AUTOMATIC TRAINING DATA GENERATION IN DEEP LEARNING-AIDED SEMANTIC SEGMENTATION OF HERITAGE BUILDINGS
Abstract. In the geomatics domain the use of deep learning, a subset of machine learning, is becoming more and more widespread. In this context, the 3D semantic segmentation of heritage point clouds presents an interesting and promising approach for modelling automation, in light of the heterogeneous nature of historical building styles and features. However, this heterogeneity also presents an obstacle in terms of generating the training data for use in deep learning, hitherto performed largely manually. The current generally low availability of labelled data also presents a motivation to aid the process of training data generation. In this paper, we propose the use of approaches based on geometric rules to automate to a certain degree this task. One object class will be discussed in this paper, namely the pillars class. Results show that the approach managed to extract pillars with satisfactory quality (98.5% of correctly detected pillars with the proposed algorithm). Tests were also performed to use the outputs in a deep learning segmentation setting, with a favourable outcome in terms of reducing the overall labelling time (−66.5%). Certain particularities were nevertheless observed, which also influence the result of the deep learning segmentation.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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AUTOMATIC TRAINING DATA GENERATION IN DEEP LEARNING-AIDED SEMANTIC SEGMENTATION OF HERITAGE BUILDINGS ; volume:V-2-2022 ; year:2022 ; pages:317-324 ; extent:8
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; V-2-2022 (2022), 317-324 (gesamt 8)
- Creator
- DOI
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10.5194/isprs-annals-V-2-2022-317-2022
- URN
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urn:nbn:de:101:1-2022051905305945776746
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:30 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Murtiyoso, A.
- Matrone, F.
- Martini, M.
- Lingua, A.
- Grussenmeyer, Pierre
- Pierdicca, R.
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A BENCHMARK FOR LARGE-SCALE HERITAGE POINT CLOUD SEMANTIC SEGMENTATION
Semantic segmentation of point clouds of ancient buildings based on weak supervision
ASSEMBLING AN IMAGE AND POINT CLOUD DATASET FOR HERITAGE BUILDING SEMANTIC SEGMENTATION
AN IMAGE-BASED DEEP LEARNING WORKFLOW FOR 3D HERITAGE POINT CLOUD SEMANTIC SEGMENTATION
GRAPH CNN WITH RADIUS DISTANCE FOR SEMANTIC SEGMENTATION OF HISTORICAL BUILDINGS TLS POINT CLOUDS
2D TO 3D LABEL PROPAGATION FOR THE SEMANTIC SEGMENTATION OF HERITAGE BUILDING POINT CLOUDS
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Sfnet: Faster and Accurate Semantic Segmentation Via Semantic Flow
SYNTHETIC DATA GENERATION AND TESTING FOR THE SEMANTIC SEGMENTATION OF HERITAGE BUILDINGS
A PERFORMANCE COMPARISON BETWEEN SEGNET AND DEEPLABV3+ ON THE SEMANTIC SEGMENTATION OF HERITAGE BUILDINGS
Pseudo-labelling-aided semantic segmentation on sparsely annotated 3D point clouds
A BENCHMARK FOR LARGE-SCALE HERITAGE POINT CLOUD SEMANTIC SEGMENTATION
Semantic segmentation of point clouds of ancient buildings based on weak supervision
ASSEMBLING AN IMAGE AND POINT CLOUD DATASET FOR HERITAGE BUILDING SEMANTIC SEGMENTATION
AN IMAGE-BASED DEEP LEARNING WORKFLOW FOR 3D HERITAGE POINT CLOUD SEMANTIC SEGMENTATION
GRAPH CNN WITH RADIUS DISTANCE FOR SEMANTIC SEGMENTATION OF HISTORICAL BUILDINGS TLS POINT CLOUDS
2D TO 3D LABEL PROPAGATION FOR THE SEMANTIC SEGMENTATION OF HERITAGE BUILDING POINT CLOUDS
Semantic Segmentation of Ambiguous Images
Semantic segmentation of textured mosaics