Artificial Intelligence for Multisource Geospatial Information

This reprint collects 10 original research contributions published in the Special Issue entitled "Artificial Intelligence for Multisource Geospatial Information" of the ISPRS International Journal of Geo-Information. The focus is on different methods of Geospatial Artificial Intelligence (...

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Bibliographic Details
Other Authors: Bordogna, Gloria (Editor), Fugazza, Cristiano (Editor)
Format: Electronic Book Chapter
Language:English
Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
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DOAB: description of the publication
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520 |a This reprint collects 10 original research contributions published in the Special Issue entitled "Artificial Intelligence for Multisource Geospatial Information" of the ISPRS International Journal of Geo-Information. The focus is on different methods of Geospatial Artificial Intelligence (GeoAI) based on deep learning using different network architectures, clustering, soft computing, and semantic approaches. They are proposed to deal with a variety of Geospatial Big Data (GBD), such as georeferenced texts and photos in social networks, remote sensing images, cartographic maps, multidimensional geo databases, metadata in spatial data infrastructures, and for different tasks, such as for multisource georeferenced text integration and geodata flexible querying, for social sensing by applying sentiment analysis, clustering and geo analysis, for segmentation of roads, clouds and snow, and for detection of small targets and people on the streets. 
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546 |a English 
650 7 |a Technology: general issues  |2 bicssc 
650 7 |a History of engineering & technology  |2 bicssc 
653 |a geosemantics 
653 |a implicit semantics 
653 |a formal semantics 
653 |a powerful semantics 
653 |a satellite image 
653 |a semantic segmentation 
653 |a encoder-decoder 
653 |a CNN 
653 |a TH-1 
653 |a cloud and snow detection 
653 |a label quality 
653 |a street crime 
653 |a people on the street 
653 |a streetscape 
653 |a Baidu Street View image 
653 |a spatial lag negative binomial regression 
653 |a deep learning 
653 |a convolutional neural network 
653 |a backbone network 
653 |a target detection 
653 |a remote sensing images 
653 |a segmentation 
653 |a high resolution 
653 |a transformer 
653 |a OLAP 
653 |a fuzzy SOLAP-based framework 
653 |a fuzzy spatiotemporal queries 
653 |a fuzzy spatiotemporal predictive query 
653 |a fuzzy query visualization 
653 |a machine learning 
653 |a classification 
653 |a LiDAR 
653 |a 3D point cloud 
653 |a urban trees 
653 |a image feature vector 
653 |a clustering 
653 |a Siamese Network 
653 |a automatic classification of tourist photos 
653 |a deep learning model 
653 |a Arabic tweets 
653 |a COVID-19 pandemic 
653 |a sentiment analysis 
653 |a social data mining 
653 |a spatio-temporal correlation 
653 |a off-line integration of geo-tagged data sets 
653 |a data sets about public places 
653 |a soft integration methodology 
653 |a effective soft integration through a stand-along tool 
653 |a n/a 
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