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Big Data Computing for Geospatial Applications
Big Data Computing for Geospatial Applications
Autore Li Zhenlong
Pubbl/distr/stampa Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020
Descrizione fisica 1 online resource (222 p.)
Soggetto topico Geography
Research & information: general
Soggetto non controllato big data
big geospatial data
big mobile navigation trajectory data
CA Markov
city blocks
climate science
cloud
cloud computing
cyberGIS
ELT
ETL
fine-grained emotion classification
formalization
GeoAI
geographic knowledge graph
geographic knowledge representation
GeoKG
geospatial big data
geospatial computing
geospatial cyberinfrastructure
geospatial problem-solving
geovisual analytics
global terrain dataset
Hadoop
hazard mitigation
IoT
knowledge base
land-use change prediction
machine learning
MapReduce
massive data
metadata
missing road
mobility community
overlay analysis
parallel computing
sensor data
shape complexity
smart card data
social media
spatial thinking
spatio-temporal analysis
task
terrain modeling
topographic surface
topology
transit corridor
trip
web cataloging service
workflow
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910557664803321
Li Zhenlong  
Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
High Performance Computing for Geospatial Applications / / edited by Wenwu Tang, Shaowen Wang
High Performance Computing for Geospatial Applications / / edited by Wenwu Tang, Shaowen Wang
Edizione [1st ed. 2020.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020
Descrizione fisica 1 online resource (XIII, 296 p. 94 illus., 70 illus. in color.)
Disciplina 004.35
Collana Geotechnologies and the Environment
Soggetto topico Remote sensing
Big data
Sociophysics
Econophysics
Computer simulation
Environmental sciences
Landscape ecology
Remote Sensing/Photogrammetry
Big Data
Data-driven Science, Modeling and Theory Building
Simulation and Modeling
Environmental Science and Engineering
Landscape Ecology
ISBN 3-030-47998-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910411920603321
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui