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Scalable Evaluation of 3D City Models
- Source :
- IGARSS 2019-IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019-IEEE International Geoscience and Remote Sensing Symposium, Jul 2019, Yokohama, Japan, IGARSS 2019-IEEE International Geoscience and Remote Sensing Symposium, Jul 2019, Yokohama, Japan. ⟨10.1109/IGARSS.2019.8899337⟩, IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, Jul 2019, Yokohama, Japan. pp.3400-3403, ⟨10.1109/IGARSS.2019.8899337⟩, IGARSS
- Publication Year :
- 2019
- Publisher :
- HAL CCSD, 2019.
-
Abstract
- International audience; The generation of 3D building models from Very High Resolution geospatial data is now an automatized procedure. However , urban areas are very complex and practitioners still have to visually assess the correctness of these models and detect reconstruction errors. We proposed an approach for automatically evaluating the quality of 3D building models. It is cast as a supervised classification task based on a hierarchical taxon-omy and multimodal handcrafted features (building geometry, optical images, height data). In this paper, we evaluate how the urban area composition impacts prediction transferability and scalability of our framework to unseen scenes. This allows to define minimal feature and training sets for a problem where no benchmark data has been released so far.
- Subjects :
- Geospatial analysis
010504 meteorology & atmospheric sciences
3D city models
Computer science
Urban scenes
02 engineering and technology
[INFO.INFO-DM]Computer Science [cs]/Discrete Mathematics [cs.DM]
Machine learning
computer.software_genre
Urban area
01 natural sciences
Task (project management)
[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]
Taxonomy (general)
11. Sustainability
0202 electrical engineering, electronic engineering, information engineering
Quality assessments
ComputingMilieux_MISCELLANEOUS
0105 earth and related environmental sciences
Very High Resolution
geography
geography.geographical_feature_category
business.industry
Scalability
[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]
Remote sensing
Classification
[INFO.INFO-DM] Computer Science [cs]/Discrete Mathematics [cs.DM]
Transferability
Geospatial imagery
Feature (computer vision)
[SDE]Environmental Sciences
020201 artificial intelligence & image processing
Artificial intelligence
business
computer
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- IGARSS 2019-IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019-IEEE International Geoscience and Remote Sensing Symposium, Jul 2019, Yokohama, Japan, IGARSS 2019-IEEE International Geoscience and Remote Sensing Symposium, Jul 2019, Yokohama, Japan. ⟨10.1109/IGARSS.2019.8899337⟩, IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, Jul 2019, Yokohama, Japan. pp.3400-3403, ⟨10.1109/IGARSS.2019.8899337⟩, IGARSS
- Accession number :
- edsair.doi.dedup.....3b93a037d33d53fcc59e3428e0853104