Journal of Korean Society of Industrial and Systems Engineering (산업경영시스템학회지)

Society of Korea Industrial and System Engineering (한국산업경영시스템학회)
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Development of Deep Learning-based Land Monitoring Web Service

딥러닝 기반의 국토모니터링 웹 서비스 개발

  • In-Hak Kong (LX corp. Spatial Information Research Institute, Department of spatial Information Engineering, Pukyong National University) ;
  • Dong-Hoon Jeong (LX corp. Spatial Information Research Institute) ;
  • Gu-Ha Jeong (LX corp. Spatial Information Research Institute)
  • 공인학 (LX 공간정보연구원, 부경대학교 지구환경시스템과학부 공간정보공학 전공) ;
  • 정동훈 (LX 공간정보연구원) ;
  • 정구하 (LX 공간정보연구원)
  • Received : 2023.08.18
  • Accepted : 2023.09.19
  • Published : 2023.09.30
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Abstract

Land monitoring involves systematically understanding changes in land use, leveraging spatial information such as satellite imagery and aerial photographs. Recently, the integration of deep learning technologies, notably object detection and semantic segmentation, into land monitoring has spurred active research. This study developed a web service to facilitate such integrations, allowing users to analyze aerial and drone images using CNN models. The web service architecture comprises AI, WEB/WAS, and DB servers and employs three primary deep learning models: DeepLab V3, YOLO, and Rotated Mask R-CNN. Specifically, YOLO offers rapid detection capabilities, Rotated Mask R-CNN excels in detecting rotated objects, while DeepLab V3 provides pixel-wise image classification. The performance of these models fluctuates depending on the quantity and quality of the training data. Anticipated to be integrated into the LX Corporation's operational network and the Land-XI system, this service is expected to enhance the accuracy and efficiency of land monitoring.

Keywords

References

  1. AI hub, https://www.aihub.or.kr/.
  2. Chen, L.C., Papandreou, G., Schroff, F., and Adam, H., Rethinking Atrous Convolution for Semantic Image Segmentation, arXiv Pre-print, 2017, arXiv:1706.05587
  3. Ha, J., Park, K., and Kim, M., Development of Road Crack Detection System Using Deep Learning-based Segmentation and Object Detection, Journal of Society for e-Business Studies, 2021, Vol. 26, No. 1, pp. 93-106.
  4. Jeon, J.B., Yoo, E.J., Jeong, G.H., and Kim, B.E., A Study on the Implementation Plan of National Land using Survey for LX Future Business Model Discovery, LX Spatial Information Research Institute, 2020
  5. Jo, W. and Park, K. H., Deep learning based Land Cover Change Detection Using U-Net, Journal of the Korean Geographical Society, 2022, Vol. 57, No. 3, pp. 297-306
  6. Jo, W. and Park, K.H., Deep learning based Land Cover Change Detection Using U-Net, Journal of the Korean Geographical Society, 2022, Vol. 57, No. 3, pp. 297-306.
  7. Kornienko, D. V., Mishina, S. V., Shcherbatykh, S. V., and Melnikov, M. O., Principles of Securing RESTful API Web Services Developed with Python Frameworks, Journal of Physics: Conference Series, Vol. 2094, No. 3, IOP Publishing, 2021, p. 032016.
  8. Lee, J.H., Ji, Y.S., and Kang, J.H., A Study on Monitoring for Restricted Development Zone, LX Spatial Information Research Institute, 2020
  9. Mo, J.S., Seong, S.K., and Choi, J.W., Comparative evaluation of deep learning-based building extraction techniques using aerial images, Journal of the Korean Society of Surveying, 2021, Vol. 39, No. 3, pp. 157-165.
  10. Pack, H.J., A Study on Implementation of Web-based Machine Learning Platform and Applications[dissertation], [Deajeon, Korea]: Pai Chai University, 2020.
  11. Park, S., Park, Y., Jang, S., and Kim, T. H., A Study on Evaluating the Possibility of Monitoring Ships of CAS500-1 Images Based on YOLO Algorithm: A Case Study of a Busan New Port and an Oakland Port in California, Korean Journal of Remote Sensing, 2022, Vol. 38, No. 6-1, pp. 1463~1478 https://doi.org/10.7780/KJRS.2022.38.6.1.35
  12. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A., You Only Look Once: Unified, Real-Time Object Detection, arXiv Pre-print, 2015, arXiv:1506.02640
  13. Wang, P., Hu, W., Zhang, J., Wen, Y., Xu, C., and Qian, D., Enhanced Rotated Mask R-CNN for Chromosome Segmentation, 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2021, Mexico, pp. 2769-2772
  14. Yoo, J.S. and Lee, M.H., Design of Enterprise System Platform based on Spring Data JPA in Spring Framework Environment, Journal of Convergence for Information Technology. Convergence Society for SMB, 2019, Vol. 9, No. 12, pp. 39-46.
  15. Zhang, F., Sun, G., Zheng, B., and Dong, L., Design and Implementation of Energy Management System Based on Spring Boot Framework, Information, 2021, Vol. 12, No. 11, p. 457.