Cloud Approach to Automated Crop Classification Using Sentinel-1 Imagery

For accurate crop classification, it is necessary to use time-series of high-resolution satellite data to better discriminate among certain crop types. This task brings the following challenges: a large amount of satellite data for download, Big data processing and computational resources for utilization of state-of-the-art classification approaches. For solving these problems, we have developed an automated crop classification workflow, which is based on machine-learning techniques. By deployment of the workflow on the cloud platform, we can overcome challenges of Big data downloading and processing. In this paper, we present the system architecture and describe the experiments on structural and parametric identification of machine learning models utilized in the system.