Artificial intelligence has significantly altered many job workflows, hence expanding earlier notions of limitations, outcomes, size, and prices. GeoAI is a multidisciplinary field that encompasses computer science, e...Artificial intelligence has significantly altered many job workflows, hence expanding earlier notions of limitations, outcomes, size, and prices. GeoAI is a multidisciplinary field that encompasses computer science, engineering, statistics, and spatial science. Because this subject focuses on real-world issues, it has a significant impact on society and the economy. A broad context incorporating fundamental questions of theory, epistemology, and the scientific method is used to bring artificial intelligence (Al) and geography together. This connection has the potential to have far-reaching implications for the geographic study. GeoAI, or the combination of geography with artificial intelligence, offers unique solutions to a variety of smart city issues. This paper provides an overview of GeoAI technology, including the definition of GeoAI and the differences between GeoAI and traditional AI. Key steps to successful geographic data analysis include integrating AI with GIS and using GeoAI tools and technologies. Also shown are key areas of applications and models in GeoAI, likewise challenges to adopt GeoAI methods and technology as well as benefits. This article also included a case study on the use of GeoAI in Kuwait, as well as a number of recommendations.展开更多
The enhancement of computing power,the maturity of learning algorithms,and the richness of application scenarios make Artificial Intelligence(AI)solution increasingly attractive when solving Geo-spatial Information Sc...The enhancement of computing power,the maturity of learning algorithms,and the richness of application scenarios make Artificial Intelligence(AI)solution increasingly attractive when solving Geo-spatial Information Science(GSIS)problems.These include image matching,image target detection,change detection,image retrieval,and for generating data models of various types.This paper discusses the connection and synthesis between AI and GSIS in block adjustment,image search and discovery in big databases,automatic change detection,and detection of abnormalities,demonstrating that AI can integrate GSIS.Moreover,the concept of Earth Observation Brain and Smart Geo-spatial Service(SGSS)is introduced in the end,and it is expected to promote the development of GSIS into broadening applications.展开更多
This paper presents a dynamic geospatial knowledge graph as part of The World Avatar project,with an underlying ontology based on CityGML 2.0 for three-dimensional geometrical city objects.We comprehensively evaluated...This paper presents a dynamic geospatial knowledge graph as part of The World Avatar project,with an underlying ontology based on CityGML 2.0 for three-dimensional geometrical city objects.We comprehensively evaluated,repaired and refined an existing CityGML ontology to produce an improved version that could pass the necessary tests and complete unit test development.A corresponding data transformation tool,originally designed to work alongside CityGML,was extended.This allowed for the transformation of original data into a form of semantic triples.We compared various scalable technologies for this semantic data storage and chose Blazegraph™as it provided the required geospatial search functionality.We also evaluated scalable hardware data solutions and file systems using the publicly available CityGML 2.0 data of Charlottenburg in Berlin,Germany as a working example.The structural isomorphism of the CityGML schemas and the OntoCityGML Tbox allowed the data to be transformed without loss of information.Efficient geospatial search algorithms allowed us to retrieve building data from any point in a city using coordinates.The use of named graphs and namespaces for data partitioning ensured the system performance stayed well below its capacity limits.This was achieved by evaluating scalable and dedicated data storage hardware capable of hosting expansible file systems,which strengthened the architectural foundations of the target system.展开更多
This paper presents a system of autonomous intelligent software agents,based on a cognitive architecture,capable of automated instantiation,visualisation and analysis of multifaceted City Information Models in dynamic...This paper presents a system of autonomous intelligent software agents,based on a cognitive architecture,capable of automated instantiation,visualisation and analysis of multifaceted City Information Models in dynamic geospatial knowledge graphs.Design of JPS Agent Framework and Routed Knowledge Graph Access components was required in order to provide backbone infrastructure for an intelligent agent system as well as technology agnostic knowledge graph access enabling automation of multi-domain data interoperability.Development of CityImportAgent,CityExportAgent and DistanceAgent showcased intelligent automation capa-bilities of the Cities Knowledge Graph.The agents successfully created a semantic model of Berlin in LOD 2,compliant with CityGML 2.0 standard and consisting of 419909661 triples described using OntoCityGML.The system of agents also visualised and analysed the model by autonomously tracking interactions with a web interface as well as enriched the model by adding new information to the knowledge graph.This way it was possible to design a geospatial information system able to meet demands imposed by the Industry 4.0 and link it with the other multi-domain knowledge representations of The World Avatar.展开更多
文摘Artificial intelligence has significantly altered many job workflows, hence expanding earlier notions of limitations, outcomes, size, and prices. GeoAI is a multidisciplinary field that encompasses computer science, engineering, statistics, and spatial science. Because this subject focuses on real-world issues, it has a significant impact on society and the economy. A broad context incorporating fundamental questions of theory, epistemology, and the scientific method is used to bring artificial intelligence (Al) and geography together. This connection has the potential to have far-reaching implications for the geographic study. GeoAI, or the combination of geography with artificial intelligence, offers unique solutions to a variety of smart city issues. This paper provides an overview of GeoAI technology, including the definition of GeoAI and the differences between GeoAI and traditional AI. Key steps to successful geographic data analysis include integrating AI with GIS and using GeoAI tools and technologies. Also shown are key areas of applications and models in GeoAI, likewise challenges to adopt GeoAI methods and technology as well as benefits. This article also included a case study on the use of GeoAI in Kuwait, as well as a number of recommendations.
