Claudio Mercurio, Laura Paola Calderón-Cucunuba, Abel Alexei Argueta-Platero, Grazia Azzara, C. Cappadonia, C. Martinello, E. Rotigliano, C. Conoscenti
In January and February 2001, El Salvador was hit by two strong earthquakes that triggered thousands of landslides, causing 1259 fatalities and extensive damage. The analysis of aerial and SPOT-4 satellite images allowed us to map 6491 coseismic landslides, mainly debris slides and flows that occurred in volcanic epiclastites and pyroclastites. Four different multivariate adaptive regression splines (MARS) models were produced using different predictors and landslide inventories which contain slope failures triggered by an extreme rainfall event in 2009 and those induced by the earthquakes of 2001. In a predictive analysis, three validation scenarios were employed: the first and the second included 25% and 95% of the landslides, respectively, while the third was based on a k-fold spatial cross-validation. The results of our analysis revealed that: (i) the MARS algorithm provides reliable predictions of coseismic landslides; (ii) a better ability to predict coseismic slope failures was observed when including susceptibility to rainfall-triggered landslides as an independent variable; (iii) the best accuracy is achieved by models trained with both preparatory and trigger variables; (iv) an incomplete inventory of coseismic slope failures built just after the earthquake event can be used to identify potential locations of yet unreported landslides.
{"title":"Predicting Earthquake-Induced Landslides by Using a Stochastic Modeling Approach: A Case Study of the 2001 El Salvador Coseismic Landslides","authors":"Claudio Mercurio, Laura Paola Calderón-Cucunuba, Abel Alexei Argueta-Platero, Grazia Azzara, C. Cappadonia, C. Martinello, E. Rotigliano, C. Conoscenti","doi":"10.3390/ijgi12040178","DOIUrl":"https://doi.org/10.3390/ijgi12040178","url":null,"abstract":"In January and February 2001, El Salvador was hit by two strong earthquakes that triggered thousands of landslides, causing 1259 fatalities and extensive damage. The analysis of aerial and SPOT-4 satellite images allowed us to map 6491 coseismic landslides, mainly debris slides and flows that occurred in volcanic epiclastites and pyroclastites. Four different multivariate adaptive regression splines (MARS) models were produced using different predictors and landslide inventories which contain slope failures triggered by an extreme rainfall event in 2009 and those induced by the earthquakes of 2001. In a predictive analysis, three validation scenarios were employed: the first and the second included 25% and 95% of the landslides, respectively, while the third was based on a k-fold spatial cross-validation. The results of our analysis revealed that: (i) the MARS algorithm provides reliable predictions of coseismic landslides; (ii) a better ability to predict coseismic slope failures was observed when including susceptibility to rainfall-triggered landslides as an independent variable; (iii) the best accuracy is achieved by models trained with both preparatory and trigger variables; (iv) an incomplete inventory of coseismic slope failures built just after the earthquake event can be used to identify potential locations of yet unreported landslides.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90617981","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}
Similar time series search is one of the most important time series mining tasks in our daily life. As recent advances in sensor technologies accumulate abundant multi-dimensional time series data associated with multivariate quantities, it becomes a privilege to adapt similar time series searches for large-scale and multi-dimensional time series data. However, traditional similar time series search methods are mainly designed for one-dimensional time series, while advanced methods applicable for multi-dimensional time series data are largely immature and, more importantly, are not friendly to users from the domain of geography. As an alternative, we propose a novel method to search similar multi-dimensional time series with spatial databases. Compared with traditional methods that often conduct the similarity search based on features of the raw time series data sequence, the proposed method stores multi-dimensional time series as spatial objects in a spatial database, and then searches similar time series based on their spatial features. To demonstrate the validity of the proposed method, we analyzed the correlation between temporal features of the raw time series and spatial features of their corresponding spatial objects theoretically and empirically. Results indicate that the proposed method can not only support similar multi-dimensional time series searches but also markedly improve its efficiency under many specific scenarios. We believe that such a new paradigm will shed further light on the similarity search in large-scale multi-dimensional time series data, and will lower the barrier for users familiar with spatial databases to conduct complex time series mining tasks.
