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.

1. 서 론
2. 웹서비스의 구성
    2.1. AI 서버
    2.2 WEB/WAS 서버
    2.3 DB 서버
    2.4 내·외부망 연계
3. 국토 모니터링을 위한 딥러닝 모델
    3.1 YOLO(You Only Look Once)
    3.2 Rotated Mask R-CNN
    3.3 DeepLab V3
    3.4 학습자료 및 모델 성능 평가
4. 시스템 구성
    4.1 시스템 아키텍처 설계
    4.2 AI 기반 서비스 구현 방법
5. 결 론

• In-Hak Kong(LX corp. Spatial Information Research Institute, Department of spatial Information Engineering, Pukyong National University) | 공인학 (LX 공간정보연구원, 부경대학교 지구환경시스템과학부 공간정보공학 전공)
• Dong-Hoon Jeong(LX corp. Spatial Information Research Institute) | 정동훈 (LX 공간정보연구원)
• Gu-Ha Jeong(LX corp. Spatial Information Research Institute) | 정구하 (LX 공간정보연구원) Corresponding author