@misc{SchefflerSipsBehlingetal., author = {Scheffler, Daniel and Sips, Mike and Behling, Robert and Dransch, Doris and Eggert, Daniel and Fajerski, Jan and Freytag, Johann-Christoph and Griffiths, Patrick and Hollstein, Andr{\´e} and Hostert, Patrick and K{\"o}thur, Patrick and Peters, Mathias and Pflugmacher, Dirk and Rabe, Andreas and Reinefeld, Alexander and Schintke, Florian and Segel, Karl}, title = {GeoMultiSens - Scalable Multisensoral Analysis of Satellite Remote Sensing Data}, series = {ESA Living Planet Symposium, EO Open Science Posters}, journal = {ESA Living Planet Symposium, EO Open Science Posters}, language = {en} } @inproceedings{SchefflerSipsBehlingetal., author = {Scheffler, Daniel and Sips, Mike and Behling, Robert and Dransch, Doris and Eggert, Daniel and Fajerski, Jan and Freytag, Johann-Christoph and Griffiths, Patrick and Hollstein, Andr{\´e} and Hostert, Patrick and K{\"o}thur, Patrick and Peters, Mathias and Pflugmacher, Dirk and Rabe, Andreas and Reinefeld, Alexander and Schintke, Florian and Segel, Karl}, title = {Geomultisens - a common automatic processing and analysis system for multi-sensor satellite data}, series = {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}, booktitle = {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}, pages = {18 -- 19}, language = {en} } @article{WeilandtBehlingGoncalvesetal., author = {Weilandt, Frank and Behling, Robert and Goncalves, Romulo and Madadi, Arash and Richter, Lorenz and Sanona, Tiago and Spengler, Daniel and Welsch, Jona}, title = {Early Crop Classification via Multi-Modal Satellite Data Fusion and Temporal Attention}, series = {Remote Sensing}, volume = {15}, journal = {Remote Sensing}, number = {3}, doi = {10.3390/rs15030799}, pages = {799}, abstract = {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.}, language = {en} }