Mapping Center Pivot Irrigation Systems in the Southern Amazon from Sentinel-2 Images

International audience Irrigation systems play an important role in agriculture. Center pivot irrigation systemsare popular in many countries as they are labor-saving and water consumption efficient. Monitoringthe distribution of center pivot irrigation systems can provide important information for agriculturalproduction, water consumption and land use. Deep learning has become an effective method forimage classification and object detection. In this paper, a new method to detect the precise shapeof center pivot irrigation systems is proposed. The proposed method combines a lightweight real-time object detection network (PVANET) based on deep learning, an image classification model(GoogLeNet) and accurate shape detection (Hough transform) to detect and accurately delineatecenter pivot irrigation systems and their associated circular shape. PVANET is lightweight and fastand GoogLeNet can reduce the false detections associated with PVANET, while Hough transform canaccurately detect the shape of center pivot irrigation systems. Experiments with Sentinel-2 images inMato Grosso achieved a precision of 95% and a recall of 95.5%, which demonstrated the effectivenessof the proposed method. Finally, with the accurate shape of center pivot irrigation systems detected,the area of irrigation in the region was estimated