aim of this study was to modify an algorithm for mapping service areas, also known as access areas. The algorithm is widely applied in network analyses. Service areas are generated based on features such as road networks and base points representing selected objects or facilities. Spatial barriers in the space between road segments are not taken into account in the process of generating service areas. Such barriers include railway lines and rivers. In this study, a methodology for generating service areas that accounts for spatial barriers was proposed by designing a dedicated tool in the ModelBuilder application in ArcGIS (ESRI) software. The ModelBuilder application has limited functionality, and the developed algorithm had to be modified. The modified algorithm was verified based on spatial data from four cities. The results produced by standard analytical methods were compared with the results generated by the modified algorithm. The study demonstrated that spatial barriers decrease the size of service areas. The modified algorithm generates more reliable results than standard methods. Full article

Drainage network analysis includes several operations that quantify the topological organization of stream networks. Network analysis operations are frequently performed on streams that are derived from digital elevation models (DEMs). While these methods are suited to application with fine-resolution DEM data, this is not the case for coarse DEMs or low-relief landscapes. In these cases, network analysis that is based on mapped vector streams is an alternative. This study presents a novel vector drainage network analysis technique for performing stream ordering, basin tagging, the identification of main stems and tributaries, and the calculation of total upstream channel length and distance to outlet. The algorithm uses a method for automatically identifying outlet nodes and for determining the upstream-downstream connections among links within vector stream networks while using the priority-flood method. The new algorithm was applied to test stream datasets in two Canadian study areas. The tests demonstrated that the new algorithm could efficiently process large hydrographic layers containing a variety of topological errors. The approach handled topological errors in the hydrography data that have challenged previous methods, including disjoint links, conjoined channels, and heterogeneity in the digitized direction of links. The method can provide a suitable alternative to DEM-based approaches to drainage network analysis, particularly in applications where stream burning would otherwise be necessary. Full article

The identification of overpass structures in road networks has great significance for multi-scale modeling of roads, congestion analysis, and vehicle navigation. The traditional vector-based methods identify overpasses by the methodologies coming from computational geometry and graph theory, and they overly rely on the artificially designed features and have poor adaptability to complex scenes. This paper presents a novel method of identifying overpasses based on a target detection model (Faster-RCNN). This method utilizes raster representation of vector data and convolutional neural networks (CNNs) to learn task adaptive features from raster data, then identifies the location of an overpass by a Region Proposal network (RPN). The contribution of this paper is: (1) An overpass labelling geodatabase (OLGDB) for the OpenStreetMap (OSM) road network data of six typical cities in China is established; (2) Three different CNNs (ZF-net, VGG-16, Inception-ResNet V2) are integrated into Faster-RCNN and evaluated by accuracy performance; (3) The optimal combination of learning rate and batchsize is determined by fine-tuning; and (4) Five geometric metrics (perimeter, area, squareness, circularity, and W/L) are synthetized into image bands to enhance the training data, and their contribution to the overpass identification task is determined. The experimental results have shown that the proposed method has good accuracy performance (around 90%), and could be improved with the expansion of OLGDB and switching to more sophisticated target detection models. The deep learning target detection model has great application potential in large-scale road network pattern recognition, it can task-adaptively learn road structure features and easily extend to other road network patterns. Full article

Propagating crowdsourcing services via a wireless network can be an appropriate solution to using the potential of crowds in crisis management processes. The present study aimed to deploy crowdsourcing services properly to spatial urgent requests. Composing such atomic services can conquer sophisticated crisis management. In addition, the conducted propagated services guide people through crisis fields and allow managers to use crowd potential appropriately. The use of such services requires a suitable automated allocation method, along with a proper approach to arranging the sequence of propagating services. The solution uses a mathematical framework in the context of a GIS (Geospatial Information System) in order to construct an allocation approach. Solution elements are set out in a multi-agent environment structure, which simulate disaster field objects. Agents which are dynamically linked to objects in a crisis field, interact with each other in a competitive environment, and the results in forming crowdsourcing services are used to guide crowds in the crisis field via the crowdsourcing services. The present solution was implemented through a proper data schema in a powerful geodatabase, along with various users with specialized interfaces. Finally, a solution and crowdsourcing service was tested in the context of a GIS in the 2019 Aqala flood disaster in Iran and other complement scenarios. The allocating performance and operation of other system elements were acceptable and reduced indicators, such as rescuer fatigue and delay time. Crowdsourcing service was positioned well in the solution and provided good performance among the elements of the Geospatial Information System. Full article

