TY - GEN A1 - Scheffler, Daniel A1 - Sips, Mike A1 - Behling, Robert A1 - Dransch, Doris A1 - Eggert, Daniel A1 - Fajerski, Jan A1 - Freytag, Johann-Christoph A1 - Griffiths, Patrick A1 - Hollstein, André A1 - Hostert, Patrick A1 - Köthur, Patrick A1 - Peters, Mathias A1 - Pflugmacher, Dirk A1 - Rabe, Andreas A1 - Reinefeld, Alexander A1 - Schintke, Florian A1 - Segel, Karl T1 - GeoMultiSens – Scalable Multisensoral Analysis of Satellite Remote Sensing Data T2 - ESA Living Planet Symposium, EO Open Science Posters Y1 - 2016 ER - TY - CHAP A1 - Scheffler, Daniel A1 - Sips, Mike A1 - Behling, Robert A1 - Dransch, Doris A1 - Eggert, Daniel A1 - Fajerski, Jan A1 - Freytag, Johann-Christoph A1 - Griffiths, Patrick A1 - Hollstein, André A1 - Hostert, Patrick A1 - Köthur, Patrick A1 - Peters, Mathias A1 - Pflugmacher, Dirk A1 - Rabe, Andreas A1 - Reinefeld, Alexander A1 - Schintke, Florian A1 - Segel, Karl T1 - Geomultisens – a common automatic processing and analysis system for multi-sensor satellite data T2 - Advancing Horizons for Land Cover Services Entering the Big Data Era, Second joint Workshop of the EARSeL Special Interest Group on Land Use & Land Cover and the NASA LCLUC Program Y1 - 2016 SP - 18 EP - 19 ER - TY - JOUR A1 - Weilandt, Frank A1 - Behling, Robert A1 - Goncalves, Romulo A1 - Madadi, Arash A1 - Richter, Lorenz A1 - Sanona, Tiago A1 - Spengler, Daniel A1 - Welsch, Jona T1 - Early Crop Classification via Multi-Modal Satellite Data Fusion and Temporal Attention JF - Remote Sensing N2 - In this article, we propose a deep learning-based algorithm for the classification of crop types from Sentinel-1 and Sentinel-2 time series data which is based on the celebrated transformer architecture. Crucially, we enable our algorithm to do early classification, i.e., predict crop types at arbitrary time points early in the year with a single trained model (progressive intra-season classification). Such early season predictions are of practical relevance for instance for yield forecasts or the modeling of agricultural water balances, therefore being important for the public as well as the private sector. Furthermore, we improve the mechanism of combining different data sources for the prediction task, allowing for both optical and radar data as inputs (multi-modal data fusion) without the need for temporal interpolation. We can demonstrate the effectiveness of our approach on an extensive data set from three federal states of Germany reaching an average F1 score of 0.92 using data of a complete growing season to predict the eight most important crop types and an F1 score above 0.8 when doing early classification at least one month before harvest time. In carefully chosen experiments, we can show that our model generalizes well in time and space. Y1 - 2023 U6 - https://doi.org/10.3390/rs15030799 VL - 15 IS - 3 SP - 799 ER -