The particle system is widely used in vector field feature visualization due to its dynamics and simulation. However, there are some defects of the vector field visualization method based on the Euler fields, such as unclear feature expression and discontinuous temporal expression, so the method cannot effectively express the characteristics of wind field on the temporal scale. We propose a Lagrangian visualization method based on spatio-temporal interpolation to solve these problems, which realizes the fusion and expression of the particle system and the time-varying wind data based on the WebGL shader. Firstly, the linear interpolation algorithm is used to interpolate to obtain continuous and dense wind field data according to the wind field data at adjacent moments. Then, we introduce the Lagrangian analysis method to study the structure of the wind field and optimize the visualization effect of the particle system based on Runge–Kutta algorithms. Finally, we adopt the nonlinear color mapping method with double standard deviation (2SD) to improve the expression effect of wind field features. The experimental results indicate that the wind visualization achieves a comprehensive visual effect and the rendering frame rate is greater than 45. The methods can render the particles smoothly with stable and outstanding uniformity when expressing continuous spatio-temporal dynamic visualization characteristics of the wind field.
{"title":"A Spatio-Temporal Dynamic Visualization Method of Time-Varying Wind Fields Based on Particle System","authors":"Lele Chu, Bo Ai, Yubo Wen, Qingtong Shi, Huadong Ma, Wenjun Feng","doi":"10.3390/ijgi12040146","DOIUrl":"https://doi.org/10.3390/ijgi12040146","url":null,"abstract":"The particle system is widely used in vector field feature visualization due to its dynamics and simulation. However, there are some defects of the vector field visualization method based on the Euler fields, such as unclear feature expression and discontinuous temporal expression, so the method cannot effectively express the characteristics of wind field on the temporal scale. We propose a Lagrangian visualization method based on spatio-temporal interpolation to solve these problems, which realizes the fusion and expression of the particle system and the time-varying wind data based on the WebGL shader. Firstly, the linear interpolation algorithm is used to interpolate to obtain continuous and dense wind field data according to the wind field data at adjacent moments. Then, we introduce the Lagrangian analysis method to study the structure of the wind field and optimize the visualization effect of the particle system based on Runge–Kutta algorithms. Finally, we adopt the nonlinear color mapping method with double standard deviation (2SD) to improve the expression effect of wind field features. The experimental results indicate that the wind visualization achieves a comprehensive visual effect and the rendering frame rate is greater than 45. The methods can render the particles smoothly with stable and outstanding uniformity when expressing continuous spatio-temporal dynamic visualization characteristics of the wind field.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":"33 1","pages":"146"},"PeriodicalIF":0.0,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91340502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
H. Shang, Yan-Gen Shen, Shuangbo Li, An-Bo Li, Tao Zhang
With the three-dimensional (3D) geological information system development, 3D geological cross-sections (GCs) have become the primary data for geological work and scientific research. Throughout past geological surveys or research works, a lot of two-dimensional (2D) geological cross-section maps have been accumulated, which struggle to meet the scientific research and application needs of 3D visual expression, 3D geological analysis, and many other aspects. Therefore, this paper proposes an automatic generation method for 3D GCs by increasing the dimensions based on a digital elevation model (DEM) and 2D geological cross-section maps. By matching corresponding nodes, generating topographic feature lines, constructing an affine transformation matrix, and inferring the elevation value of each geometric node on the GC, the 3D transformation of the 2D GCs is realized. In this study, fourteen 2D GCs within Nanjing City, Jiangsu Province, are transformed into 3D GCs using the proposed method. The transformed results and quantitative error show that: (1) the proposed method applies to both straight and bent GCs; (2) each transformed GC can fit seamlessly with the ground and maintain minimal geometric deformation, and the geometric shape is consistent with the original GC in non-mountains area. This paper corroborated the proposed method’s effectiveness by comparing it with the other two 3D transformation strategies. In addition, the transformed GCs can be subjected to 3D geological modeling and digital Earth presentation, achieving positive effects in both 3D application and representation.
