no code implementations • 4 Mar 2024 • Lei LI, Tianfang Zhang, Zhongyu Jiang, Cheng-Yen Yang, Jenq-Neng Hwang, Stefan Oehmcke, Dimitri Pierre Johannes Gominski, Fabian Gieseke, Christian Igel
We leverage the fusion of three-dimensional LiDAR measurements and 2D imagery to facilitate the accurate counting of trees.
1 code implementation • 18 Jul 2023 • Denis Mayr Lima Martins, Christian Lülf, Fabian Gieseke
Hyperbox-based classification has been seen as a promising technique in which decisions on the data are represented as a series of orthogonal, multidimensional boxes (i. e., hyperboxes) that are often interpretable and human-readable.
no code implementations • 15 Jan 2023 • Lei LI, Tianfang Zhang, Stefan Oehmcke, Fabian Gieseke, Christian Igel
Building segmentation from aerial images and 3D laser scanning (LiDAR) is a challenging task due to the diversity of backgrounds, building textures, and image quality.
no code implementations • 18 Dec 2022 • Tianfang Zhang, Lei LI, Christian Igel, Stefan Oehmcke, Fabian Gieseke, Zhenming Peng
In this work, we propose a DUN called low-rank CS network (LR-CSNet) for natural image CS.
no code implementations • 21 Dec 2021 • Stefan Oehmcke, Lei LI, Katerina Trepekli, Jaime Revenga, Thomas Nord-Larsen, Fabian Gieseke, Christian Igel
Quantification of forest biomass stocks and their dynamics is important for implementing effective climate change mitigation measures.
no code implementations • 1 Nov 2020 • Stefan Oehmcke, Tzu Hsin Karen Chen, Alexander V Prishchepov, Fabian Gieseke
The model uses supplementary data, namely the approximate cloud coverage of input images, the temporal distance to the target time, and a missing data mask for each input time step.
2 code implementations • 29 Sep 2020 • Yimian Dai, Fabian Gieseke, Stefan Oehmcke, Yiquan Wu, Kobus Barnard
Feature fusion, the combination of features from different layers or branches, is an omnipresent part of modern network architectures.
Ranked #654 on Image Classification on ImageNet
1 code implementation • 15 Jul 2020 • Yimian Dai, Stefan Oehmcke, Fabian Gieseke, Yiquan Wu, Kobus Barnard
Inspired by their similarity, we propose a novel type of activation units called attentional activation (ATAC) units as a unification of activation functions and attention mechanisms.
no code implementations • 16 May 2020 • Nikita Moriakov, Ashwin Samudre, Michela Negro, Fabian Gieseke, Sydney Otten, Luc Hendriks
We investigate the use of deep learning in the context of X-ray polarization detection from astrophysical sources as will be observed by the Imaging X-ray Polarimetry Explorer (IXPE), a future NASA selected space-based mission expected to be operative in 2021.
no code implementations • 10 Dec 2019 • Vinnie Ko, Stefan Oehmcke, Fabian Gieseke
One important advantage of our M&U pruning criterion is that it is scale-invariant, a phenomenon that the magnitude-based pruning criterion suffers from.
no code implementations • 4 Dec 2019 • Stefan Oehmcke, Christoffer Thrysøe, Andreas Borgstad, Marcos Antonio Vaz Salles, Martin Brandt, Fabian Gieseke
We evaluate our approaches on a dataset that is based on Sentinel~2 satellite imagery and OpenStreetMap vector data.
no code implementations • 25 Sep 2019 • Stefan Oehmcke, Fabian Gieseke
Both the associated selection masks as well as the neural network are trained simultaneously such that a good model performance is achieved while, at the same time, only a minimal amount of data is selected.
1 code implementation • 11 Jun 2019 • Stefan Oehmcke, Fabian Gieseke
The model as well as the associated selection masks are trained simultaneously such that a good model performance is achieved while only a minimal amount of data is selected.
no code implementations • 27 Mar 2018 • Antonio D'Isanto, Stefano Cavuoti, Fabian Gieseke, Kai Lars Polsterer
The methodology described here is very general and can be used to improve the performance of machine learning models for any regression or classification task.
Instrumentation and Methods for Astrophysics
1 code implementation • 18 Feb 2018 • Fabian Gieseke, Christian Igel
Without access to large compute clusters, building random forests on large datasets is still a challenging problem.
3 code implementations • 19 Sep 2017 • Ashish Mahabal, Kshiteej Sheth, Fabian Gieseke, Akshay Pai, S. George Djorgovski, Andrew Drake, Matthew Graham, the CSS/CRTS/PTF Collaboration
As a result standard time series methods regularly used for financial and similar datasets are of little help and astronomers are usually left to their own instruments and techniques to classify light curves.
no code implementations • 15 Apr 2017 • Jan Kremer, Kristoffer Stensbo-Smidt, Fabian Gieseke, Kim Steenstrup Pedersen, Christian Igel
Astrophysics and cosmology are rich with data.
1 code implementation • 9 Dec 2015 • Fabian Gieseke, Cosmin Eugen Oancea, Ashish Mahabal, Christian Igel, Tom Heskes
A buffer k-d tree is a k-d tree variant for massively-parallel nearest neighbor search.
1 code implementation • 17 Nov 2015 • Kristoffer Stensbo-Smidt, Fabian Gieseke, Christian Igel, Andrew Zirm, Kim Steenstrup Pedersen
This study promotes a feature selection algorithm, which selects the most informative magnitudes and colours for a given task of estimating physical quantities from photometric data alone.