Bo Zhang, Qinlin Li, Hongyang Chao, Billy Chen, E. Ofek, Ying-Qing Xu
Due to the rapid increase in video capture technology, more and more tourist videos are captured every day, creating a challenge for organization and association with metadata. In this paper, we present a novel system for annotating and navigating tourist videos. Placing annotations in a video is difficult because of the need to track the movement of the camera. Navigation of a regular video is also challenging due to the sequential nature of the media. To overcome these challenges, we introduce a system for registering videos to geo-referenced 3D models and analyzing the video contents. We also introduce a novel scheduling algorithm for showing annotations in video. We show results in automatically annotated videos and in a map-based application for browsing videos. Our user study indicates the system is very useful.
{"title":"Annotating and navigating tourist videos","authors":"Bo Zhang, Qinlin Li, Hongyang Chao, Billy Chen, E. Ofek, Ying-Qing Xu","doi":"10.1145/1869790.1869828","DOIUrl":"https://doi.org/10.1145/1869790.1869828","url":null,"abstract":"Due to the rapid increase in video capture technology, more and more tourist videos are captured every day, creating a challenge for organization and association with metadata. In this paper, we present a novel system for annotating and navigating tourist videos. Placing annotations in a video is difficult because of the need to track the movement of the camera. Navigation of a regular video is also challenging due to the sequential nature of the media. To overcome these challenges, we introduce a system for registering videos to geo-referenced 3D models and analyzing the video contents. We also introduce a novel scheduling algorithm for showing annotations in video. We show results in automatically annotated videos and in a map-based application for browsing videos. Our user study indicates the system is very useful.","PeriodicalId":359068,"journal":{"name":"ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121112802","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}
Reasoning about space has been a considerable field of study both in Artificial Intelligence and in spatial information theory. Many applications benefit from the inference of new knowledge about the spatial relationships between spatial objects on the basis of already available and explicit spatial relationship knowledge that we call spatial (relationship) facts. Hence, the task is to derive new spatial facts from known spatial facts. A considerable amount of work has focused on reasoning about topological relationships (as a special and important subset of spatial relationships) between simple spatial objects like simple regions. There is a common consensus in the GIS and spatial database communities that simple regions are insufficient to model spatial reality and that complex region objects are needed that allow multiple components and holes. Models for topological relationships between complex regions have already been developed. Hence, as the next logical step, the goal of this paper is to develop a reasoning model for them. Further, no reasoning model considers changes of the spatial fact basis stored in a database between consecutive queries. We show that conventional modeling suffers from performance degradation when the database is frequently changing. Our model does not assume any geometric representation model or data structure for the regions. The model is also backward compatible, i.e., it is also applicable to simple regions.
{"title":"Topological reasoning between complex regions in databases with frequent updates","authors":"Arif Khan, Markus Schneider","doi":"10.1145/1869790.1869842","DOIUrl":"https://doi.org/10.1145/1869790.1869842","url":null,"abstract":"Reasoning about space has been a considerable field of study both in Artificial Intelligence and in spatial information theory. Many applications benefit from the inference of new knowledge about the spatial relationships between spatial objects on the basis of already available and explicit spatial relationship knowledge that we call spatial (relationship) facts. Hence, the task is to derive new spatial facts from known spatial facts. A considerable amount of work has focused on reasoning about topological relationships (as a special and important subset of spatial relationships) between simple spatial objects like simple regions. There is a common consensus in the GIS and spatial database communities that simple regions are insufficient to model spatial reality and that complex region objects are needed that allow multiple components and holes. Models for topological relationships between complex regions have already been developed. Hence, as the next logical step, the goal of this paper is to develop a reasoning model for them. Further, no reasoning model considers changes of the spatial fact basis stored in a database between consecutive queries. We show that conventional modeling suffers from performance degradation when the database is frequently changing. Our model does not assume any geometric representation model or data structure for the regions. The model is also backward compatible, i.e., it is also applicable to simple regions.","PeriodicalId":359068,"journal":{"name":"ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121563035","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}
Markus Bestehorn, Klemens Böhm, Erik Buchmann, Stephan Kessler
Research on Moving Object Databases (MOD) has resulted in sophisticated query mechanisms for moving objects and regions. Wireless Sensor Networks (WSN) support a wide range of applications that track or monitor moving objects. However, applying the concepts of MOD to WSN is difficult: While MOD tend to require precise object positions, the information acquired in WSN may be incomplete or inaccurate. This may be because of limited detection ranges, node failures or detection mechanisms that only determine if an object is in the vicinity of a node, but not its exact position. In this paper, we study the processing of spatiotemporal queries in WSN. First, we adapt the models used in MOD to WSN while keeping their semantical depth. Second, we propose two approaches for processing such queries in WSN in-network instead of collecting all data at the base station. Our experimental evaluations using simulation as well as a Sun SPOT deployment show that our measures reduce communication by up to 89%, compared to collecting all information at the base station.
