Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Land Surveying and Geo-Informatics | en_US |
| dc.contributor.advisor | Yao, Wei (LSGI) | en_US |
| dc.creator | Wang, Puzuo | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/13417 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Label-efficient geospatial point cloud semantic segmentation | en_US |
| dcterms.abstract | Recent advancements in point cloud semantic segmentation have consistently surpassed previous state-of-the-art approaches. Nonetheless, the effectiveness of these models is heavily contingent upon the availability of extensive labeled data. The process of annotating large-scale geospatial point clouds, particularly those encompassing multiple classes in urban environments, is exceptionally time-consuming and labor-intensive. This reliance on vast annotated datasets to achieve leading performance significantly hinders the practical applicability of large-scale point cloud semantic segmentation. Consequently, attaining promising results while substantially minimizing labeling efforts is a crucial objective. | en_US |
| dcterms.extent | xxii, 154 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2024 | en_US |
| dcterms.educationalLevel | Ph.D. | en_US |
| dcterms.educationalLevel | All Doctorate | en_US |
| dcterms.LCSH | Geospatial data | en_US |
| dcterms.LCSH | Geospatial data -- Computer processing | en_US |
| dcterms.LCSH | Machine learning | en_US |
| dcterms.LCSH | Spatial data mining | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | open access | en_US |
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