CLASSIFICATION OF AERIAL POINT CLOUDS WITH DEEP LEARNING
Due to their usefulness in various implementations, such as energy evaluation, visibility analysis, emergency response, 3D cadastre, urban planning, change detection, navigation, etc., 3D city models have gained importance over the last decades. Point clouds are one of the primary data sources for t...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2019-06-01
|
| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W13/103/2019/isprs-archives-XLII-2-W13-103-2019.pdf |
| _version_ | 1828412364080283648 |
|---|---|
| author | E. Özdemir E. Özdemir F. Remondino |
| author_facet | E. Özdemir E. Özdemir F. Remondino |
| author_sort | E. Özdemir |
| collection | DOAJ |
| description | Due to their usefulness in various implementations, such as energy evaluation, visibility analysis, emergency response, 3D cadastre, urban planning, change detection, navigation, etc., 3D city models have gained importance over the last decades. Point clouds are one of the primary data sources for the generation of realistic city models. Beside model-driven approaches, 3D building models can be directly produced from classified aerial point clouds. This paper presents an ongoing research for 3D building reconstruction based on the classification of aerial point clouds without given ancillary data (e.g. footprints, etc.). The work includes a deep learning approach based on specific geometric features extracted from the point cloud. The methodology was tested on the ISPRS 3D Semantic Labeling Contest (Vaihingen and Toronto point clouds) showing promising results, although partly affected by the low density and lack of points on the building facades for the available clouds. |
| first_indexed | 2024-12-10T12:47:51Z |
| format | Article |
| id | doaj.art-42c806f5c5344a9db05a72a7b2cad3c7 |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-12-10T12:47:51Z |
| publishDate | 2019-06-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-42c806f5c5344a9db05a72a7b2cad3c72022-12-22T01:48:21ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342019-06-01XLII-2-W1310311010.5194/isprs-archives-XLII-2-W13-103-2019CLASSIFICATION OF AERIAL POINT CLOUDS WITH DEEP LEARNINGE. Özdemir0E. Özdemir1F. Remondino2Space Center, Skolkovo Institute of Technology (SKOLTECH), Moscow, Russia3D Optical Metrology (3DOM) unit, Bruno Kessler Foundation (FBK), Trento, Italy3D Optical Metrology (3DOM) unit, Bruno Kessler Foundation (FBK), Trento, ItalyDue to their usefulness in various implementations, such as energy evaluation, visibility analysis, emergency response, 3D cadastre, urban planning, change detection, navigation, etc., 3D city models have gained importance over the last decades. Point clouds are one of the primary data sources for the generation of realistic city models. Beside model-driven approaches, 3D building models can be directly produced from classified aerial point clouds. This paper presents an ongoing research for 3D building reconstruction based on the classification of aerial point clouds without given ancillary data (e.g. footprints, etc.). The work includes a deep learning approach based on specific geometric features extracted from the point cloud. The methodology was tested on the ISPRS 3D Semantic Labeling Contest (Vaihingen and Toronto point clouds) showing promising results, although partly affected by the low density and lack of points on the building facades for the available clouds.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W13/103/2019/isprs-archives-XLII-2-W13-103-2019.pdf |
| spellingShingle | E. Özdemir E. Özdemir F. Remondino CLASSIFICATION OF AERIAL POINT CLOUDS WITH DEEP LEARNING The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | CLASSIFICATION OF AERIAL POINT CLOUDS WITH DEEP LEARNING |
| title_full | CLASSIFICATION OF AERIAL POINT CLOUDS WITH DEEP LEARNING |
| title_fullStr | CLASSIFICATION OF AERIAL POINT CLOUDS WITH DEEP LEARNING |
| title_full_unstemmed | CLASSIFICATION OF AERIAL POINT CLOUDS WITH DEEP LEARNING |
| title_short | CLASSIFICATION OF AERIAL POINT CLOUDS WITH DEEP LEARNING |
| title_sort | classification of aerial point clouds with deep learning |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W13/103/2019/isprs-archives-XLII-2-W13-103-2019.pdf |
| work_keys_str_mv | AT eozdemir classificationofaerialpointcloudswithdeeplearning AT eozdemir classificationofaerialpointcloudswithdeeplearning AT fremondino classificationofaerialpointcloudswithdeeplearning |