Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/17857
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dc.contributor.authorKitanovski, Ivanen_US
dc.contributor.authorDimitrovski, Ivicaen_US
dc.contributor.authorPanov, Pancheen_US
dc.contributor.authorSimidjievski, Nikolaen_US
dc.contributor.authorKocev, Dragien_US
dc.date.accessioned2022-06-01T11:27:20Z-
dc.date.available2022-06-01T11:27:20Z-
dc.date.issued2022-01-21-
dc.identifier.urihttp://hdl.handle.net/20.500.12188/17857-
dc.description.abstractThe AiTLAS toolbox (Artificial Intelligence Toolbox for Earth Observation) includes state-of-theart machine learning methods for exploratory and predictive analysis of satellite imagery as well as repository of AI-ready Earth Observation (EO) datasets. It can be easily applied for a variety of Earth Observation tasks, such as land use and cover classification, crop type prediction, localization of specific objects (semantic segmentation), etc. The main goal of AiTLAS is to facilitate better usability and adoption of novel AI methods (and models) by EO experts, while offering easy access and standardized format of EO datasets to AI experts which further allows benchmarking of various existing and novel AI methods tailored for EO data.en_US
dc.relation.ispartofarXiv preprint arXiv:2201.08789en_US
dc.titleAiTLAS: Artificial Intelligence Toolbox for Earth Observationen_US
dc.typeJournal Articleen_US
item.grantfulltextopen-
item.fulltextWith Fulltext-
crisitem.author.deptFaculty of Computer Science and Engineering-
crisitem.author.deptFaculty of Computer Science and Engineering-
Appears in Collections:Faculty of Computer Science and Engineering: Journal Articles
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