County-level economic statistics estimation using remotely sensed data, such as nighttime light data, has various advantages over traditional methods. However, uncertainties in remotely sensed data, such as the saturation problem of the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) NSL (nighttime stable lights) data, may influence the accuracy of this remote sensing-based method, and thus hinder its use. This study proposes a simple method to address the saturation phenomenon of nighttime light data using the GDP growth rate. Compared with other methods, the NSL data statistics obtained using the new method reflect the development of economics more accurately. We use this method to calibrate the DMSP-OLS NSL data from 1992 to 2013 to obtain the NSL density data for each county and linearly regress them with economic statistics from 2004 to 2013. Regression results show that lighting data is highly correlated with economic data. We then use the light data to further estimate the county-level GDP, and find that the estimated GDP is consistent with the authoritative GDP statistics. Our approach provides a reliable way to capture county-level economic development in different regions. Full article

In recent years, volunteered-geographic-information (VGI) image data have served as a data source for various geographic applications, attracting researchers to assess the quality of these images. However, these applications and quality assessments are generally focused on images associated with geolocation through textual annotations, which is only part of valid images to them. In this paper, we explore the distribution pattern for most relevant VGI images of specific landmarks to extend the current quality analysis, and to provide guidance for improving the data-retrieval process of geographic applications. Distribution is explored in terms of two aspects, namely, semantic distribution and spatial distribution. In this paper, the term semantic distribution is used to describe the matching of building-image tags and content with each other. There are three kinds of images (semantic-relevant and content-relevant, semantic-relevant but content-irrelevant, and semantic-irrelevant but content-relevant). Spatial distribution shows how relevant images are distributed around a landmark. The process of this work can be divided into three parts: data filtering, retrieval of relevant landmark images, and distribution analysis. For semantic distribution, statistical results show that an average of 60% of images tagged with the building’s name actually represents the building, while 69% of images depicting the building are not annotated with the building’s name. There was also an observation that for most landmarks, 97% of relevant building images were located within 300 m around the building in terms of spatial distribution. Full article

Remote sensing image captioning involves remote sensing objects and their spatial relationships. However, it is still difficult to determine the spatial extent of a remote sensing object and the size of a sample patch. If the patch size is too large, it will include too many remote sensing objects and their complex spatial relationships. This will increase the computational burden of the image captioning network and reduce its precision. If the patch size is too small, it often fails to provide enough environmental and contextual information, which makes the remote sensing object difficult to describe. To address this problem, we propose a multi-scale semantic long short-term memory network (MS-LSTM). The remote sensing images are paired into image patches with different spatial scales. First, the large-scale patches have larger sizes. We use a Visual Geometry Group (VGG) network to extract the features from the large-scale patches and input them into the improved MS-LSTM network as the semantic information, which provides a larger receptive field and more contextual semantic information for small-scale image caption so as to play the role of global perspective, thereby enabling the accurate identification of small-scale samples with the same features. Second, a small-scale patch is used to highlight remote sensing objects and simplify their spatial relations. In addition, the multi-receptive field provides perspectives from local to global. The experimental results demonstrated that compared with the original long short-term memory network (LSTM), the MS-LSTM’s Bilingual Evaluation Understudy (BLEU) has been increased by 5.6% to 0.859, thereby reflecting that the MS-LSTM has a more comprehensive receptive field, which provides more abundant semantic information and enhances the remote sensing image captions. Full article