{"title":"An Automated Mapping Method of 3D Geological Cross-Sections Using 2D Geological Cross-Sections and a DEM","authors":"H. Shang, Yan-Gen Shen, Shuangbo Li, An-Bo Li, Tao Zhang","doi":"10.3390/ijgi12040147","DOIUrl":"https://doi.org/10.3390/ijgi12040147","url":null,"abstract":"With the three-dimensional (3D) geological information system development, 3D geological cross-sections (GCs) have become the primary data for geological work and scientific research. Throughout past geological surveys or research works, a lot of two-dimensional (2D) geological cross-section maps have been accumulated, which struggle to meet the scientific research and application needs of 3D visual expression, 3D geological analysis, and many other aspects. Therefore, this paper proposes an automatic generation method for 3D GCs by increasing the dimensions based on a digital elevation model (DEM) and 2D geological cross-section maps. By matching corresponding nodes, generating topographic feature lines, constructing an affine transformation matrix, and inferring the elevation value of each geometric node on the GC, the 3D transformation of the 2D GCs is realized. In this study, fourteen 2D GCs within Nanjing City, Jiangsu Province, are transformed into 3D GCs using the proposed method. The transformed results and quantitative error show that: (1) the proposed method applies to both straight and bent GCs; (2) each transformed GC can fit seamlessly with the ground and maintain minimal geometric deformation, and the geometric shape is consistent with the original GC in non-mountains area. This paper corroborated the proposed method’s effectiveness by comparing it with the other two 3D transformation strategies. In addition, the transformed GCs can be subjected to 3D geological modeling and digital Earth presentation, achieving positive effects in both 3D application and representation.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":"12 1","pages":"147"},"PeriodicalIF":0.0,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81914917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The rapid development of big data technology and mobile intelligent devices has led to the development of location-based social networks (LBSNs). To understand users’ behavioral patterns and improve the accuracy of location-based services, point-of-interest (POI) recommendation has become an important task. In contrast to the general task of product recommendation, POI recommendation faces the problems of the sparsity and weak semantics of user check-in data. To address these issues, an increasing number of studies have improved the accuracy of POI recommendations by introducing contextual information such as geographical, temporal, textual, and social relations. However, the rich context also brings great challenges to POI recommendation, such as the low utilization rate of context information, difficulty in balancing the richness of contextual information, and the complexity of the recommendation matrix. Considering that similar users have more interest preferences in common than users generally have, the check-in information of similar users has greater reference meaning. Thus, we propose a personalized POI recommendation method named CULT-TF, which incorporates similar users’ contextual information into the tensor factorization model. First, we present a user activity model and a user similarity model, which integrate contextual information to calculate the user activity and similarity between users. According to user activity, the most representative active users are selected as user clustering centers, and then users are clustered based on user similarity into several similar user clusters (C). Next, we construct a third-order tensor (user-location-time matrix) for each user cluster by using user activity, POI popularity, and time slot popularity as the eigenvalues in the user (U), location (L), and time (T) dimensions, and the eigenvalue of each dimension is modeled by integrating contextual information of users’ check-in behavior at the user, location, and time levels. Similar user clustering reduces the number of users in tensor modeling, reducing the U dimension. To further reduce the complexity of the recommendation matrix, the reduction of the L dimension is achieved through ROI (region of interest) clustering, and the reduction of the T dimension is achieved through time slot encoding. Then, we use tensor factorization (TF) to obtain the recommendation results. Our method decreases the complexity of the tensor matrix and integrates rich contextual information on users’ check-in behavior. Finally, we conducted a comprehensive performance evaluation of CULT-TF using real-world LBSN datasets from Brightkite. The experimental results show that our proposed method performs much better than other recommendation methods in terms of precision and recall.