{"title":"Energy-efficient processing of spatio-temporal queries in wireless sensor networks","authors":"Markus Bestehorn, Klemens Böhm, Erik Buchmann, Stephan Kessler","doi":"10.1145/1869790.1869838","DOIUrl":"https://doi.org/10.1145/1869790.1869838","url":null,"abstract":"Research on Moving Object Databases (MOD) has resulted in sophisticated query mechanisms for moving objects and regions. Wireless Sensor Networks (WSN) support a wide range of applications that track or monitor moving objects. However, applying the concepts of MOD to WSN is difficult: While MOD tend to require precise object positions, the information acquired in WSN may be incomplete or inaccurate. This may be because of limited detection ranges, node failures or detection mechanisms that only determine if an object is in the vicinity of a node, but not its exact position. In this paper, we study the processing of spatiotemporal queries in WSN. First, we adapt the models used in MOD to WSN while keeping their semantical depth. Second, we propose two approaches for processing such queries in WSN in-network instead of collecting all data at the base station. Our experimental evaluations using simulation as well as a Sun SPOT deployment show that our measures reduce communication by up to 89%, compared to collecting all information at the base station.","PeriodicalId":359068,"journal":{"name":"ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130721395","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}
M. Buchin, A. Driemel, M. V. Kreveld, Vera Sacristán Adinolfi
In this paper we address the problem of segmenting a trajectory such that each segment is in some sense homogeneous. We formally define different spatio-temporal criteria under which a trajectory can be homogeneous, including location, heading, speed, velocity, curvature, sinuosity, and curviness. We present a framework that allows us to segment any trajectory into a minimum number of segments under any of these criteria, or any combination of these criteria. In this framework, the segmentation problem can generally be solved in O(n log n) time, where n is the number of edges of the trajectory to be segmented.
{"title":"An algorithmic framework for segmenting trajectories based on spatio-temporal criteria","authors":"M. Buchin, A. Driemel, M. V. Kreveld, Vera Sacristán Adinolfi","doi":"10.1145/1869790.1869821","DOIUrl":"https://doi.org/10.1145/1869790.1869821","url":null,"abstract":"In this paper we address the problem of segmenting a trajectory such that each segment is in some sense homogeneous. We formally define different spatio-temporal criteria under which a trajectory can be homogeneous, including location, heading, speed, velocity, curvature, sinuosity, and curviness. We present a framework that allows us to segment any trajectory into a minimum number of segments under any of these criteria, or any combination of these criteria. In this framework, the segmentation problem can generally be solved in O(n log n) time, where n is the number of edges of the trajectory to be segmented.","PeriodicalId":359068,"journal":{"name":"ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133156421","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}
GPS-equipped taxis can be regarded as mobile sensors probing traffic flows on road surfaces, and taxi drivers are usually experienced in finding the fastest (quickest) route to a destination based on their knowledge. In this paper, we mine smart driving directions from the historical GPS trajectories of a large number of taxis, and provide a user with the practically fastest route to a given destination at a given departure time. In our approach, we propose a time-dependent landmark graph, where a node (landmark) is a road segment frequently traversed by taxis, to model the intelligence of taxi drivers and the properties of dynamic road networks. Then, a Variance-Entropy-Based Clustering approach is devised to estimate the distribution of travel time between two landmarks in different time slots. Based on this graph, we design a two-stage routing algorithm to compute the practically fastest route. We build our system based on a real-world trajectory dataset generated by over 33,000 taxis in a period of 3 months, and evaluate the system by conducting both synthetic experiments and in-the-field evaluations. As a result, 60-70% of the routes suggested by our method are faster than the competing methods, and 20% of the routes share the same results. On average, 50% of our routes are at least 20% faster than the competing approaches.
