| Author: | Wang, Puzuo |
| Title: | Label-efficient geospatial point cloud semantic segmentation |
| Advisors: | Yao, Wei (LSGI) |
| Degree: | Ph.D. |
| Year: | 2024 |
| Subject: | Geospatial data Geospatial data -- Computer processing Machine learning Spatial data mining Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Land Surveying and Geo-Informatics |
| Pages: | xxii, 154 pages : color illustrations |
| Language: | English |
| 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. |
| Rights: | All rights reserved |
| Access: | open access |
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