大数据技术和移动智能设备的快速发展带动了基于位置的社交网络的发展。为了了解用户的行为模式，提高定位服务的准确性，兴趣点推荐成为一项重要的任务。与一般的产品推荐任务相比，POI推荐面临用户签入数据的稀疏性和弱语义问题。为了解决这些问题，越来越多的研究通过引入地理、时间、文本和社会关系等上下文信息来提高POI建议的准确性。然而，丰富的上下文也给POI推荐带来了巨大的挑战，如上下文信息的利用率低、上下文信息的丰富度难以平衡、推荐矩阵的复杂性等。考虑到相似用户比一般用户有更多共同的兴趣偏好，相似用户的签到信息具有更大的参考意义。因此，我们提出了一种个性化的POI推荐方法CULT-TF，该方法将相似用户的上下文信息纳入张量分解模型。首先，我们提出了用户活动模型和用户相似度模型，结合上下文信息计算用户活动和用户之间的相似度。根据用户活跃度选择最具代表性的活跃用户作为用户聚类中心，然后根据用户相似度将用户聚类为几个相似的用户聚类(C)。接下来，我们使用用户活跃度、POI流行度和时点流行度作为用户(U)、位置(L)和时间(T)维度的特征值，为每个用户聚类构建一个三阶张量(用户-位置-时间矩阵)。通过整合用户在用户、地点和时间层面的签到行为的上下文信息，对每个维度的特征值进行建模。相似用户聚类减少了张量建模中的用户数量，降低了U维。为了进一步降低推荐矩阵的复杂度，通过ROI (region of interest)聚类实现L维的降维，通过时隙编码实现T维的降维。然后，我们使用张量分解(TF)来获得推荐结果。该方法降低了张量矩阵的复杂度，并集成了用户签入行为的丰富上下文信息。最后，我们使用来自Brightkite的真实LBSN数据集对CULT-TF进行了全面的性能评估。实验结果表明，该方法在准确率和召回率方面都优于其他推荐方法。
{"title":"Context-Aware Point-of-Interest Recommendation Based on Similar User Clustering and Tensor Factorization","authors":"Yan Zhou, Kaixuan Zhou, Shuaixian Chen","doi":"10.3390/ijgi12040145","DOIUrl":"https://doi.org/10.3390/ijgi12040145","url":null,"abstract":"The rapid development of big data technology and mobile intelligent devices has led to the development of location-based social networks (LBSNs). To understand users’ behavioral patterns and improve the accuracy of location-based services, point-of-interest (POI) recommendation has become an important task. In contrast to the general task of product recommendation, POI recommendation faces the problems of the sparsity and weak semantics of user check-in data. To address these issues, an increasing number of studies have improved the accuracy of POI recommendations by introducing contextual information such as geographical, temporal, textual, and social relations. However, the rich context also brings great challenges to POI recommendation, such as the low utilization rate of context information, difficulty in balancing the richness of contextual information, and the complexity of the recommendation matrix. Considering that similar users have more interest preferences in common than users generally have, the check-in information of similar users has greater reference meaning. Thus, we propose a personalized POI recommendation method named CULT-TF, which incorporates similar users’ contextual information into the tensor factorization model. First, we present a user activity model and a user similarity model, which integrate contextual information to calculate the user activity and similarity between users. According to user activity, the most representative active users are selected as user clustering centers, and then users are clustered based on user similarity into several similar user clusters (C). Next, we construct a third-order tensor (user-location-time matrix) for each user cluster by using user activity, POI popularity, and time slot popularity as the eigenvalues in the user (U), location (L), and time (T) dimensions, and the eigenvalue of each dimension is modeled by integrating contextual information of users’ check-in behavior at the user, location, and time levels. Similar user clustering reduces the number of users in tensor modeling, reducing the U dimension. To further reduce the complexity of the recommendation matrix, the reduction of the L dimension is achieved through ROI (region of interest) clustering, and the reduction of the T dimension is achieved through time slot encoding. Then, we use tensor factorization (TF) to obtain the recommendation results. Our method decreases the complexity of the tensor matrix and integrates rich contextual information on users’ check-in behavior. Finally, we conducted a comprehensive performance evaluation of CULT-TF using real-world LBSN datasets from Brightkite. The experimental results show that our proposed method performs much better than other recommendation methods in terms of precision and recall.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":"41 1","pages":"145"},"PeriodicalIF":0.0,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74621800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhuhua Liao, H. Huang, Yijiang Zhao, Yizhi Liu, Guoqiang Zhang
Urban planning and function layout have important implications for the journeys of a large percentage of commuters, which often make up the majority of daily traffic in many cities. Therefore, the analysis and forecast of traffic flow among urban functional areas are of great significance for detecting urban traffic flow directions and traffic congestion causes, as well as helping commuters plan routes in advance. Existing methods based on ride-hailing trajectories are relatively effective solution schemes, but they often lack in-depth analyses on time and space. In the paper, to explore the rules and trends of traffic flow among functional areas, a new spatiotemporal characteristics analysis and forecast method of traffic flow among functional areas based on urban ride-hailing trajectories is proposed. Firstly, a city is divided into areas based on the actual urban road topology, and all functional areas are generated by using areas of interest (AOI); then, according to the proximity and periodicity of inter-area traffic flow data, the periodic sequence and the adjacent sequence are established, and the topological structure is learned through graph convolutional neural (GCN) networks to extract the spatial correlation of traffic flow among functional areas. Furthermore, we propose an attention-based gated graph convolutional network (AG-GCN) forecast method, which is used to extract the temporal features of traffic flow among functional areas and make predictions. In the experiment, the proposed method is verified by using real urban traffic flow data. The results show that the method can not only mine the traffic flow characteristics among functional areas under different time periods, directions, and distances, but also forecast the spatiotemporal change trend of traffic flow among functional areas in a multi-step manner, and the accuracy of the forecasting results is higher than that of common benchmark methods, reaching 96.82%.