基金This work was supported in part by the National key R and D plan on strategic international scientific and technological innovation cooperation special project[grant number 2016YFE0202300]the National Natural Science Foundation of China[grant number 61671332,41771452,51708426,41890820,41771454]+1 种基金the Natural Science Fund of Hubei Province in China[grant number 2018CFA007]the Independent Research Projects of Wuhan University[grant number 2042018kf0250].
文摘The enhancement of computing power,the maturity of learning algorithms,and the richness of application scenarios make Artificial Intelligence(AI)solution increasingly attractive when solving Geo-spatial Information Science(GSIS)problems.These include image matching,image target detection,change detection,image retrieval,and for generating data models of various types.This paper discusses the connection and synthesis between AI and GSIS in block adjustment,image search and discovery in big databases,automatic change detection,and detection of abnormalities,demonstrating that AI can integrate GSIS.Moreover,the concept of Earth Observation Brain and Smart Geo-spatial Service(SGSS)is introduced in the end,and it is expected to promote the development of GSIS into broadening applications.
文摘This paper presents a dynamic geospatial knowledge graph as part of The World Avatar project,with an underlying ontology based on CityGML 2.0 for three-dimensional geometrical city objects.We comprehensively evaluated,repaired and refined an existing CityGML ontology to produce an improved version that could pass the necessary tests and complete unit test development.A corresponding data transformation tool,originally designed to work alongside CityGML,was extended.This allowed for the transformation of original data into a form of semantic triples.We compared various scalable technologies for this semantic data storage and chose Blazegraph™as it provided the required geospatial search functionality.We also evaluated scalable hardware data solutions and file systems using the publicly available CityGML 2.0 data of Charlottenburg in Berlin,Germany as a working example.The structural isomorphism of the CityGML schemas and the OntoCityGML Tbox allowed the data to be transformed without loss of information.Efficient geospatial search algorithms allowed us to retrieve building data from any point in a city using coordinates.The use of named graphs and namespaces for data partitioning ensured the system performance stayed well below its capacity limits.This was achieved by evaluating scalable and dedicated data storage hardware capable of hosting expansible file systems,which strengthened the architectural foundations of the target system.
文摘This paper presents a system of autonomous intelligent software agents,based on a cognitive architecture,capable of automated instantiation,visualisation and analysis of multifaceted City Information Models in dynamic geospatial knowledge graphs.Design of JPS Agent Framework and Routed Knowledge Graph Access components was required in order to provide backbone infrastructure for an intelligent agent system as well as technology agnostic knowledge graph access enabling automation of multi-domain data interoperability.Development of CityImportAgent,CityExportAgent and DistanceAgent showcased intelligent automation capa-bilities of the Cities Knowledge Graph.The agents successfully created a semantic model of Berlin in LOD 2,compliant with CityGML 2.0 standard and consisting of 419909661 triples described using OntoCityGML.The system of agents also visualised and analysed the model by autonomously tracking interactions with a web interface as well as enriched the model by adding new information to the knowledge graph.This way it was possible to design a geospatial information system able to meet demands imposed by the Industry 4.0 and link it with the other multi-domain knowledge representations of The World Avatar.