With the development of autonomous driving, lane-level maps have attracted significant attention. Since the lane-level road network is an important part of the lane-level map, the efficient, low-cost, and automatic generation of lane-level road networks has become increasingly important. We propose a new method here that generates lane-level road networks using only position information based on an autonomous vehicle and the existing lane-level road networks from the existing road-level professionally surveyed without lane details. This method uses the parallel relationship between the centerline of a lane and the centerline of the corresponding segment. Since the direct point-by-point computation is huge, we propose a method based on a trajectory-similarity-join pruning strategy (TSJ-PS). This method uses a filter-and-verify search framework. First, it performs quick segmentation based on the minimum distance and then uses the similarity of two trajectories to prune the trajectory similarity join. Next, it calculates the centerline trajectory for lanes using the simulation transformation model by the unpruned trajectory points. Finally, we demonstrate the efficiency of the algorithm and generate a lane-level road network via experiments on a real road. Full article

Recently, social networks have shown huge potential in terms of collaborative web services and the study of peer influence as a result of the massive amount of data, datasets, and interrelations generated. These interrelations cannot guarantee the success of online social networks without ensuring the existence of trust between nodes. Detecting influential nodes improves collaborative filtering (CF) recommendations in which nodes with the highest influential capability are most likely to be the source of recommendations. Although CF-based recommendation systems are the most widely used approach for implementing recommender systems, this approach ignores the mutual trust between users. In this paper, a trust-based algorithm (TBA) is introduced to detect influential spreaders in social networks efficiently. In particular, the proposed TBA estimates the influence that each node has on the other connected nodes as well as on the whole network. Next, a Friend-of-Friend recommendation (FoF-SocialI) algorithm is addressed to detect the influence of social ties in the recommendation process. Finally, experimental results, performed on three large scale location-based social networks, namely, Brightkite, Gowalla, and Weeplaces, to test the efficiency of the proposed algorithm, are presented. The conducted experiments show a remarkable enhancement in predicting and recommending locations in various social networks. Full article

Taxi demand prediction is one of the key factors in making online taxi hailing services more successful and more popular. Accurate taxi demand prediction can bring various advantages including, but not limited to, enhancing user experience, increasing taxi utilization, and optimizing traffic efficiency. However, the task is challenging because of complex spatial and temporal dependencies of taxi demand. In addition, relationships between non-adjacent regions are also critical for accurate taxi demand prediction, whereas they are largely ignored by existing approaches. To this end, we propose a novel graph and time-series learning model for city-wide taxi demand prediction in this paper. It has two main building blocks, the first one utilize a graph network with attention mechanism to effectively learn spatial dependencies of taxi demand in a broader perspective of the entire city, and the output at each time interval is then transferred to the second block. In the graph network, the edge is defined by an Origin–Destination relation to capture non-adjacent impacts. The second one uses a neural network which is adept with processing sequence data to capture the temporal correlations of city-wide taxi demand. Using a large, real-world dataset and three metrics, we conduct an extensive experimental study and find that our model outperforms state-of-the-art baselines by 9.3% in terms of the root-mean-square error. Full article

The geographical location and check-in frequency of social platform users indicate their personal preferences and intentions for space. On the basis of social media data and gender differences, this study analyzes Weibo users’ preferences and the reasons behind these preferences for the waterfronts of the 21 major lakes within Wuhan’s Third Ring Road, in accordance with users’ check-in behaviors. According to the distribution characteristics of the waterfronts’ points of interest, this study explores the preferences of male and female users for waterfronts and reveals, through the check-in behaviors of Weibo users, the gender differences in the preference and willingness of these users to choose urban waterfronts. Results show that men and women check in significantly more frequently on weekends than on weekdays. Women are more likely than men to check in at waterfronts. Significant differences in time and space exist between male and female users’ preferences for different lakes. Full article

Road anomaly detection is essential in road maintenance and management; however, continuously monitoring road anomalies (such as bumps and potholes) with a low-cost and high-efficiency solution remains a challenging research question. In this study, we put forward an enhanced mobile sensing solution to detect road anomalies using mobile sensed data. We first create a smartphone app to detect irregular vehicle vibrations that usually imply road anomalies. Then, the mobile sensed signals are analyzed through continuous wavelet transform to identify road anomalies and estimate their sizes. Next, we innovatively utilize a spatial clustering method to group multiple driving tests’ results into clusters based on their spatial density patterns. Finally, the optimized detection results are obtained by synthesizing each cluster’s member points. Results demonstrate that our proposed solution can accurately detect road surface anomalies (94.44%) with a high positioning accuracy (within 3.29 meters in average) and an acceptable size estimation error (with a mean error of 14 cm). This study suggests that implementing a crowdsensing solution could substantially improve the effectiveness of traditional road monitoring systems. Full article

