The concept of crowdsourcing is nowadays extensively used to refer to the collection of data and ... more The concept of crowdsourcing is nowadays extensively used to refer to the collection of data and the generation of information by large groups of users/contributors. OpenStreetMap (OSM) is a very successful example of a crowd-sourced geospatial data project. Unfortunately, it is often the case that OSM contributor inputs (including geometry and attribute data inserts, deletions and updates) have been found to be inaccurate, incomplete, inconsistent or vague. This is due to several reasons which include: (1) many contributors with little experience or training in mapping and Geographic Information Systems (GIS); (2) not enough contributors familiar with the areas being mapped; (3) contributors having different interpretations of the attributes (tags) for specific features; (4) different levels of enthusiasm between mappers resulting in different number of tags for similar features and (5) the user-friendliness of the online user-interface where the underlying map can be viewed and edited. This paper suggests an automatic mechanism, which uses raw spatial data (trajectories of movements contributed by contributors to OSM) to minimise the uncertainty and impact of the above-mentioned issues. This approach takes the raw trajectory datasets as input and analyses them using data mining techniques. In addition, we extract some patterns and rules about the geometry and attributes of the recognised features for the purpose of insertion or editing of features in the OSM database. The underlying idea is that certain characteristics of user trajectories are directly linked to the geometry and the attributes of geographic features. Using these rules successfully results in the generation of new features with higher spatial quality which are subsequently automatically inserted into the OSM database.
2013 Fourth International Conference on Computing for Geospatial Research and Application, 2013
ABSTRACT form only given: Today a huge amount of geospatial data is being created, collected and ... more ABSTRACT form only given: Today a huge amount of geospatial data is being created, collected and used more than ever before. The ever increasing observations and measurements of geo-sensor networks, satellite imageries, point clouds from laser scanning, geospatial data of Location Based Services (LBS) and location-based social networks has become a serious challenge for data management and analysis systems. Traditionally, Relational Database Management Systems (RDBMS) were used to manage and to some extent analyze the geospatial data. Nowadays these systems can be used in many scenarios but there are some situations when using these systems may not provide the required efficiency and effectiveness. More specifically when the geospatial data has high volume, high frequency of change (in both data content and data structure) and variety of structures, the conventional data storage systems cannot provide needed efficiency in online systems in terms of performance and scalability. In these situations, NoSQL solutions can provide the efficiency necessary for applications using geospatial data. This paper provides an overview of the characteristics of geospatial big data, possible solutions for managing and processing them. Then the paper provides an overview of the major types of NoSQL solutions, their advantages and disadvantages and the challenges they present in managing geospatial big data. Then the paper elaborates on serving geospatial data using standard geospatial web services with a NoSQL XML database as a backend.
Proceedings of the Third ACM SIGSPATIAL International Workshop on Querying and Mining Uncertain Spatio-Temporal Data - QUeST '12, 2012
ABSTRACT Navigation services, such as used in cars, are widely used nowadays. Many applications, ... more ABSTRACT Navigation services, such as used in cars, are widely used nowadays. Many applications, positioning technologies and techniques have been developed to make navigation systems easier to use. However current navigation systems suffer from different aspects of uncertainty such as incomplete or inaccurate positional data. This paper reviews aspects of uncertainty which should be considered when developing navigation systems. A proposed approach, based on rough set and fuzzy set theories, is explained and implemented in an application.
The concept of crowdsourcing is nowadays extensively used to refer to the collection of data and ... more The concept of crowdsourcing is nowadays extensively used to refer to the collection of data and the generation of information by large groups of users/contributors. OpenStreetMap (OSM) is a very successful example of a crowd-sourced geospatial data project. Unfortunately, it is often the case that OSM contributor inputs (including geometry and attribute data inserts, deletions and updates) have been found to be inaccurate, incomplete, inconsistent or vague. This is due to several reasons which include: (1) many contributors with little experience or training in mapping and Geographic Information Systems (GIS); (2) not enough contributors familiar with the areas being mapped; (3) contributors having different interpretations of the attributes (tags) for specific features; (4) different levels of enthusiasm between mappers resulting in different number of tags for similar features and (5) the user-friendliness of the online user-interface where the underlying map can be viewed and edited. This paper suggests an automatic mechanism, which uses raw spatial data (trajectories of movements contributed by contributors to OSM) to minimise the uncertainty and impact of the above-mentioned issues. This approach takes the raw trajectory datasets as input and analyses them using data mining techniques. In addition, we extract some patterns and rules about the geometry and attributes of the recognised features for the purpose of insertion or editing of features in the OSM database. The underlying idea is that certain characteristics of user trajectories are directly linked to the geometry and the attributes of geographic features. Using these rules successfully results in the generation of new features with higher spatial quality which are subsequently automatically inserted into the OSM database.
2013 Fourth International Conference on Computing for Geospatial Research and Application, 2013
ABSTRACT form only given: Today a huge amount of geospatial data is being created, collected and ... more ABSTRACT form only given: Today a huge amount of geospatial data is being created, collected and used more than ever before. The ever increasing observations and measurements of geo-sensor networks, satellite imageries, point clouds from laser scanning, geospatial data of Location Based Services (LBS) and location-based social networks has become a serious challenge for data management and analysis systems. Traditionally, Relational Database Management Systems (RDBMS) were used to manage and to some extent analyze the geospatial data. Nowadays these systems can be used in many scenarios but there are some situations when using these systems may not provide the required efficiency and effectiveness. More specifically when the geospatial data has high volume, high frequency of change (in both data content and data structure) and variety of structures, the conventional data storage systems cannot provide needed efficiency in online systems in terms of performance and scalability. In these situations, NoSQL solutions can provide the efficiency necessary for applications using geospatial data. This paper provides an overview of the characteristics of geospatial big data, possible solutions for managing and processing them. Then the paper provides an overview of the major types of NoSQL solutions, their advantages and disadvantages and the challenges they present in managing geospatial big data. Then the paper elaborates on serving geospatial data using standard geospatial web services with a NoSQL XML database as a backend.
Proceedings of the Third ACM SIGSPATIAL International Workshop on Querying and Mining Uncertain Spatio-Temporal Data - QUeST '12, 2012
ABSTRACT Navigation services, such as used in cars, are widely used nowadays. Many applications, ... more ABSTRACT Navigation services, such as used in cars, are widely used nowadays. Many applications, positioning technologies and techniques have been developed to make navigation systems easier to use. However current navigation systems suffer from different aspects of uncertainty such as incomplete or inaccurate positional data. This paper reviews aspects of uncertainty which should be considered when developing navigation systems. A proposed approach, based on rough set and fuzzy set theories, is explained and implemented in an application.
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Papers by Anahid Basiri