{"title":"Analysis and Forecast of Traffic Flow between Urban Functional Areas Based on Ride-Hailing Trajectories","authors":"Zhuhua Liao, H. Huang, Yijiang Zhao, Yizhi Liu, Guoqiang Zhang","doi":"10.3390/ijgi12040144","DOIUrl":"https://doi.org/10.3390/ijgi12040144","url":null,"abstract":"Urban planning and function layout have important implications for the journeys of a large percentage of commuters, which often make up the majority of daily traffic in many cities. Therefore, the analysis and forecast of traffic flow among urban functional areas are of great significance for detecting urban traffic flow directions and traffic congestion causes, as well as helping commuters plan routes in advance. Existing methods based on ride-hailing trajectories are relatively effective solution schemes, but they often lack in-depth analyses on time and space. In the paper, to explore the rules and trends of traffic flow among functional areas, a new spatiotemporal characteristics analysis and forecast method of traffic flow among functional areas based on urban ride-hailing trajectories is proposed. Firstly, a city is divided into areas based on the actual urban road topology, and all functional areas are generated by using areas of interest (AOI); then, according to the proximity and periodicity of inter-area traffic flow data, the periodic sequence and the adjacent sequence are established, and the topological structure is learned through graph convolutional neural (GCN) networks to extract the spatial correlation of traffic flow among functional areas. Furthermore, we propose an attention-based gated graph convolutional network (AG-GCN) forecast method, which is used to extract the temporal features of traffic flow among functional areas and make predictions. In the experiment, the proposed method is verified by using real urban traffic flow data. The results show that the method can not only mine the traffic flow characteristics among functional areas under different time periods, directions, and distances, but also forecast the spatiotemporal change trend of traffic flow among functional areas in a multi-step manner, and the accuracy of the forecasting results is higher than that of common benchmark methods, reaching 96.82%.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":"41 1","pages":"144"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82340167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tahir Ullah, S. Lautenbach, B. Herfort, M. Reinmuth, D. Schorlemmer
Natural hazards threaten millions of people all over the world. To address this risk, exposure and vulnerability models with high resolution data are essential. However, in many areas of the world, exposure models are rather coarse and are aggregated over large areas. Although OpenStreetMap (OSM) offers great potential to assess risk at a detailed building-by-building level, the completeness of OSM building footprints is still heterogeneous. We present an approach to close this gap by means of crowd-sourcing based on the mobile app MapSwipe, where volunteers swipe through satellite images of a region collecting user feedback on classification tasks. For our application, MapSwipe was extended by a completeness feature that allows to classify a tile as “no building”, “complete” or “incomplete”. To assess the quality of the produced data, the completeness feature was applied to four regions. The MapSwipe-based assessment was compared with an intrinsic approach to quantify completeness and with the prediction of an existing model. Our results show that the crowd-sourced approach yields a reasonable classification performance of the completeness of OSM building footprints. Results showed that the MapSwipe-based assessment produced consistent estimates for the case study regions while the other two approaches showed a higher variability. Our study also revealed that volunteers tend to classify nearly completely mapped tiles as “complete”, especially in areas with a high OSM building density. Another factor that influenced the classification performance was the level of alignment of the OSM layer with the satellite imagery.
