no code implementations • 22 Jan 2024 • Francisco Mena, Deepak Pathak, Hiba Najjar, Cristhian Sanchez, Patrick Helber, Benjamin Bischke, Peter Habelitz, Miro Miranda, Jayanth Siddamsetty, Marlon Nuske, Marcela Charfuelan, Diego Arenas, Michaela Vollmer, Andreas Dengel
The GU module learned different weights based on the country and crop-type, aligning with the variable significance of each data source to the prediction task.
no code implementations • 17 Aug 2023 • Deepak Pathak, Miro Miranda, Francisco Mena, Cristhian Sanchez, Patrick Helber, Benjamin Bischke, Peter Habelitz, Hiba Najjar, Jayanth Siddamsetty, Diego Arenas, Michaela Vollmer, Marcela Charfuelan, Marlon Nuske, Andreas Dengel
We introduce a simple yet effective early fusion method for crop yield prediction that handles multiple input modalities with different temporal and spatial resolutions.
no code implementations • 26 Oct 2022 • Priyash Bhugra, Benjamin Bischke, Christoph Werner, Robert Syrnicki, Carolin Packbier, Patrick Helber, Caglar Senaras, Akhil Singh Rana, Tim Davis, Wanda De Keersmaecker, Daniele Zanaga, Annett Wania, Ruben Van De Kerchove, Giovanni Marchisio
The results demonstrate an improvement of approximately 0. 89% in classifying satellite imagery on 15 classes using a multi-temporal approach on monthly time series images compared to the mono-temporal approach.
no code implementations • 5 Oct 2021 • Giovanni Marchisio, Patrick Helber, Benjamin Bischke, Timothy Davis, Caglar Senaras, Daniele Zanaga, Ruben Van De Kerchove, Annett Wania
Under the sponsorship of the European Union Horizon 2020 program, RapidAI4EO will establish the foundations for the next generation of Copernicus Land Monitoring Service (CLMS) products.
1 code implementation • 25 Apr 2020 • Fatemeh Azimi, Benjamin Bischke, Sebastian Palacio, Federico Raue, Joern Hees, Andreas Dengel
Video Object Segmentation (VOS) is an active research area of the visual domain.
1 code implementation • 5 Dec 2018 • Tim G. J. Rudner, Marc Rußwurm, Jakub Fil, Ramona Pelich, Benjamin Bischke, Veronika Kopackova, Piotr Bilinski
We propose a novel approach for rapid segmentation of flooded buildings by fusing multiresolution, multisensor, and multitemporal satellite imagery in a convolutional neural network.
no code implementations • 9 Aug 2018 • Benjamin Bischke, Patrick Helber, Florian König, Damian Borth, Andreas Dengel
This assumption limits the applications of multi-modal models since in practice the data collection process is likely to generate data with missing, incomplete or corrupted modalities.
1 code implementation • 18 Sep 2017 • Benjamin Bischke, Patrick Helber, Joachim Folz, Damian Borth, Andreas Dengel
In this paper, we address the problem of preserving semantic segmentation boundaries in high resolution satellite imagery by introducing a new cascaded multi-task loss.
8 code implementations • 31 Aug 2017 • Patrick Helber, Benjamin Bischke, Andreas Dengel, Damian Borth
We present a novel dataset based on Sentinel-2 satellite images covering 13 spectral bands and consisting out of 10 classes with in total 27, 000 labeled and geo-referenced images.