{"title":"Querying Similar Multi-Dimensional Time Series with a Spatial Database","authors":"Zheren Liu, Chaogui Kang, Xiaoyue Xing","doi":"10.3390/ijgi12040179","DOIUrl":"https://doi.org/10.3390/ijgi12040179","url":null,"abstract":"Similar time series search is one of the most important time series mining tasks in our daily life. As recent advances in sensor technologies accumulate abundant multi-dimensional time series data associated with multivariate quantities, it becomes a privilege to adapt similar time series searches for large-scale and multi-dimensional time series data. However, traditional similar time series search methods are mainly designed for one-dimensional time series, while advanced methods applicable for multi-dimensional time series data are largely immature and, more importantly, are not friendly to users from the domain of geography. As an alternative, we propose a novel method to search similar multi-dimensional time series with spatial databases. Compared with traditional methods that often conduct the similarity search based on features of the raw time series data sequence, the proposed method stores multi-dimensional time series as spatial objects in a spatial database, and then searches similar time series based on their spatial features. To demonstrate the validity of the proposed method, we analyzed the correlation between temporal features of the raw time series and spatial features of their corresponding spatial objects theoretically and empirically. Results indicate that the proposed method can not only support similar multi-dimensional time series searches but also markedly improve its efficiency under many specific scenarios. We believe that such a new paradigm will shed further light on the similarity search in large-scale multi-dimensional time series data, and will lower the barrier for users familiar with spatial databases to conduct complex time series mining tasks.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76342045","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}
Cristian Poliziani, G. Hsueh, David Czerwinski, T. Wenzel, Z. Needell, Haitam Laarabi, J. Schweizer, F. Rupi
We simulate the introduction of shared, automated, and electric vehicles (SAEVs) providing on-demand shuttles service in a large-scale transport digital twin of the San Francisco Bay Area region (California, USA) based on transit supply and demand data, and using the mesoscopic agent-based Behavior, Energy, Autonomy, and Mobility beta software (BEAM) developed at the Lawrence Berkeley National Laboratory (LBNL). The main goal of this study is to test the operations of this novel mobility service integrated with existing fixed-route public transportation service in a mesoscopic simulation of a real case scenario, while testing the BEAM beta software capabilities. In particular, we test the introduction of fleets of on-demand vehicles bound to operate within circular catchment areas centered on high-frequency transit stops, with the purpose of extending the reach of fixed-route transit by providing an alternative first- and last-mile connection at high-frequency public transport stations. Results show that on-demand automated shuttles represent the best solution for some users, increasing the overall transit ridership by 3%, and replacing mostly ride-hail trips, especially those connecting to transit stops, but also some walking trips. This type of service has the potential to reduce overall vehicle miles traveled (VMT), increase transit accessibility, and save energy, but future research is needed to optimize this type of service and make it more attractive to travelers.
{"title":"Micro Transit Simulation of On-Demand Shuttles Based on Transit Data for First- and Last-Mile Connection","authors":"Cristian Poliziani, G. Hsueh, David Czerwinski, T. Wenzel, Z. Needell, Haitam Laarabi, J. Schweizer, F. Rupi","doi":"10.3390/ijgi12040177","DOIUrl":"https://doi.org/10.3390/ijgi12040177","url":null,"abstract":"We simulate the introduction of shared, automated, and electric vehicles (SAEVs) providing on-demand shuttles service in a large-scale transport digital twin of the San Francisco Bay Area region (California, USA) based on transit supply and demand data, and using the mesoscopic agent-based Behavior, Energy, Autonomy, and Mobility beta software (BEAM) developed at the Lawrence Berkeley National Laboratory (LBNL). The main goal of this study is to test the operations of this novel mobility service integrated with existing fixed-route public transportation service in a mesoscopic simulation of a real case scenario, while testing the BEAM beta software capabilities. In particular, we test the introduction of fleets of on-demand vehicles bound to operate within circular catchment areas centered on high-frequency transit stops, with the purpose of extending the reach of fixed-route transit by providing an alternative first- and last-mile connection at high-frequency public transport stations. Results show that on-demand automated shuttles represent the best solution for some users, increasing the overall transit ridership by 3%, and replacing mostly ride-hail trips, especially those connecting to transit stops, but also some walking trips. This type of service has the potential to reduce overall vehicle miles traveled (VMT), increase transit accessibility, and save energy, but future research is needed to optimize this type of service and make it more attractive to travelers.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88785223","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}
