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[null,"1.","Record Nr.",null,"UNINA9910510417703321"]
[null,null,"Titolo",null,"Proceedings of the 4th ACM SIGSPATIAL international workshop on AI for geographic knowledge discovery / / Dalton Lunga [and six others], editors"]
[null,null,"Pubbl/distr/stampa",null,"New York, New York : , : Association for Computing Machinery, , 2021"]
[null,null,"Descrizione fisica",null,"1 online resource (77 pages)"]
[null,null,"Disciplina",null,"004"]
[null,null,"Soggetti",null,"Computer science"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Sommario/riassunto","Emerging advances from artificial intelligence, hardware accelerators, and processing architectures continue to transform societal challenges impacted by geospatial applications. Recent breakthroughs in deep learning have brought forward an automated capability to learn hierarchical representational features from massive and complex data, including text, images, and videos. In tandem, rapid innovations in sensing technologies are collecting geospatial data in even higher resolution and throughput to enable mapping and analysis of the earth's surface, events, and various phenomena in unprecedented detail. When integrated, these developments offer potential breakthrough opportunities in geographic knowledge discovery geared to impact better decision making. The outcomes have broader implications, from humanitarian mapping, intelligent transport systems, urban expansion analysis, spatial diffusion methods to support epidemiology, climate change-induced threats, natural disasters, and monitoring of the earth's surface."]