{"title":"Assessing Completeness of OpenStreetMap Building Footprints Using MapSwipe","authors":"Tahir Ullah, S. Lautenbach, B. Herfort, M. Reinmuth, D. Schorlemmer","doi":"10.3390/ijgi12040143","DOIUrl":"https://doi.org/10.3390/ijgi12040143","url":null,"abstract":"Natural hazards threaten millions of people all over the world. To address this risk, exposure and vulnerability models with high resolution data are essential. However, in many areas of the world, exposure models are rather coarse and are aggregated over large areas. Although OpenStreetMap (OSM) offers great potential to assess risk at a detailed building-by-building level, the completeness of OSM building footprints is still heterogeneous. We present an approach to close this gap by means of crowd-sourcing based on the mobile app MapSwipe, where volunteers swipe through satellite images of a region collecting user feedback on classification tasks. For our application, MapSwipe was extended by a completeness feature that allows to classify a tile as “no building”, “complete” or “incomplete”. To assess the quality of the produced data, the completeness feature was applied to four regions. The MapSwipe-based assessment was compared with an intrinsic approach to quantify completeness and with the prediction of an existing model. Our results show that the crowd-sourced approach yields a reasonable classification performance of the completeness of OSM building footprints. Results showed that the MapSwipe-based assessment produced consistent estimates for the case study regions while the other two approaches showed a higher variability. Our study also revealed that volunteers tend to classify nearly completely mapped tiles as “complete”, especially in areas with a high OSM building density. Another factor that influenced the classification performance was the level of alignment of the OSM layer with the satellite imagery.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":"15 1","pages":"143"},"PeriodicalIF":0.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88386588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Francesco Lodato, N. Colonna, G. Pennazza, S. Praticò, M. Santonico, L. Vollero, M. Pollino
This study analyzes, through remote sensing techniques and innovative clouding services, the recent land use dynamics in the North-Roman littoral zone, an area where the latest development has witnessed an important reconversion of purely rural areas to new residential and commercial services. The survey area includes five municipalities and encompasses important infrastructure, such as the “Leonardo Da Vinci” Airport and the harbor of Civitavecchia. The proximity to the metropolis, supported by an efficient network of connections, has modified the urban and peri-urban structure of these areas, which were formerly exclusively agricultural. Hereby, urban expansion has been quantified by classifying Landsat satellite images using the cloud computing platform “Google Earth Engine” (GEE). Landsat multispectral images from 1985 up to 2020 were used for the diachronic analysis, with a five-yearly interval. In order to achieve a high accuracy of the final result, work was carried out along the temporal dimension of the images, selecting specific time windows for the creation of datasets, which were adjusted by the information related to the NDVI index variation through time. This implementation showed interesting improvements in the model performance for each year, suggesting the importance of the NDVI standard deviation parameter. The results showed an increase in the overall accuracy, being from 90 to 97%, with improvements in distinguishing urban surfaces from impervious surfaces. The final results highlighted a significant increase in the study area of the “Urban” and “Woodland” classes over the 35-year time span that was considered, being 67.4 km2 and 70.4 km2, respectively. The accurate obtained results have allowed us to quantify and understand the landscape transformations in the area of interest, with particular reference to the dynamics of urban development.
{"title":"Analysis of the Spatiotemporal Urban Expansion of the Rome Coastline through GEE and RF Algorithm, Using Landsat Imagery","authors":"Francesco Lodato, N. Colonna, G. Pennazza, S. Praticò, M. Santonico, L. Vollero, M. Pollino","doi":"10.3390/ijgi12040141","DOIUrl":"https://doi.org/10.3390/ijgi12040141","url":null,"abstract":"This study analyzes, through remote sensing techniques and innovative clouding services, the recent land use dynamics in the North-Roman littoral zone, an area where the latest development has witnessed an important reconversion of purely rural areas to new residential and commercial services. The survey area includes five municipalities and encompasses important infrastructure, such as the “Leonardo Da Vinci” Airport and the harbor of Civitavecchia. The proximity to the metropolis, supported by an efficient network of connections, has modified the urban and peri-urban structure of these areas, which were formerly exclusively agricultural. Hereby, urban expansion has been quantified by classifying Landsat satellite images using the cloud computing platform “Google Earth Engine” (GEE). Landsat multispectral images from 1985 up to 2020 were used for the diachronic analysis, with a five-yearly interval. In order to achieve a high accuracy of the final result, work was carried out along the temporal dimension of the images, selecting specific time windows for the creation of datasets, which were adjusted by the information related to the NDVI index variation through time. This implementation showed interesting improvements in the model performance for each year, suggesting the importance of the NDVI standard deviation parameter. The results showed an increase in the overall accuracy, being from 90 to 97%, with improvements in distinguishing urban surfaces from impervious surfaces. The final results highlighted a significant increase in the study area of the “Urban” and “Woodland” classes over the 35-year time span that was considered, being 67.4 km2 and 70.4 km2, respectively. The accurate obtained results have allowed us to quantify and understand the landscape transformations in the area of interest, with particular reference to the dynamics of urban development.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":"28 1","pages":"141"},"PeriodicalIF":0.0,"publicationDate":"2023-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78979209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Muhammad Usman, M. Ejaz, J. Nichol, M. S. Farid, Sawaid Abbas, M. H. Khan
