Context Aware Object Geotagging
IMVIP 2021 Localization of street objects from images has gained a lot of attention in recent years. We propose an approach to improve asset geolocation from street view imagery by enhancing the quality of the metadata associated with the images using Structure from Motion. The predicted object geol...
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| creator | Liu, Chao-Jung Ulicny, Matej Manzke, Michael Dahyot, Rozenn |
| description | IMVIP 2021 Localization of street objects from images has gained a lot of attention in
recent years. We propose an approach to improve asset geolocation from street
view imagery by enhancing the quality of the metadata associated with the
images using Structure from Motion. The predicted object geolocation is further
refined by imposing contextual geographic information extracted from
OpenStreetMap. Our pipeline is validated experimentally against the state of
the art approaches for geotagging traffic lights. |
| doi_str_mv | 10.48550/arxiv.2108.06302 |
| format | Article |
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recent years. We propose an approach to improve asset geolocation from street
view imagery by enhancing the quality of the metadata associated with the
images using Structure from Motion. The predicted object geolocation is further
refined by imposing contextual geographic information extracted from
OpenStreetMap. Our pipeline is validated experimentally against the state of
the art approaches for geotagging traffic lights.</description><identifier>DOI: 10.48550/arxiv.2108.06302</identifier><language>eng</language><subject>Computer Science - Learning</subject><creationdate>2021-08</creationdate><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,777,882</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2108.06302$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2108.06302$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Liu, Chao-Jung</creatorcontrib><creatorcontrib>Ulicny, Matej</creatorcontrib><creatorcontrib>Manzke, Michael</creatorcontrib><creatorcontrib>Dahyot, Rozenn</creatorcontrib><title>Context Aware Object Geotagging</title><description>IMVIP 2021 Localization of street objects from images has gained a lot of attention in
recent years. We propose an approach to improve asset geolocation from street
view imagery by enhancing the quality of the metadata associated with the
images using Structure from Motion. The predicted object geolocation is further
refined by imposing contextual geographic information extracted from
OpenStreetMap. Our pipeline is validated experimentally against the state of
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recent years. We propose an approach to improve asset geolocation from street
view imagery by enhancing the quality of the metadata associated with the
images using Structure from Motion. The predicted object geolocation is further
refined by imposing contextual geographic information extracted from
OpenStreetMap. Our pipeline is validated experimentally against the state of
the art approaches for geotagging traffic lights.</abstract><doi>10.48550/arxiv.2108.06302</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Learning |
| title | Context Aware Object Geotagging |
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