Early Crop Type Classification with Satellite Imagery - An Empirical Analysis
Lukas Kondmann ⋅ Sebastian Boeck ⋅ Rogerio Bonifacio ⋅ Xiaoxiang Zhu
2022 Poster
in
Workshop: 3rd Workshop on practical ML for Developing Countries: learning under limited/low resource scenarios
in
Workshop: 3rd Workshop on practical ML for Developing Countries: learning under limited/low resource scenarios
Abstract
Crop type mapping from satellite images is an essential input for food security monitoring systems. Many approaches focus on mapping crop types based on a full time series of a growing season. However, a variety of use cases require predictions already during the growing season which can be technically challenging. In this paper, we experiment with Sentinel-2 and Planet Fusion data to explore their potential for early season crop type classification at different points in the season. We use high-quality field collections from Germany and South Africa as reference data and find that daily revisit times can be advantageous but are no silver bullet for early season classification of crops.
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