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Maneuver-based Trajectory Prediction for Self-driving Cars Using Spatio-temporal Convolutional Networks
Benedikt Mersch, Thomas Höllen, Kun Zhao +2
The ability to predict the future movements of other vehicles is a subconscious and effortless skill for humans and key to safe autonomous driving. Therefore, trajectory prediction…
A Survey of Uncertainty in Deep Neural Networks
Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali +11
Due to their increasing spread, confidence in neural network predictions became more and more important. However, basic neural networks do not deliver certainty estimates or suffer…
Behind the leaves -- Estimation of occluded grapevine berries with conditional generative adversarial networks
Jana Kierdorf, Immanuel Weber, Anna Kicherer +3
The need for accurate yield estimates for viticulture is becoming more important due to increasing competition in the wine market worldwide. One of the most promising methods to es…
Temporal Prediction and Evaluation of Brassica Growth in the Field using Conditional Generative Adversarial Networks
Lukas Drees, Laura Verena Junker-Frohn, Jana Kierdorf +1
Farmers frequently assess plant growth and performance as basis for making decisions when to take action in the field, such as fertilization, weed control, or harvesting. The predi…
Towards a Collective Agenda on AI for Earth Science Data Analysis
Devis Tuia, Ribana Roscher, Jan Dirk Wegner +3
In the last years we have witnessed the fields of geosciences and remote sensing and artificial intelligence to become closer. Thanks to both the massive availability of observatio…
Artificial and beneficial -- Exploiting artificial images for aerial vehicle detection
Immanuel Weber, Jens Bongartz, Ribana Roscher
Object detection in aerial images is an important task in environmental, economic, and infrastructure-related tasks. One of the most prominent applications is the detection of vehi…