Ubiquitous trajectory data provides new opportunities for production and update of the road network. A number of methods have been proposed for road network construction and update based on trajectory data. However, existing methods were mainly focused on reconstruction of the existing road network, and the update of newly added roads was not given much attention. Besides, most of existing methods were designed for high sampling rate trajectory data, while the commonly available GPS trajectory data are usually low-quality data with noise, low sampling rates, and uneven spatial distributions. In this paper, we present an automatic method for detection and update of newly added roads based on the common low-quality trajectory data. First, additive changes (i.e., newly added roads) are detected using a point-to-segment matching algorithm. Then, the geometric structures of new roads are constructed based on a newly developed decomposition-combination map generation algorithm. Finally, the detected new roads are refined and combined with the original road network. Seven trajectory data were used to test the proposed method. Experiments show that the proposed method can successfully detect the additive changes and generate a road network which updates efficiently. Full article

To organize trajectory data is a challenging issue for both studies on spatial databases and spatial data mining in the last decade, especially where there is semantic information involved. The high-level semantic features of trajectory data exploit human movement interrelated with geographic context, which is becoming increasingly important in representing and analyzing actual information contained in movements and further processing. This paper argues for a novel semantic trajectory model named TOST. It considers both semantic and geographic information of trajectory data happens along network infrastructure simultaneously. In TOST, a flexible intersection-based semantic representation is designed to express movement typically constrained by urban road networks by combining sets of local semantic details along the time axis. A relational schema based on this model was instantiated against real datasets, which illustrated the effectivity of our proposed model. Full article

The timely and proper rehabilitation of damaged roads is essential for road maintenance, and an effective method to detect road surface distress with high efficiency and low cost is urgently needed. Meanwhile, unmanned aerial vehicles (UAVs), with the advantages of high flexibility, low cost, and easy maneuverability, are a new fascinating choice for road condition monitoring. In this paper, road images from UAV oblique photogrammetry are used to reconstruct road three-dimensional (3D) models, from which road pavement distress is automatically detected and the corresponding dimensions are extracted using the developed algorithm. Compared with a field survey, the detection result presents a high precision with an error of around 1 cm in the height dimension for most cases, demonstrating the potential of the proposed method for future engineering practice. Full article

Building fire is a complex geographic process related to the indoor spatial environment, a smart spatial data model can accurately describe the spatial-temporal information of a building fire scene, which is important for modeling a fire process. With the development of fire dynamics and computer science, many building fire models have been proposed and widely used. However, the spatial representation of these models is relatively weak. In this study, a fire process modeled via the Fire Dynamics Simulator (FDS) and the requirements of a spatial data model are initially analyzed. Then, a new spatial data model named the Combinatorial Spatial Data Model (CSDM) is combined with Geographic Information System (GIS). The key features of the CSDM, which include spatial, semantic, topological, event and state representations of a building fire scene modeled via the CSDM are subsequently presented. In addition, the Unified Modeling Language (UML) class diagram of the CSDM is also presented, and then experiments with a simplified building are conducted as a CSDM implementation case. A method of transferring data from the CSDM to FDS and a building fire analysis approach using the CSDM are subsequently proposed. Full article

Urban roads are the lifeline of urban transportation and satisfy the commuting and travel needs of citizens. Following the acceleration of urbanization and the frequent extreme weather in recent years, urban waterlogging is occurring more than usual in summer and has negative effects on the urban traffic networks. Extracting flooded roads is a critical procedure for improving the resistance ability of roads after urban waterlogging occurs. This paper proposes a flooded road extraction method to extract the flooding degree and the time at which roads become flooded in large urban areas by using global positioning system (GPS) trajectory points with driving status information and the high position accuracy of vector road data with semantic information. This method uses partition statistics to create density grids (grid layer) and uses map matching to construct a time-series of GPS trajectory point density for each road (vector layer). Finally, the fusion of grids and vector layers obtains a more accurate result. The experiment uses a dataset of GPS trajectory points and vector road data in the Wuchang district, which proves that the extraction result has a high similarity with respect to the flooded roads reported in the news. Additionally, extracted flooded roads that were not reported in the news were also found. Compared with the traditional methods for extracting flooded roads and areas, such as rainfall simulation and SAR image-based classification in urban areas, the proposed method discovers hidden flooding information from geospatial big data, uploaded at no cost by urban taxis and remaining stable for a long period of time. Full article