South Manitou Island, part of Sleeping Bear Dunes National Lakeshore in northern Lake Michigan, is a post-glacial lacustrine landscape with substantial geomorphic changes including landslides, shoreline and bluff retreat, and sand dune movement. These changes involve interrelated processes, and are influenced to different extents by lake level, climate change, and land use patterns, among other factors. The utility of DEM of Difference (DoD) and other terrain analyses were investigated as a means of understanding interrelated geomorphologic changes and processes across multiple decades and at multiple scales. A 1m DEM was developed from 1955 historical aerial imagery using Structure from Motion Multi-View Stereo (SfM-MVS) and compared to a 2016 lidar-based DEM to quantify change. Landslides, shoreline erosion, bluff retreat, and sand dune movement were investigated throughout South Manitou Island. While the DoD indicates net loss or gain, interpretation of change must take into consideration the SfM-MVS source of the historical DEM. In the case of landslides, where additional understanding may be gleaned through review of the timing of lake high- and lowstands together with DoD values. Landscape-scale findings quantified cumulative feedbacks between interrelated processes. These findings could be upscaled to assess changes across the entire park, informing future change investigations and land management decisions.
南马尼托岛是密歇根湖北部睡熊沙丘国家湖岸的一部分，是一个冰川后的湖泊景观，其地貌发生了重大变化，包括山体滑坡、海岸线和悬崖退缩以及沙丘运动。这些变化涉及相互关联的过程，并在不同程度上受到湖泊水位、气候变化和土地利用模式等因素的影响。利用差分高程(DEM of Difference, DoD)和其他地形分析方法，研究了在几十年和多个尺度上相互关联的地貌变化和过程。利用运动多视点立体结构(SfM-MVS)从1955年的历史航空图像中开发了一个1m的DEM，并与2016年基于激光雷达的DEM进行了比较，以量化变化。研究了南马尼托岛的滑坡、海岸线侵蚀、断崖退缩和沙丘运动。虽然DoD表示净损失或净收益，但对变化的解释必须考虑历史DEM的SfM-MVS来源。在滑坡的情况下，可以通过审查湖泊高低水位的时间以及DoD值来收集更多的了解。景观尺度的发现量化了相互关联过程之间的累积反馈。这些发现可以扩大到评估整个公园的变化，为未来的变化调查和土地管理决策提供信息。
{"title":"Investigating Geomorphic Change Using a Structure from Motion Elevation Model Created from Historical Aerial Imagery: A Case Study in Northern Lake Michigan, USA","authors":"J. DeWitt, F. Ashland","doi":"10.3390/ijgi12040173","DOIUrl":"https://doi.org/10.3390/ijgi12040173","url":null,"abstract":"South Manitou Island, part of Sleeping Bear Dunes National Lakeshore in northern Lake Michigan, is a post-glacial lacustrine landscape with substantial geomorphic changes including landslides, shoreline and bluff retreat, and sand dune movement. These changes involve interrelated processes, and are influenced to different extents by lake level, climate change, and land use patterns, among other factors. The utility of DEM of Difference (DoD) and other terrain analyses were investigated as a means of understanding interrelated geomorphologic changes and processes across multiple decades and at multiple scales. A 1m DEM was developed from 1955 historical aerial imagery using Structure from Motion Multi-View Stereo (SfM-MVS) and compared to a 2016 lidar-based DEM to quantify change. Landslides, shoreline erosion, bluff retreat, and sand dune movement were investigated throughout South Manitou Island. While the DoD indicates net loss or gain, interpretation of change must take into consideration the SfM-MVS source of the historical DEM. In the case of landslides, where additional understanding may be gleaned through review of the timing of lake high- and lowstands together with DoD values. Landscape-scale findings quantified cumulative feedbacks between interrelated processes. These findings could be upscaled to assess changes across the entire park, informing future change investigations and land management decisions.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78054810","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}
Driving analysis of urban expansion (DAUE) is usually implemented to identify the driving factors and their corresponding driving effects/mechanisms for the expansion processes of urban land, aiming to provide scientific guidance for urban planning and management. Based on a thorough analysis and summarization of the development process and quantitative models, four major limitations in existing DAUE studies have been uncovered: (1) the interactions in hierarchical urban systems have not been fully explored; (2) the employed data cannot fully depict urban dynamic through finer social perspectives; (3) the employed models cannot deal with high-level feature correlations; and (4) the simulation and analysis models are still not intrinsically integrated. Four future directions are thus proposed: (1) to pay attention to the hierarchical characteristics of urban systems and conduct multi-scale research on the complex interactions within them to capture dynamic features; (2) to leverage remote sensing data so as to obtain diverse urban expansion data and assimilate multi-source spatiotemporal big data to supplement novel socio-economic driving factors; (3) to integrate with interpretable data-driven machine learning techniques to bolster the performance and reliability of DAUE models; and (4) to construct mechanism-coupled urban simulation to achieve a complementary enhancement and facilitate theory development and testing for urban land systems.