Farmland trees are a vital part of the local economy as trees are used by farmers for fuelwood as well as food, fodder, medicines, fibre, and building materials. As a result, mapping tree species is important for ecological, socio-economic, and natural resource management. The study evaluates very high-resolution remotely sensed WorldView-2 (WV-2) imagery for tree species classification in the agroforestry landscape of the Kano Close-Settled Zone (KCSZ), Northern Nigeria. Individual tree crowns extracted by geographic object-based image analysis (GEOBIA) were used to remotely identify nine dominant tree species (Faidherbia albida, Anogeissus leiocarpus, Azadirachta indica, Diospyros mespiliformis, Mangifera indica, Parkia biglobosa, Piliostigma reticulatum, Tamarindus indica, and Vitellaria paradoxa) at the object level. For every tree object in the reference datasets, eight original spectral bands of the WV-2 image, their spectral statistics (minimum, maximum, mean, standard deviation, etc.), spatial, textural, and color-space (hue, saturation), and different spectral vegetation indices (VI) were used as predictor variables for the classification of tree species. Nine different machine learning methods were used for object-level tree species classification. These were Extra Gradient Boost (XGB), Gaussian Naïve Bayes (GNB), Gradient Boosting (GB), K-nearest neighbours (KNN), Light Gradient Boosting Machine (LGBM), Logistic Regression (LR), Multi-layered Perceptron (MLP), Random Forest (RF), and Support Vector Machines (SVM). The two top-performing models in terms of highest accuracies for individual tree species classification were found to be SVM (overall accuracy = 82.1% and Cohen’s kappa = 0.79) and MLP (overall accuracy = 81.7% and Cohen’s kappa = 0.79) with the lowest numbers of misclassified trees compared to other machine learning methods.
{"title":"A Comparison of Machine Learning Models for Mapping Tree Species Using WorldView-2 Imagery in the Agroforestry Landscape of West Africa","authors":"Muhammad Usman, M. Ejaz, J. Nichol, M. S. Farid, Sawaid Abbas, M. H. Khan","doi":"10.3390/ijgi12040142","DOIUrl":"https://doi.org/10.3390/ijgi12040142","url":null,"abstract":"Farmland trees are a vital part of the local economy as trees are used by farmers for fuelwood as well as food, fodder, medicines, fibre, and building materials. As a result, mapping tree species is important for ecological, socio-economic, and natural resource management. The study evaluates very high-resolution remotely sensed WorldView-2 (WV-2) imagery for tree species classification in the agroforestry landscape of the Kano Close-Settled Zone (KCSZ), Northern Nigeria. Individual tree crowns extracted by geographic object-based image analysis (GEOBIA) were used to remotely identify nine dominant tree species (Faidherbia albida, Anogeissus leiocarpus, Azadirachta indica, Diospyros mespiliformis, Mangifera indica, Parkia biglobosa, Piliostigma reticulatum, Tamarindus indica, and Vitellaria paradoxa) at the object level. For every tree object in the reference datasets, eight original spectral bands of the WV-2 image, their spectral statistics (minimum, maximum, mean, standard deviation, etc.), spatial, textural, and color-space (hue, saturation), and different spectral vegetation indices (VI) were used as predictor variables for the classification of tree species. Nine different machine learning methods were used for object-level tree species classification. These were Extra Gradient Boost (XGB), Gaussian Naïve Bayes (GNB), Gradient Boosting (GB), K-nearest neighbours (KNN), Light Gradient Boosting Machine (LGBM), Logistic Regression (LR), Multi-layered Perceptron (MLP), Random Forest (RF), and Support Vector Machines (SVM). The two top-performing models in terms of highest accuracies for individual tree species classification were found to be SVM (overall accuracy = 82.1% and Cohen’s kappa = 0.79) and MLP (overall accuracy = 81.7% and Cohen’s kappa = 0.79) with the lowest numbers of misclassified trees compared to other machine learning methods.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":"55 1","pages":"142"},"PeriodicalIF":0.0,"publicationDate":"2023-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82244861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Veit Ulrich, Josephine Brückner, M. Schultz, S. Vardag, C. Ludwig, J. Fürle, M. Zia, S. Lautenbach, A. Zipf
As one of the major greenhouse gas (GHG) emitters that has not seen significant emission reductions in the previous decades, the transportation sector requires special attention from policymakers. Policy decisions, thereby need to be supported by traffic emission assessments. Estimations of traffic emissions often rely on huge amounts of actual traffic data whose availability is limited, hampering the transferability of the estimation approaches in time and space. Here, we propose a high-resolution estimation of traffic emissions, which is based entirely on open data, such as the road network and points of interest derived from OpenStreetMap (OSM). We estimated the annual average daily GHG emissions from individual motor traffic for the OSM road network in Berlin by combining the estimated Annual Average Daily Traffic Volume (AADTV) with respective emission factors. The AADTV was calculated by simulating car trips with the open routing engine Openrouteservice, weighted by activity functions based on statistics of the German Mobility Panel. Our estimated total annual GHG emissions were 7.3 million t CO2 equivalent. The highest emissions were estimated for the motorways and major roads connecting the city center with the outskirts. The application of the approach to Berlin showed that the method could reflect the traffic pattern. As the input data is freely available, the approach can be applied to other study areas within Germany with little additional effort.
