Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/6520
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dc.contributor.authorProf. LI Yi Man, Ritaen_US
dc.contributor.authorChau, Kwong Wingen_US
dc.contributor.authorLi, Ching Yu, Herruen_US
dc.contributor.authorZeng, Fanjieen_US
dc.contributor.authorTang, Beiqien_US
dc.contributor.authorDing, Meilinen_US
dc.date.accessioned2021-03-07T07:23:06Z-
dc.date.available2021-03-07T07:23:06Z-
dc.date.issued2020-
dc.identifier.citationIn Tareq, Ahram (ed.) (2020). Advances in artificial intelligence, software and systems engineering, proceedings of the AHFE 2020 virtual conferences on software and systems engineering, and artificial intelligence and social computing (pp. 113-118).en_US
dc.identifier.isbn9783030513276-
dc.identifier.isbn9783030513283-
dc.identifier.urihttp://hdl.handle.net/20.500.11861/6520-
dc.description.abstractGlobal warming has become a major environmental concern in recent years. It is believed that urban heat island issues have caused health problems and thus we aim to investigate the impact of the heat island effect on housing prices by studying Whampoa Garden – one of the largest trading volume housing estates in Hong Kong. Landsat 8, an advance satellite that comprises the camera of the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) were used to collect satellite images from 2014 to 2018 from the United States Geological Survey (USGS). All the satellite images of the study points are equipped with the least cloud. We then used that information to conduct housing price prediction via AutoML.en_US
dc.language.isoenen_US
dc.titleRemote sensing, heat island effect and housing price prediction via autoMLen_US
dc.typeConference Paperen_US
dc.relation.conferenceAHFE 2020: Advances in Artificial Intelligence, Software and Systems Engineeringen_US
dc.identifier.doi10.1007/978-3-030-51328-3_17-
item.fulltextNo Fulltext-
crisitem.author.deptDepartment of Economics and Finance-
Appears in Collections:Economics and Finance - Publication
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