城市扩张驱动分析(Driving analysis of urban expansion, DAUE)通常用于识别城市土地扩张过程的驱动因素及其相应的驱动效应/机制，旨在为城市规划和管理提供科学指导。在对发展过程和定量模型进行深入分析和总结的基础上，揭示了现有城市城市经济研究的四个主要局限性:(1)对城市等级体系中相互作用的探索不够充分;(2)使用的数据不能通过更精细的社会视角全面描绘城市动态;(3)所采用的模型不能处理高层次的特征相关性;(4)仿真模型和分析模型还没有实现内在的整合。提出了四个未来发展方向:(1)关注城市体系的层次性特征，对城市体系内部复杂的相互作用进行多尺度研究，捕捉城市体系的动态特征;(2)利用遥感数据获取多样化的城市扩张数据，吸收多源时空大数据，补充新的社会经济驱动因素;(3)与可解释数据驱动的机器学习技术相结合，提高dae模型的性能和可靠性;(4)构建机制耦合的城市模拟，实现互补增强，促进城市土地系统的理论发展和检验。
{"title":"Development Process, Quantitative Models, and Future Directions in Driving Analysis of Urban Expansion","authors":"Xuefeng Guan, Jingbo Li, Changlan Yang, Weiran Xing","doi":"10.3390/ijgi12040174","DOIUrl":"https://doi.org/10.3390/ijgi12040174","url":null,"abstract":"Driving analysis of urban expansion (DAUE) is usually implemented to identify the driving factors and their corresponding driving effects/mechanisms for the expansion processes of urban land, aiming to provide scientific guidance for urban planning and management. Based on a thorough analysis and summarization of the development process and quantitative models, four major limitations in existing DAUE studies have been uncovered: (1) the interactions in hierarchical urban systems have not been fully explored; (2) the employed data cannot fully depict urban dynamic through finer social perspectives; (3) the employed models cannot deal with high-level feature correlations; and (4) the simulation and analysis models are still not intrinsically integrated. Four future directions are thus proposed: (1) to pay attention to the hierarchical characteristics of urban systems and conduct multi-scale research on the complex interactions within them to capture dynamic features; (2) to leverage remote sensing data so as to obtain diverse urban expansion data and assimilate multi-source spatiotemporal big data to supplement novel socio-economic driving factors; (3) to integrate with interpretable data-driven machine learning techniques to bolster the performance and reliability of DAUE models; and (4) to construct mechanism-coupled urban simulation to achieve a complementary enhancement and facilitate theory development and testing for urban land systems.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89543088","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}
B. Chen, H. Huang, Hui-Ping Chen, Wenxuan Liu, Xuan-Yan Chen, Tao Jia
The order k Voronoi diagram (OkVD) is an effective geometric construction to partition the geographical space into a set of Voronoi regions such that all locations within a Voronoi region share the same k nearest points of interest (POIs). Despite the broad applications of OkVD in various geographical analysis, few efficient algorithms have been proposed to construct OkVD in real road networks. This study proposes a novel algorithm consisting of two stages. In the first stage, a new one-to-all k shortest path finding procedure is proposed to efficiently determine the shortest paths to k nearest POIs for each node. In the second stage, a new recursive procedure is introduced to effectively divide boundary links within different Voronoi regions using the hierarchical tessellation property of the OkVD. To demonstrate the applicability of the proposed OkVD construction algorithm, a case study of place-based accessibility evaluation is carried out. Computational experiments are also conducted on five real road networks with different sizes, and results show that the proposed OkVD algorithm performed significantly better than state-of-the-art algorithms.
