딥러닝 기반의 국토모니터링 웹 서비스 개발

• 영문명: Development of Deep Learning-based Land Monitoring Web Service
• 발행기관: 한국산업경영시스템학회
• 저자명: 공인학 정동훈 정구하
• 간행물 정보: 『산업경영시스템학회지』제46권 제3호, 275~284쪽, 전체 10쪽
• 주제분류: 공학 > 산업공학
• 파일형태: PDF
• 발행일자: 2023.09.30

영문 초록

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.

목차

1. 서 론

2. 웹서비스의 구성

3. 국토 모니터링을 위한 딥러닝 모델

4. 시스템 구성

5. 결 론

References

키워드

Land monitoring Deep learning Object detection Web service Spatial information