End-to-end learned early classification of time series for in-season crop type mapping

• Published in: ISPRS journal of photogrammetry and remote sensing Vol. 196; pp. 445 - 456
• Main Authors: Rußwurm, Marc, Courty, Nicolas, Emonet, Rémi, Lefèvre, Sébastien, Tuia, Devis, Tavenard, Romain
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2023; Elsevier
• Subjects: Africa; Artificial Intelligence; Computer Science; Crop type mapping; data collection; Deep learning; Early classification; early development; Europe; head; In-season crop type mapping; Machine Learning; photogrammetry; prediction; probability; Satellite time series; satellites; Statistics; time series analysis; Europe; Africa; Deep learning; Crop type mapping; Satellite time series; Early classification; In-season crop type mapping

Remote sensing satellites capture the cyclic dynamics of our Planet in regular time intervals recorded in satellite time series data. End-to-end trained deep learning models use this time series data to make predictions at a large scale, for instance, to produce up-to-date crop cover maps. Most time...