{"title":"Efficient Algorithm for Constructing Order K Voronoi Diagrams in Road Networks","authors":"B. Chen, H. Huang, Hui-Ping Chen, Wenxuan Liu, Xuan-Yan Chen, Tao Jia","doi":"10.3390/ijgi12040172","DOIUrl":"https://doi.org/10.3390/ijgi12040172","url":null,"abstract":"The order k Voronoi diagram (OkVD) is an effective geometric construction to partition the geographical space into a set of Voronoi regions such that all locations within a Voronoi region share the same k nearest points of interest (POIs). Despite the broad applications of OkVD in various geographical analysis, few efficient algorithms have been proposed to construct OkVD in real road networks. This study proposes a novel algorithm consisting of two stages. In the first stage, a new one-to-all k shortest path finding procedure is proposed to efficiently determine the shortest paths to k nearest POIs for each node. In the second stage, a new recursive procedure is introduced to effectively divide boundary links within different Voronoi regions using the hierarchical tessellation property of the OkVD. To demonstrate the applicability of the proposed OkVD construction algorithm, a case study of place-based accessibility evaluation is carried out. Computational experiments are also conducted on five real road networks with different sizes, and results show that the proposed OkVD algorithm performed significantly better than state-of-the-art algorithms.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83181318","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}
To have a sustainable city, human pressures on urban ecosystems should not exceed certain thresholds, which are defined by the urban carrying capacity concept. The main goal of this research was to monitor environmental pressures caused by the impacts of human activities on the ecosystem of Tehran city using spatial indicators. According to the enhanced Urban Carrying Capacity Load Number (EUCCLN) model, first, the most related indicators were collected from the open access databases, including satellite products, air quality monitoring stations, municipality statistical yearbook, and a related article. Then, the indicators were classified into air, traffic, and waste groups. Afterwards, the importance coefficients of all indicators were specified using the analytical hierarchy process. Their degree of carrying capacity tables were determined, and finally, load numbers were calculated. The results showed that 100%, 4.55%, and 40.91% of all districts had very high-to-critical degrees in terms of air, traffic, and waste indicators, respectively. The final human-induced pressure degrees were very high-to-critical in Districts 1, 3, 6, 7, 8, 12, and 14 (31.82% out of 22 districts) and high-to-very high in the rest of them. Therefore, the overall pressure in all 22 districts of Tehran had reached or exceeded its maximum threshold degree.
