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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2023.10
- 수록면
- 367 - 382 (16page)
- DOI
- 10.7848/ksgpc.2023.41.5.367
이용수
초록· 키워드
오류제보하기
People spend most of their time indoors. Therefore, indoor spatial information is vital for leading a safe and high-quality life. Nowadays, as digital twins and smart cities become popular, various fields are trying to build indoor spatial information to simulate disaster or terrorist situations in advance and find countermeasures or to provide efficient routes and query services to users. There are various methods for visually modeling indoor spaces, but recently, modeling using image data that are efficient in economic feasibility and usability has been increasing interest. However, since image data does not contain geometric or semantic information, there are limitations in providing image-based indoor spatial information services. In order to provide services such as routing, spatial or non-spatial queries, and object identification, these services must use topology data in conjunction with image data. Multiple studies have proposed methodologies to link image and topology data, but existing studies have limitations in requiring external reference data or too much manual work. This study aims to overcome the limitations of previous studies and effectively construct indoor spatial information by establishing relationships between image data and generating network-based topology data from the image dataset. Accordingly, we present a series of methodologies to automatically create an NRS (Node-Relation Structure) dataset using an omnidirectional image dataset. Specifically, we present methods to acquire an image dataset, detect object space, and generate nodes and edges using image and object space data. The final output of this study is NRS data consisting of nodes and edges.
목차
Abstract
1. Introduction
2. Related Works
3. Methodology
4. Implementation
5. Conclusion
References
참고문헌 (30)
참고문헌 신청
Ahn, D., Claridades, A.R.C., and Lee, J. (2020), Integrating image and network-based topological data through spatial data fusion for indoor location-based services, Journal of Sensors, Vol. 2020, Article ID 8877739, 12pages.https://doi.org/10.1155/2020/8
Becker, T., Nagel, C., and Kolbe, T.H. (2009), A multilayered space-event model for navigation in indoor spaces, In: Lee, J., Zlatanova, S. (eds.), 3D Geo-Information Sciences, Springer, Berlin, Heidelberg, ch. 5, pp.61-77.
Bochkovskiy, A., Wang, C., and Liao, H.M. (2020), YOLOv4: Optimal speed and accuracy of object detection, arXiv:2004.10934v1.
Claridades, A.R.C., Kim, M., and Lee, J. (2023), Developing a model to express spatial relationships on omnidirectional images for indoor space representation to provide location-based services, ISPRS International Journal of Geo-Information, Vol. 12, No.
Claridades, A.R.C., Lee, J., and Blanco, A. (2018), Using omnidirectional images for semi-automatically generating IndoorGML data, Journal of Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 36, No. 5, pp. 319-333.
Becker, T., Nagel, C., and Kolbe, T.H. (2009), A multilayered space-event model for navigation in indoor spaces, In: Lee, J., Zlatanova, S. (eds.), 3D Geo-Information Sciences, Springer, Berlin, Heidelberg, ch. 5, pp.61-77.
Bochkovskiy, A., Wang, C., and Liao, H.M. (2020), YOLOv4: Optimal speed and accuracy of object detection, arXiv:2004.10934v1.
Claridades, A.R.C., Kim, M., and Lee, J. (2023), Developing a model to express spatial relationships on omnidirectional images for indoor space representation to provide location-based services, ISPRS International Journal of Geo-Information, Vol. 12, No.
Claridades, A.R.C., Lee, J., and Blanco, A. (2018), Using omnidirectional images for semi-automatically generating IndoorGML data, Journal of Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 36, No. 5, pp. 319-333.
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