This study developed an agricultural parcel detection model based on the YOLO-Seg algorithm using high-resolution satellite imagery of CAS500-1 (Compact Advanced Satellite 500-1). We analyzed the effects of grid overlap and confidence threshold settings on object detection accuracy. The main results showed that a 60% grid overlap achieved optimal performance with 70% detection accuracy and about 5% false detection rate within one minute of processing time. By analyzing ratios of overlapping from 10% to 90%, the detection accuracy improved, but the processing time significantly increased, requiring a balance between accuracy and efficiency. The proposed method of spatial continuity correction using polygon centroids improved detection accuracy by approximately 5%p while significantly reducing false detection rates. The system demonstrated 80% detection accuracy when the confidence threshold was set between 0.5 and 0.7, which outperformed traditional detection methods. Additionally, our approach addressed the boundary detection issues in split grid analysis through an optimized overlap strategy. This automated detection system can be effectively applied to agricultural monitoring, resource management, and policy development, providing a cost-effective solution for large-scale agricultural parcel monitoring.



Keywords

YOLO; segmentation; satellite image; tiling move; image overlap