{"title":"Monitoring the Impacts of Human Activities on Urban Ecosystems Based on the Enhanced UCCLN (EUCCLN) Model","authors":"N. .. Tehrani, F. Farhanj, M. Janalipour","doi":"10.3390/ijgi12040170","DOIUrl":"https://doi.org/10.3390/ijgi12040170","url":null,"abstract":"To have a sustainable city, human pressures on urban ecosystems should not exceed certain thresholds, which are defined by the urban carrying capacity concept. The main goal of this research was to monitor environmental pressures caused by the impacts of human activities on the ecosystem of Tehran city using spatial indicators. According to the enhanced Urban Carrying Capacity Load Number (EUCCLN) model, first, the most related indicators were collected from the open access databases, including satellite products, air quality monitoring stations, municipality statistical yearbook, and a related article. Then, the indicators were classified into air, traffic, and waste groups. Afterwards, the importance coefficients of all indicators were specified using the analytical hierarchy process. Their degree of carrying capacity tables were determined, and finally, load numbers were calculated. The results showed that 100%, 4.55%, and 40.91% of all districts had very high-to-critical degrees in terms of air, traffic, and waste indicators, respectively. The final human-induced pressure degrees were very high-to-critical in Districts 1, 3, 6, 7, 8, 12, and 14 (31.82% out of 22 districts) and high-to-very high in the rest of them. Therefore, the overall pressure in all 22 districts of Tehran had reached or exceeded its maximum threshold degree.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89535630","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 spatial learned index constructs a spatial index by learning the spatial distribution, which performs a lower cost of storage and query than the spatial indices. The current update strategies of spatial learned indices can only solve limited updates at the cost of query performance. We propose a novel spatial learned index structure based on a Block Range Index (SLBRIN for short). Its core idea is to cooperate history range and current range to satisfy a fast spatial query and efficient index update simultaneously. SLBRIN deconstructs the update transaction into three parallel operations and optimizes them based on the temporal proximity of spatial distribution. SLBRIN also provides the spatial query strategy with the spatial learned index and spatial location code, including point query, range query and kNN query. Experiments on synthetic and real datasets demonstrate that SLBRIN clearly outperforms traditional spatial indices and state-of-the-art spatial learned indices in the cost of storage and query. Moreover, in the simulated real-time update scenario, SLBRIN has the faster and more stable query performance while satisfying efficient updates.
空间学习索引通过学习空间分布来构建空间索引，具有比空间索引更低的存储和查询成本。目前空间学习索引的更新策略只能解决有限的更新问题，从而降低了查询性能。提出了一种基于块范围索引(Block Range index，简称SLBRIN)的空间学习索引结构。其核心思想是历史范围和当前范围的协同，同时满足快速的空间查询和高效的索引更新。SLBRIN将更新事务分解为三个并行操作，并基于空间分布的时间接近性对其进行优化。SLBRIN还提供了具有空间学习索引和空间位置码的空间查询策略，包括点查询、范围查询和kNN查询。在合成数据集和真实数据集上的实验表明，SLBRIN在存储和查询成本上明显优于传统的空间索引和最先进的空间学习索引。此外，在模拟实时更新场景中，SLBRIN在满足高效更新的同时具有更快、更稳定的查询性能。
{"title":"SLBRIN: A Spatial Learned Index Based on BRIN","authors":"Lijun Wang, Linshu Hu, Chenhua Fu, Yuhan Yu, Peng Tang, Feng Zhang, Ren-yi Liu","doi":"10.3390/ijgi12040171","DOIUrl":"https://doi.org/10.3390/ijgi12040171","url":null,"abstract":"The spatial learned index constructs a spatial index by learning the spatial distribution, which performs a lower cost of storage and query than the spatial indices. The current update strategies of spatial learned indices can only solve limited updates at the cost of query performance. We propose a novel spatial learned index structure based on a Block Range Index (SLBRIN for short). Its core idea is to cooperate history range and current range to satisfy a fast spatial query and efficient index update simultaneously. SLBRIN deconstructs the update transaction into three parallel operations and optimizes them based on the temporal proximity of spatial distribution. SLBRIN also provides the spatial query strategy with the spatial learned index and spatial location code, including point query, range query and kNN query. Experiments on synthetic and real datasets demonstrate that SLBRIN clearly outperforms traditional spatial indices and state-of-the-art spatial learned indices in the cost of storage and query. Moreover, in the simulated real-time update scenario, SLBRIN has the faster and more stable query performance while satisfying efficient updates.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81842166","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}
In visual indoor positioning systems, the method of constructing a visual map by point-by-point sampling is widely used due to its characteristics of clear static images and simple coordinate calculation. However, too small a sampling interval will cause image redundancy, while too large a sampling interval will lead to the absence of any scene images, which will result in worse positioning efficiency and inferior positioning accuracy. As a result, this paper proposed a visual map construction method based on pre-sampled image features matching, according to the epipolar geometry of adjacent position images, to determine the optimal sampling spacing within the constraints and effectively control the database size while ensuring the integrity of the image information. In addition, in order to realize the rapid retrieval of the visual map and reduce the positioning error caused by the time overhead, an image retrieval method based on deep hashing was also designed in this paper. This method used a convolutional neural network to extract image features to construct the semantic similarity structure to guide the generation of hash code. Based on the log-cosh function, this paper proposed a loss function whose function curve was smooth and not affected by outliers, and then integrated it into the deep network to optimize parameters, for fast and accurate image retrieval. Experiments on the FLICKR25K dataset and the visual map proved that the method proposed in this paper could achieve sub-second image retrieval with guaranteed accuracy, thereby demonstrating its promising performance.