{"title":"T-drive: driving directions based on taxi trajectories","authors":"Jing Yuan, Yu Zheng, Chengyang Zhang, Wenlei Xie, Xing Xie, Guangzhong Sun, Y. Huang","doi":"10.1145/1869790.1869807","DOIUrl":"https://doi.org/10.1145/1869790.1869807","url":null,"abstract":"GPS-equipped taxis can be regarded as mobile sensors probing traffic flows on road surfaces, and taxi drivers are usually experienced in finding the fastest (quickest) route to a destination based on their knowledge. In this paper, we mine smart driving directions from the historical GPS trajectories of a large number of taxis, and provide a user with the practically fastest route to a given destination at a given departure time. In our approach, we propose a time-dependent landmark graph, where a node (landmark) is a road segment frequently traversed by taxis, to model the intelligence of taxi drivers and the properties of dynamic road networks. Then, a Variance-Entropy-Based Clustering approach is devised to estimate the distribution of travel time between two landmarks in different time slots. Based on this graph, we design a two-stage routing algorithm to compute the practically fastest route. We build our system based on a real-world trajectory dataset generated by over 33,000 taxis in a period of 3 months, and evaluate the system by conducting both synthetic experiments and in-the-field evaluations. As a result, 60-70% of the routes suggested by our method are faster than the competing methods, and 20% of the routes share the same results. On average, 50% of our routes are at least 20% faster than the competing approaches.","PeriodicalId":359068,"journal":{"name":"ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133431780","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}
With the popularity of location-based services and the abundant usage of smart phones and GPS enabled devices, the necessity of outsourcing spatial data has grown rapidly over the past few years. Nevertheless, in the database outsourcing paradigm, the authentication of the query results at the client remains a challenging problem. In this paper, we focus on the Outsourced Spatial Database (OSDB) model and propose an efficient scheme, called VN-Auth, that allows a client to verify the correctness and completeness of the result set. Our approach can handle both k nearest neighbor (kNN) and range queries, and is based on neighborhood information derived by the Voronoi diagram of the underlying spatial dataset. Specifically, upon receiving a query result, the client can verify its integrity by examining the signatures and exploring the neighborhood of every object in the result set. Compared to the current state-of-the-art approaches (i.e., methods based on Merkle hash trees), VN-Auth produces significantly smaller verification objects (VO) and is more computationally efficient, especially for queries with low selectivity.
{"title":"Verifying spatial queries using Voronoi neighbors","authors":"Ling Hu, Wei-Shinn Ku, S. Bakiras, C. Shahabi","doi":"10.1145/1869790.1869839","DOIUrl":"https://doi.org/10.1145/1869790.1869839","url":null,"abstract":"With the popularity of location-based services and the abundant usage of smart phones and GPS enabled devices, the necessity of outsourcing spatial data has grown rapidly over the past few years. Nevertheless, in the database outsourcing paradigm, the authentication of the query results at the client remains a challenging problem. In this paper, we focus on the Outsourced Spatial Database (OSDB) model and propose an efficient scheme, called VN-Auth, that allows a client to verify the correctness and completeness of the result set. Our approach can handle both k nearest neighbor (kNN) and range queries, and is based on neighborhood information derived by the Voronoi diagram of the underlying spatial dataset. Specifically, upon receiving a query result, the client can verify its integrity by examining the signatures and exploring the neighborhood of every object in the result set. Compared to the current state-of-the-art approaches (i.e., methods based on Merkle hash trees), VN-Auth produces significantly smaller verification objects (VO) and is more computationally efficient, especially for queries with low selectivity.","PeriodicalId":359068,"journal":{"name":"ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128644787","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}
Current Internet-inspired mapping data are in the form of street maps, orthophotos, 3D models or street-side images to support searches and navigation. Images represent predominantly "eye candy" with little added value to the Internet-user. We analyze the Internet-inspired vertical aerial images themselves to add value to the data by detecting interesting data about the buildings, initially counting building floors and windows. For this purpose, oblique aerial imagery is less useful due to its excessive occlusions. This paper specifically introduces 3D point clouds to deal with balconies and awnings in the counting of floors and windows.