Location-based social networking services have attracted great interest with the growth of smart mobile devices. Recommending locations for users based on their preferences is an important task for location-based social networks (LBSNs). Since human beings are social by nature, group activities are important in individuals’ lives. Due to the different interests and priorities of individuals, it is difficult to find places that are ideal for all members of a group. In this study, a context-aware group-oriented location recommendation system is proposed based on a random walk algorithm. The proposed approach considers three different contexts, namely users’ contexts (i.e., social relationships, personal preferences), location context (i.e., category, popularity, capacity, and spatial proximity), and environmental context (i.e., weather, day of the week). Three graph models of LBSNs are constructed to perform a random walk with restart (RWR) algorithm in which a user-location graph is considered as the basis. In addition, two group recommendation strategies are used. One is an aggregated prediction strategy, and the other is derived from extending the RWR to the group. After performing the RWR algorithm, the group profile and location popularity are used to improve the effectiveness of the recommendation. The performance of the proposed system is examined using the Gowalla dataset related to the city of London from March 2009 to July 2011. The results indicate that the RWR algorithm outperforms popularity-based, collaborative filtering and content-based filtering. In addition, using the group profile and location popularity significantly improves the accuracy of recommendation. On the user-location graph, the number of users with recommendations matching the test data increases by 1.18 times, while the precision of creating relevant recommendations is increased by 3.4 times. Full article

Accurate bathymetric modeling is required for safe maritime navigation in shallow waters as well as for other marine operations. Traditionally, bathymetric modeling is commonly carried out using linear models, such as the Stumpf method. Linear methods are developed to derive bathymetry using the strong linear correlation between the grey values of satellite imagery visible bands and the water depth where the energy of these visible bands, received at the satellite sensor, is inversely proportional to the depth of water. However, without satisfying homogeneity of the seafloor topography, this linear method fails. The current state-of-the-art is represented by artificial neural network (ANN) models, which were developed using a non-linear, static modeling function. However, more accurate modeling can be achieved using a highly non-linear, dynamic modeling function. This paper investigates a highly non-linear wavelet network model for accurate satellite-based bathymetric modeling with dynamic non-linear wavelet activation function that has been proven to be a valuable modeling method for many applications. Freely available Level-1C satellite imagery from the Sentinel-2A satellite was employed to develop and justify the proposed wavelet network model. The top-of-atmosphere spectral reflectance values for the multispectral bands were employed to establish the wavelet network model. It is shown that the root-mean-squared (RMS) error of the developed wavelet network model was about 1.82 m, and the correlation between the wavelet network model depth estimate and “truth” nautical chart depths was about 95%, on average. To further justify the proposed model, a comparison was made among the developed, highly non-linear wavelet network method, the Stumpf log-ratio method, and the ANN method. It is concluded that the developed, highly non-linear wavelet network model is superior to the Stumpf log-ratio method by about 37% and outperforms the ANN model by about 21%, on average, on the basis of the RMS errors. Also, the accuracy of the bathymetry-derived wavelet network model was evaluated on the basis of the International Hydrographic Organization (IHO)’s standards for all survey orders. It is shown that the accuracy of the bathymetry derived from the wavelet network model does not meet the IHO’s standards for all survey orders; however, the wavelet network model can still be employed as an accurate and powerful tool for survey planning when conducting hydrographic surveys for new, shallow water areas. Full article