{"title":"Image Retrieval Method Based on Visual Map Pre-Sampling Construction in Indoor Positioning","authors":"Jianan Bai, Danyang Qin, Ping Zheng, Lin Ma","doi":"10.3390/ijgi12040169","DOIUrl":"https://doi.org/10.3390/ijgi12040169","url":null,"abstract":"In visual indoor positioning systems, the method of constructing a visual map by point-by-point sampling is widely used due to its characteristics of clear static images and simple coordinate calculation. However, too small a sampling interval will cause image redundancy, while too large a sampling interval will lead to the absence of any scene images, which will result in worse positioning efficiency and inferior positioning accuracy. As a result, this paper proposed a visual map construction method based on pre-sampled image features matching, according to the epipolar geometry of adjacent position images, to determine the optimal sampling spacing within the constraints and effectively control the database size while ensuring the integrity of the image information. In addition, in order to realize the rapid retrieval of the visual map and reduce the positioning error caused by the time overhead, an image retrieval method based on deep hashing was also designed in this paper. This method used a convolutional neural network to extract image features to construct the semantic similarity structure to guide the generation of hash code. Based on the log-cosh function, this paper proposed a loss function whose function curve was smooth and not affected by outliers, and then integrated it into the deep network to optimize parameters, for fast and accurate image retrieval. Experiments on the FLICKR25K dataset and the visual map proved that the method proposed in this paper could achieve sub-second image retrieval with guaranteed accuracy, thereby demonstrating its promising performance.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85082945","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}
Florian Thiery, J. Veller, Laura Raddatz, Louise Rokohl, F. Boochs, A. Mees
In this paper, we introduce applications of Artificial Intelligence techniques, such as Decision Trees and Semantic Reasoning, for semi-automatic and semantic-model-based decision-making for archaeological feature comparisons. This paper uses the example of Roman African Red Slip Ware (ARS) and the collection of ARS at the LEIZA archaeological research institute. The main challenge is to create a Digital Twin of the ARS objects and artefacts using geometric capturing and semantic modelling of archaeological information. Moreover, the individualisation and comparison of features (appliqués), along with their visualisation, extraction, and rectification, results in a strategy and application for comparison of these features using both geometrical and archaeological aspects with a comprehensible rule set. This method of a semi-automatic semantic model-based comparison workflow for archaeological features on Roman ceramics is showcased, discussed, and concluded in three use cases: woman and boy, human–horse hybrid, and bears with local twists and shifts.
{"title":"A Semi-Automatic Semantic-Model-Based Comparison Workflow for Archaeological Features on Roman Ceramics","authors":"Florian Thiery, J. Veller, Laura Raddatz, Louise Rokohl, F. Boochs, A. Mees","doi":"10.3390/ijgi12040167","DOIUrl":"https://doi.org/10.3390/ijgi12040167","url":null,"abstract":"In this paper, we introduce applications of Artificial Intelligence techniques, such as Decision Trees and Semantic Reasoning, for semi-automatic and semantic-model-based decision-making for archaeological feature comparisons. This paper uses the example of Roman African Red Slip Ware (ARS) and the collection of ARS at the LEIZA archaeological research institute. The main challenge is to create a Digital Twin of the ARS objects and artefacts using geometric capturing and semantic modelling of archaeological information. Moreover, the individualisation and comparison of features (appliqués), along with their visualisation, extraction, and rectification, results in a strategy and application for comparison of these features using both geometrical and archaeological aspects with a comprehensible rule set. This method of a semi-automatic semantic model-based comparison workflow for archaeological features on Roman ceramics is showcased, discussed, and concluded in three use cases: woman and boy, human–horse hybrid, and bears with local twists and shifts.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77557048","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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