A PERFORMANCE COMPARISON BETWEEN SEGNET AND DEEPLABV3+ ON THE SEMANTIC SEGMENTATION OF HERITAGE BUILDINGS
Abstract. During the last decade, the use of machine and deep learning tools to support 3D semantic segmentation of point clouds remarkably increased and their impressive results have led to the application of such methods to the semantic modeling of heritage buildings. Nevertheless, a standard procedure to deal with such problem is still missing, and several significant challenges, caused by the complexity of heritage building scenario, have still to be faced. This paper aims at comparing the overall performance of two convolutional neural network architectures, named SegNet and Deeplabv3+, for the semantic segmentation of heritage point clouds throughout a multiview approach. More specifically, the two architectures have been tested to obtain 2D segmentation maps of the related photogrammetric images of the buildings, and then the output maps have been projected to the photogrammetric point cloud by means of the interior and exterior camera parameters. Experiments to test the effectiveness of the proposed approach have been conducted on the case study of Spedale del Ceppo in Pistoia, Italy. Despite the results shown a remarkable performance of both the architectures, Deeplabv3+ outperformed SegNet in terms of accuracy, memory consumption and training time.
- 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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A PERFORMANCE COMPARISON BETWEEN SEGNET AND DEEPLABV3+ ON THE SEMANTIC SEGMENTATION OF HERITAGE BUILDINGS ; volume:XLVIII-1/W1-2023 ; year:2023 ; pages:379-386 ; extent:8
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; XLVIII-1/W1-2023 (2023), 379-386 (gesamt 8)
- Creator
- DOI
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10.5194/isprs-archives-XLVIII-1-W1-2023-379-2023
- URN
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urn:nbn:de:101:1-2023060104231404597737
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 10:59 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Pellis, E.
- Masiero, A.
- Cortesi, I.
- Tucci, G.
- Betti, M.
- Grussenmeyer, Pierre
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SYNTHETIC DATA GENERATION AND TESTING FOR THE SEMANTIC SEGMENTATION OF HERITAGE BUILDINGS
AUTOMATIC TRAINING DATA GENERATION IN DEEP LEARNING-AIDED SEMANTIC SEGMENTATION OF HERITAGE BUILDINGS
A BENCHMARK FOR LARGE-SCALE HERITAGE POINT CLOUD SEMANTIC SEGMENTATION
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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
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Sfnet: Faster and Accurate Semantic Segmentation Via Semantic Flow
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SYNTHETIC DATA GENERATION AND TESTING FOR THE SEMANTIC SEGMENTATION OF HERITAGE BUILDINGS
AUTOMATIC TRAINING DATA GENERATION IN DEEP LEARNING-AIDED SEMANTIC SEGMENTATION OF HERITAGE BUILDINGS
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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Semantic segmentation of textured mosaics
Sfnet: Faster and Accurate Semantic Segmentation Via Semantic Flow