Challenges of Crop Classification from Satellite Imagery with Eurocrops Dataset

Abstract

Crops monitoring and classification on a nationwide level provide important information for sustainable agricultural management, food security, and policy-making. Recent technological advancements, followed by Earth observation programmes like Copernicus, have provided plenty of publicly available multispectral data. Combining these data with field annotations allows for continuous crop monitoring from publicly available data. In this paper, we present a solution for crop classification to determine crop type from Sentinel-2 multispectral data, utilizing machine learning techniques. Apart from presenting initial results, we discuss the challenges of crop classification on a Eurocrops dataset and further research directions.