{"title":"Augmented internet maps with property information from aerial imagery","authors":"P. Meixner, F. Leberl","doi":"10.1145/1869790.1869848","DOIUrl":"https://doi.org/10.1145/1869790.1869848","url":null,"abstract":"Current Internet-inspired mapping data are in the form of street maps, orthophotos, 3D models or street-side images to support searches and navigation. Images represent predominantly \"eye candy\" with little added value to the Internet-user. We analyze the Internet-inspired vertical aerial images themselves to add value to the data by detecting interesting data about the buildings, initially counting building floors and windows. For this purpose, oblique aerial imagery is less useful due to its excessive occlusions. This paper specifically introduces 3D point clouds to deal with balconies and awnings in the counting of floors and windows.","PeriodicalId":359068,"journal":{"name":"ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114594799","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}
Many Web sites support keyword search on their spatial data, such as business listings and photos. In these systems, inconsistencies and errors can exist in both queries and the data. To bridge the gap between queries and data, it is important to support approximate keyword search on spatial data. In this paper we study how to answer such queries efficiently. We focus on a natural index structure that augments a tree-based spatial index with capabilities for approximate keyword search. We systematically study how to efficiently combine these two types of indexes, and how to search the resulting index to find answers. We develop three algorithms for constructing the index, successively improving the time and space efficiency by exploiting the textual and spatial properties of the data. We experimentally demonstrate the efficiency of our techniques on real, large datasets.
{"title":"Supporting location-based approximate-keyword queries","authors":"Sattam Alsubaiee, Alexander Behm, Chen Li","doi":"10.1145/1869790.1869802","DOIUrl":"https://doi.org/10.1145/1869790.1869802","url":null,"abstract":"Many Web sites support keyword search on their spatial data, such as business listings and photos. In these systems, inconsistencies and errors can exist in both queries and the data. To bridge the gap between queries and data, it is important to support approximate keyword search on spatial data. In this paper we study how to answer such queries efficiently. We focus on a natural index structure that augments a tree-based spatial index with capabilities for approximate keyword search. We systematically study how to efficiently combine these two types of indexes, and how to search the resulting index to find answers. We develop three algorithms for constructing the index, successively improving the time and space efficiency by exploiting the textual and spatial properties of the data. We experimentally demonstrate the efficiency of our techniques on real, large datasets.","PeriodicalId":359068,"journal":{"name":"ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126094643","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 growing popularity of online Earth visualization tools and geo-realistic games and the availability of high resolution terrain data have motivated a new class of queries to the interests of the GIS and spatial database community: spatial queries (e.g., kNN) over land surface. However, the fundamental challenges that restrict the applicability of these studies to real world applications are the prohibitive time complexity and storage overhead to precompute the shortest surface paths. In this paper, for the first time, we propose an approximate solution to address both challenges and allow browsing the shortest surface paths in O(log N + √N) time, where N is the size of the terrain. With this method, the time and space requirements for an exhaustive all-pair pre-computation have been reduced from O(N3) to O(N1.5) and O(N) respectively. The substantial savings in both time and storage are gained by taking advantage of the fact that the O(N2) surface paths only deviate from approximate straight lines at O(√N) points, termed rough vertices. As a result, we propose a linear time shortest surface path computation algorithm between two arbitrary vertices and a linear size storage structure, which captures all the shortest surface paths between any pair of vertices. We experimentally verified the applicability and scalability of the proposed methods with large real world and synthetic data sets and showed that accuracy higher than 97% can be obtained in most cases.