Road rutting caused by vehicle loading in the wheel path is a major form of asphalt pavement distress. Hydroplaning and loss of skid resistance are directly related to high road rutting severity. Periodical measurements of rut depth are crucial to maintenance and rehabilitation planning. In this study, we explored the feasibility of using point clouds gathered by Mobile LiDAR systems to measure the rut depth. These point clouds that are collected along roads are usually used for other purposes, namely asset inventory or topographic survey. Taking advantage of available clouds to identify rutting severity in critical pavement areas can result in considerable economic and time saving and thus, added value, when compared with specific expensive rut measuring systems. Four different strategies of cloud points aggregation are presented to create the cross-section of points. Such strategies were established to improve the precision of individual sensor measurements. Despite the 5 mm precision of the used system, it was possible to estimate rut depth values that were slightly inferior. The rut depth values obtained from each cross-section strategy were compared with the manual field measured values. The cross-sections based on averaged cloud points sensor profile aggregation was revealed to be the most suitable strategy to measure rut depth. Despite the fact that the study was specifically conducted to measure rut depth, the evaluation results show that the methodology can also be useful for other mobile LiDAR point clouds cross-sections applications. Full article

The authors analyze topography changes related to the construction and operation of the Bełchatów Brown Coal Open Mine and Power Plant, one of Europe’s larger open-pit mines, situated in central Poland. In order to achieve this, a DEM (Digital Elevation Model) is prepared, based on archival materials from the pre-investment period. Source materials include German topographical maps, issued in 1944 by the Supreme High Command of the German Army (Oberkommando des Heeres/Generalstab). The second model of the same area is prepared based on DEM data included in the Topographical Database available by CODGiK (Main Centre of Geodetic and Cartographic Documentation). The preparation of two terrain models from different periods make it possible to evaluate the changes in the morphometry. Both models are compared using ArcGIS (ESRI) tools. The comparative analysis of the models allows for observing topography changes resulting from anthropogenic transformations related to the construction of the Brown Coal Open Mine Bełchatów and Power Plant complex. Full article

The fear of crime is an established research topic, not only in sociology, environmental psychology and criminology, but also in GIScience. Using spatial analysis to analyse patterns, explore hotspots and determine the significance of respective surveys is one reason for the increase in popularity of such research topics for geographers, cartographers and spatial data scientists. This paper presents the results of an intensive online map-based questionnaire with 1551 respondents from the city of Ostrava, Czech Republic. The respondents marked 3792 points associated with the fear of crime over a ten week period. The perception data were compared with recorded crime data acquired from police department records for the years 2015–2018. This paper explores the spatial autocorrelation from perceived hotspots and from recorded crime hotspots. Our findings fit into the literature confirming results about the locations that most frequently attract fear, but there is still room for more investigations regarding the links between recorded crime and the fear of crime. Full article

In this paper, we analyse spatial variation in the Japanese dialectal lexicon by assembling a set of methodologies using theories in variationist linguistics and GIScience, and tools used in historical GIS. Based on historical dialect atlas data, we calculate a linguistic distance matrix across survey localities. The linguistic variation expressed through this distance is contrasted with several measurements, based on spatial distance, utilised to estimate language contact potential across Japan, historically and at present. Further, administrative boundaries are tested for their separation effect. Measuring aggregate associations within linguistic variation can contrast previous notions of dialect area formation by detecting continua. Depending on local geographies in spatial subsets, great circle distance, travel distance and travel times explain a similar proportion of the variance in linguistic distance despite the limitations of the latter two. While they explain the majority, two further measurements estimating contact have lower explanatory power: least cost paths, modelling contact before the industrial revolution, based on DEM and sea navigation, and a linguistic influence index based on settlement hierarchy. Historical domain boundaries and present day prefecture boundaries are found to have a statistically significant effect on dialectal variation. However, the interplay of boundaries and distance is yet to be identified. We claim that a similar methodology can address spatial variation in other digital humanities, given a similar spatial and attribute granularity. Full article

