AI Opportunities and Challenges for Crop Type Mapping Using Sentinel-2 and Drone Data

Crop type mapping represents one of the most challenging problems in remote sensing. Spatial, spectral, and temporal information are required in order to obtain a unambiguous distinction among the types of crop. This paper presents a multi-sensor approach, where labelled high-resolution images from drones, limited to small areas, are used to enhance the classification ability of machine learning models based on Sentinel 2 time series. The project described in this paper is organized into three major activities. The first part focused on the exploitation of RGB drone images by using transfer learning and convolutional networks, and it has already been described in a previous work by the team. The second part deals with preliminary analysis of multi-spectral Sentinel 2 time-series using the labelled data from the drones campaign and trees-based machine learning algorithms. Finally, the third ongoing part deals with the combination of drones and satellite data in order to show how drones data can help the Sentinel 2 classification by reducing the effort needed to collect reference crop type information.