在线地球可视化工具和地理逼真游戏的日益普及以及高分辨率地形数据的可用性激发了一类新的查询，以满足GIS和空间数据库社区的兴趣:陆地表面的空间查询(例如，kNN)。然而，限制这些研究在实际应用中的适用性的基本挑战是预先计算最短表面路径的时间复杂性和存储开销。在本文中，我们首次提出了一个近似的解决方案来解决这两个挑战，并允许在O(log N +√N)时间内浏览最短的表面路径，其中N是地形的大小。该方法将全对预计算的时间和空间要求分别从0 (N3)减少到0 (N1.5)和0 (N)。通过利用O(N2)个表面路径只在O(√N)个点(称为粗糙顶点)偏离近似直线的事实，可以节省大量的时间和存储空间。因此，我们提出了一种任意两个顶点之间的线性时间最短表面路径计算算法和一个线性大小的存储结构，该存储结构可以捕获任意一对顶点之间的所有最短表面路径。通过大型真实世界和合成数据集的实验验证了所提出方法的适用性和可扩展性，并表明在大多数情况下可以获得高于97%的准确率。
{"title":"Scalable shortest paths browsing on land surface","authors":"Songhua Xing, C. Shahabi","doi":"10.1145/1869790.1869806","DOIUrl":"https://doi.org/10.1145/1869790.1869806","url":null,"abstract":"The growing popularity of online Earth visualization tools and geo-realistic games and the availability of high resolution terrain data have motivated a new class of queries to the interests of the GIS and spatial database community: spatial queries (e.g., kNN) over land surface. However, the fundamental challenges that restrict the applicability of these studies to real world applications are the prohibitive time complexity and storage overhead to precompute the shortest surface paths. In this paper, for the first time, we propose an approximate solution to address both challenges and allow browsing the shortest surface paths in O(log N + √N) time, where N is the size of the terrain. With this method, the time and space requirements for an exhaustive all-pair pre-computation have been reduced from O(N3) to O(N1.5) and O(N) respectively. The substantial savings in both time and storage are gained by taking advantage of the fact that the O(N2) surface paths only deviate from approximate straight lines at O(√N) points, termed rough vertices. As a result, we propose a linear time shortest surface path computation algorithm between two arbitrary vertices and a linear size storage structure, which captures all the shortest surface paths between any pair of vertices. We experimentally verified the applicability and scalability of the proposed methods with large real world and synthetic data sets and showed that accuracy higher than 97% can be obtained in most cases.","PeriodicalId":359068,"journal":{"name":"ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122036445","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 recent times the amount of spatial data being collected by voluntary users, e.g. as part of the OpenStreetMap project, is rapidly increasing. Due to the fact, that everyone can participate in this social collaboration, the completeness and accuracy of the data is very heterogeneous. Although a object catalogue exists as part of the OSM project, users are not restricted which attributes they set and to which detail. Therefore the geometry of a feature is more reliable than its attributes. However, in order to use the data for analysis purposes, knowledge about the semantic contents is of importance. In our work, we propose an approach to classify spatial data solely based on geometric and topologic characteristics. We use both building outlines and road network information. In the first step, topology errors are fixed in order to create a consistent dataset. In the second step, we use unsupervised classification to separate buildings into clusters sharing the same characteristics. Including expert knowledge by visual inspection and interaction, some of these clusters are grouped together and semantically enriched. In the third step, we transfer the derived information from individual buildings to city blocks that are enclosed by edges of the road network. We evaluate our approach with test datasets from OSM and available authoritative datasets. Our results show, that enrichment of user-generated data is possible based on geometric and topologic feature characteristics.
{"title":"Semi-automatic interpretation of buildings and settlement areas in user-generated spatial data","authors":"S. Werder, Birgit Kieler, Monika Sester","doi":"10.1145/1869790.1869836","DOIUrl":"https://doi.org/10.1145/1869790.1869836","url":null,"abstract":"In recent times the amount of spatial data being collected by voluntary users, e.g. as part of the OpenStreetMap project, is rapidly increasing. Due to the fact, that everyone can participate in this social collaboration, the completeness and accuracy of the data is very heterogeneous. Although a object catalogue exists as part of the OSM project, users are not restricted which attributes they set and to which detail. Therefore the geometry of a feature is more reliable than its attributes. However, in order to use the data for analysis purposes, knowledge about the semantic contents is of importance.\u0000 In our work, we propose an approach to classify spatial data solely based on geometric and topologic characteristics. We use both building outlines and road network information. In the first step, topology errors are fixed in order to create a consistent dataset. In the second step, we use unsupervised classification to separate buildings into clusters sharing the same characteristics. Including expert knowledge by visual inspection and interaction, some of these clusters are grouped together and semantically enriched. In the third step, we transfer the derived information from individual buildings to city blocks that are enclosed by edges of the road network. We evaluate our approach with test datasets from OSM and available authoritative datasets. Our results show, that enrichment of user-generated data is possible based on geometric and topologic feature characteristics.","PeriodicalId":359068,"journal":{"name":"ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131322657","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：