The successful implementation of the African Union’s Agenda 2063 strategic development blueprint is critical for the attainment of economic development, social prosperity, political stability, protection, and regional integration in Africa. Agenda 2063 is a strategic and endogenous development plan that seeks to strategically and competitively reposition the African continent to ensure poverty eradication and equitable people-centric socio-economic and technological transformation. Its impact areas include wealth creation, shared prosperity, sustainable environment, and transformative capacities. Monitoring and evaluation systems play a critical role in collecting, recording, storing, integrating, and evaluating and tracking performance information in the implementation of longer-term strategic plans. The usage of the geographic information system (GIS) as a monitoring and evaluation tool has gained traction in the last few decades due to its ability to support the collection, integration, storage, analysis, output, and distribution of location-based data. The advent of web-based GIS provides a powerful online platform to collect, integrate, discover, use and share geospatial data, information, and services related to sustainable development. In this paper, we aim to describe the implementation, architectural structural design, and the functionality of the pilot Agenda 2063 geoportal. The live prototype internet-based geoportal is intended to facilitate data collection, management, integration, analysis, and visualization of Agenda 2063 development indicators. This geoportal is meant to support the planning, implementation, and monitoring of the Agenda 2063 goals at the continental, regional, and national levels. As our results show, we successfully demonstrated that a web-geoportal is a powerful interactive platform to upload, access, explore, visualize, analyse, and disseminate geospatial data related to the sustainable development of the African continent. Although in the pilot phase, the geoportal demonstrates the primary functionality of geoportals in terms of its capability to discover, analyse, share, and download geospatial datasets. Full article

For an accurate interpretation of high-resolution images, correct training samples are required, whose automatic production is an important step. However, the proper way to use them and the reduction of their defects should also be taken into consideration. To this end, in this study, the application of different combinations of training data in a layered structure provided different scores for each observation. For each observation (segment) in a layer, the scores corresponded to the obtained misclassification cost for all classes. Next, these scores were properly weighted by considering the stability of different layers, the adjacency analysis of each segment in a multi-scale manner and the main properties of the basic classes. Afterwards, by integrating the scores of all classes weighted in all layers, the final scores were produced. Finally, the labels were achieved in the form of collective wisdom, obtained from the weighted scores of all segments. In the present study, the aim was to develop a hybrid intelligent system that can exploit both expert knowledge and machine learning algorithms to improve the accuracy and efficiency of the object-based classification. To evaluate the efficiency of the proposed method, the results of this research were assessed and compared with those of other methods in the semi-urban domain. The experimental results indicated the reliability and efficiency of the proposed method. Full article

This study explores two modeling issues that may cause uncertainty in landslide susceptibility assessments when different sampling strategies are employed. The first issue is that extracted attributes within a landslide inventory polygon can vary if the sample is obtained from different locations with diverse topographic conditions. The second issue is the mixing problem of landslide inventory that the detection of landslide areas from remotely-sensed data generally includes source and run-out features unless the run-out portion can be removed manually with auxiliary data. To this end, different statistical sampling strategies and the run-out influence on random forests (RF)-based landslide susceptibility modeling are explored for Typhoon Morakot in 2009 in southern Taiwan. To address the construction of models with an extremely high false alarm error or missing error, this study integrated cost-sensitive analysis with RF to adjust the decision boundary to achieve improvements. Experimental results indicate that, compared with a logistic regression model, RF with the hybrid sample strategy generally performs better, achieving over 80% and 0.7 for the overall accuracy and kappa coefficient, respectively, and higher accuracies can be obtained when the run-out is treated as an independent class or combined with a non-landslide class. Cost-sensitive analysis significantly improved the prediction accuracy from 5% to 10%. Therefore, run-out should be separated from the landslide source and labeled as an individual class when preparing a landslide inventory. Full article

Woodlands in the traditional rural landscape of Japan are thought to have a role as windbreaks, among various functions. However, in previous studies, the windbreak effect of woodlands in early-modern settlements has not been quantitatively analyzed. To perform a quantitative analysis, computational fluid dynamics was used with a 3D reconstruction of the early-modern rural landscape of Kichijoji village in a suburb of Tokyo. The landscape was reconstructed based on historical records. The analysis showed that the woodland in Kichijoji village effectively reduced the speed of northbound and southbound winds in the fields. The results are consistent with the actual prevailing wind direction in this area. The purpose of this study was to determine a method and model to quantify the windbreak effect of woodlands in early-modern settlements. Full article