{"title":"Private Vehicles Greenhouse Gas Emission Estimation at Street Level for Berlin Based on Open Data","authors":"Veit Ulrich, Josephine Brückner, M. Schultz, S. Vardag, C. Ludwig, J. Fürle, M. Zia, S. Lautenbach, A. Zipf","doi":"10.3390/ijgi12040138","DOIUrl":"https://doi.org/10.3390/ijgi12040138","url":null,"abstract":"As one of the major greenhouse gas (GHG) emitters that has not seen significant emission reductions in the previous decades, the transportation sector requires special attention from policymakers. Policy decisions, thereby need to be supported by traffic emission assessments. Estimations of traffic emissions often rely on huge amounts of actual traffic data whose availability is limited, hampering the transferability of the estimation approaches in time and space. Here, we propose a high-resolution estimation of traffic emissions, which is based entirely on open data, such as the road network and points of interest derived from OpenStreetMap (OSM). We estimated the annual average daily GHG emissions from individual motor traffic for the OSM road network in Berlin by combining the estimated Annual Average Daily Traffic Volume (AADTV) with respective emission factors. The AADTV was calculated by simulating car trips with the open routing engine Openrouteservice, weighted by activity functions based on statistics of the German Mobility Panel. Our estimated total annual GHG emissions were 7.3 million t CO2 equivalent. The highest emissions were estimated for the motorways and major roads connecting the city center with the outskirts. The application of the approach to Berlin showed that the method could reflect the traffic pattern. As the input data is freely available, the approach can be applied to other study areas within Germany with little additional effort.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":"436 1","pages":"138"},"PeriodicalIF":0.0,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77009169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The positive and negative terrains (P–N terrains) of the Loess Plateau of China are important geographical topography elements for measuring the degree of surface erosion and distinguishing the types of landforms. Loess shoulder-lines are an important terrain feature in the Loess Plateau and are often used as a criterion for distinguishing P–N terrains. The extraction of shoulder lines is important for predicting erosion and recognising a gully head. However, existing extraction algorithms for loess shoulder-lines in areas with insignificant slopes need to be improved. This study proposes a regional fusion (RF) method that integrates the slope variation-based method and region-growing algorithm to extract loess shoulder-lines based on a Digital Elevation Model (DEM) at a spatial resolution of 5 m. The RF method introduces different terrain factors into the growth standards of the region-growing algorithm to extract loess-shoulder lines. First, we employed a slope-variation-based method to build the initial set of loess shoulder-lines and used the difference between the smoothed and real DEMs to extract the initial set for the N terrain. Second, the region-growing algorithm with improved growth standards was used to generate a complete area of the candidate region of the loess shoulder-lines and the N terrain, which were fused to generate and integrate contours to eliminate the discontinuity. Finally, loess shoulder-lines were identified by detecting the edge of the integrated contour, with results exhibiting congregate points or spurs, eliminated via a hit-or-miss transform to optimise the final results. Validation of the experimental area of loess ridges and hills in Shaanxi Province showed that the accuracy of the RF method based on the Euclidean distance offset percentage within a 10-m deviation range reached 96.9% compared to the manual digitalisation method. Based on the mean absolute error and standard absolute deviation values, compared with Zhou’s improved snake model and the bidirectional DEM relief-shading methods, the proposed RF method extracted the loess shoulder-lines highly accurately.
{"title":"An Optimised Region-Growing Algorithm for Extraction of the Loess Shoulder-Line from DEMs","authors":"Zihan Liu, Hongming Zhang, Liang Dong, Zhixuan Sun, Shufang Wu, Biao Zhang, Lin-shan Yuan, Zhenfei Wang, Qimeng Jia","doi":"10.3390/ijgi12040140","DOIUrl":"https://doi.org/10.3390/ijgi12040140","url":null,"abstract":"The positive and negative terrains (P–N terrains) of the Loess Plateau of China are important geographical topography elements for measuring the degree of surface erosion and distinguishing the types of landforms. Loess shoulder-lines are an important terrain feature in the Loess Plateau and are often used as a criterion for distinguishing P–N terrains. The extraction of shoulder lines is important for predicting erosion and recognising a gully head. However, existing extraction algorithms for loess shoulder-lines in areas with insignificant slopes need to be improved. This study proposes a regional fusion (RF) method that integrates the slope variation-based method and region-growing algorithm to extract loess shoulder-lines based on a Digital Elevation Model (DEM) at a spatial resolution of 5 m. The RF method introduces different terrain factors into the growth standards of the region-growing algorithm to extract loess-shoulder lines. First, we employed a slope-variation-based method to build the initial set of loess shoulder-lines and used the difference between the smoothed and real DEMs to extract the initial set for the N terrain. Second, the region-growing algorithm with improved growth standards was used to generate a complete area of the candidate region of the loess shoulder-lines and the N terrain, which were fused to generate and integrate contours to eliminate the discontinuity. Finally, loess shoulder-lines were identified by detecting the edge of the integrated contour, with results exhibiting congregate points or spurs, eliminated via a hit-or-miss transform to optimise the final results. Validation of the experimental area of loess ridges and hills in Shaanxi Province showed that the accuracy of the RF method based on the Euclidean distance offset percentage within a 10-m deviation range reached 96.9% compared to the manual digitalisation method. Based on the mean absolute error and standard absolute deviation values, compared with Zhou’s improved snake model and the bidirectional DEM relief-shading methods, the proposed RF method extracted the loess shoulder-lines highly accurately.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":"1 1","pages":"140"},"PeriodicalIF":0.0,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81126463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The current COVID-19 pandemic has caused a significant decline in human mobility during the past three years. This may lead to reconfiguring future tourism flows and resulting transformations in the geographic patterns of economic activities and transportation needs. This study empirically addresses the changes in tourism mobility caused by the pandemic. It focuses on the yet unexplored effects of the destination type on tourism volume change. To investigate this, 1426 metropolitan, urban/resort and dispersed destinations were delimited based on Airbnb offers. Airbnb reviews were used as the proxy for the changes in tourist visits in 2019–2022. Linear mixed-effects models were employed to verify two hypotheses on the differences between the effects of the pandemic on three kinds of tourism destinations. The results confirm the tourism de-metropolisation hypothesis: metropolitan destinations have experienced between −12.4% and −7.5% additional decreases in tourism visits compared to secondary cities and resorts. The second de-concentration hypothesis that urban/resort destinations are more affected than dispersed tourism destinations is not supported. The results also confirm that stricter restrictions and destination dependence on international tourism have negatively affected their visitation. The study sheds light on post-pandemic scenarios on tourism mobility transformations in various geographic locations.
{"title":"Tourism De-Metropolisation but Not De-Concentration: COVID-19 and World Destinations","authors":"C. Adamiak","doi":"10.3390/ijgi12040139","DOIUrl":"https://doi.org/10.3390/ijgi12040139","url":null,"abstract":"The current COVID-19 pandemic has caused a significant decline in human mobility during the past three years. This may lead to reconfiguring future tourism flows and resulting transformations in the geographic patterns of economic activities and transportation needs. This study empirically addresses the changes in tourism mobility caused by the pandemic. It focuses on the yet unexplored effects of the destination type on tourism volume change. To investigate this, 1426 metropolitan, urban/resort and dispersed destinations were delimited based on Airbnb offers. Airbnb reviews were used as the proxy for the changes in tourist visits in 2019–2022. Linear mixed-effects models were employed to verify two hypotheses on the differences between the effects of the pandemic on three kinds of tourism destinations. The results confirm the tourism de-metropolisation hypothesis: metropolitan destinations have experienced between −12.4% and −7.5% additional decreases in tourism visits compared to secondary cities and resorts. The second de-concentration hypothesis that urban/resort destinations are more affected than dispersed tourism destinations is not supported. The results also confirm that stricter restrictions and destination dependence on international tourism have negatively affected their visitation. The study sheds light on post-pandemic scenarios on tourism mobility transformations in various geographic locations.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":"41 1","pages":"139"},"PeriodicalIF":0.0,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86446392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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