100 citations · 390 across the 21 of their papers we have counts for
24 papers
Tree Counting by Bridging 3D Point Clouds with Imagery
Lei Li, Tianfang Zhang, Zhongyu Jiang +6
Accurate and consistent methods for counting trees based on remote sensing data are needed to support sustainable forest management, assess climate change mitigation strategies, an…
End-to-End Neural Network Training for Hyperbox-Based Classification
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., hyperbo…
Fast Search-By-Classification for Large-Scale Databases Using Index-Aware Decision Trees and Random Forests
Christian Lülf, Denis Mayr Lima Martins, Marcos Antonio Vaz Salles +2
The vast amounts of data collected in various domains pose great challenges to modern data exploration and analysis. To find "interesting" objects in large databases, users typical…
BuildSeg: A General Framework for the Segmentation of Buildings
Lei Li, Tianfang Zhang, Stefan Oehmcke +2
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. While curren…
LR-CSNet: Low-Rank Deep Unfolding Network for Image Compressive Sensing
Tianfang Zhang, Lei Li, Christian Igel +3
Deep unfolding networks (DUNs) have proven to be a viable approach to compressive sensing (CS). In this work, we propose a DUN called low-rank CS network (LR-CSNet) for natural ima…
Deep Learning Based 3D Point Cloud Regression for Estimating Forest Biomass
Stefan Oehmcke, Lei Li, Katerina Trepekli +4
Quantification of forest biomass stocks and their dynamics is important for implementing effective climate change mitigation measures. The knowledge is needed, e.g., for local fore…
Attentional Feature Fusion
Yimian Dai, Fabian Gieseke, Stefan Oehmcke +2
Feature fusion, the combination of features from different layers or branches, is an omnipresent part of modern network architectures. It is often implemented via simple operations…
Inferring astrophysical X-ray polarization with deep learning
Nikita Moriakov, Ashwin Samudre, Michela Negro +3
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 (IXP…
Magnitude and Uncertainty Pruning Criterion for Neural Networks
Vinnie Ko, Stefan Oehmcke, Fabian Gieseke
Neural networks have achieved dramatic improvements in recent years and depict the state-of-the-art methods for many real-world tasks nowadays. One drawback is, however, that many…
Detecting Hardly Visible Roads in Low-Resolution Satellite Time Series Data
Stefan Oehmcke, Christoffer Thrysøe, Andreas Borgstad +3
Massive amounts of satellite data have been gathered over time, holding the potential to unveil a spatiotemporal chronicle of the surface of Earth. These data allow scientists to i…
Input Selection for Bandwidth-Limited Neural Network Inference
Stefan Oehmcke, Fabian Gieseke
Data are often accommodated on centralized storage servers. This is the case, for instance, in remote sensing and astronomy, where projects produce several petabytes of data every…
Massively-Parallel Break Detection for Satellite Data
Malte von Mehren, Fabian Gieseke, Jan Verbesselt +3
The field of remote sensing is nowadays faced with huge amounts of data. While this offers a variety of exciting research opportunities, it also yields significant challenges regar…
Return of the features. Efficient feature selection and interpretation for photometric redshifts
Antonio D'Isanto, Stefano Cavuoti, Fabian Gieseke +1
The explosion of data in recent years has generated an increasing need for new analysis techniques in order to extract knowledge from massive datasets. Machine learning has proved…
Training Big Random Forests with Little Resources
Fabian Gieseke, Christian Igel
Without access to large compute clusters, building random forests on large datasets is still a challenging problem. This is, in particular, the case if fully-grown trees are desire…
Deep-Learnt Classification of Light Curves
Ashish Mahabal, Kshiteej Sheth, Fabian Gieseke +5
Astronomy light curves are sparse, gappy, and heteroscedastic. As a result standard time series methods regularly used for financial and similar datasets are of little help and ast…
Convolutional Neural Networks for Transient Candidate Vetting in Large-Scale Surveys
Fabian Gieseke, Steven Bloemen, Cas van den Bogaard +9
Current synoptic sky surveys monitor large areas of the sky to find variable and transient astronomical sources. As the number of detections per night at a single telescope easily…
Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy
Jan Kremer, Kristoffer Stensbo-Smidt, Fabian Gieseke +2
Astrophysics and cosmology are rich with data. The advent of wide-area digital cameras on large aperture telescopes has led to ever more ambitious surveys of the sky. Data volumes…
A probabilistic approach to emission-line galaxy classification
R. S. de Souza, M. L. L. Dantas, M. V. Costa-Duarte +7
We invoke a Gaussian mixture model (GMM) to jointly analyse two traditional emission-line classification schemes of galaxy ionization sources: the Baldwin-Phillips-Terlevich (BPT)…
Uncertain Photometric Redshifts
Kai Lars Polsterer, Antonio D'Isanto, Fabian Gieseke
Photometric redshifts play an important role as a measure of distance for various cosmological topics. Spectroscopic redshifts are only available for a very limited number of objec…
Bigger Buffer k-d Trees on Multi-Many-Core Systems
Fabian Gieseke, Cosmin Eugen Oancea, Ashish Mahabal +2
A buffer k-d tree is a k-d tree variant for massively-parallel nearest neighbor search. While providing valuable speed-ups on modern many-core devices in case both a large number o…
Exploring the spectroscopic diversity of type Ia supernovae with DRACULA: a machine learning approach
Michele Sasdelli, E. E. O. Ishida, R. Vilalta +8
The existence of multiple subclasses of type Ia supernovae (SNeIa) has been the subject of great debate in the last decade. One major challenge inevitably met when trying to infer…
Sacrificing information for the greater good: how to select photometric bands for optimal accuracy
Kristoffer Stensbo-Smidt, Fabian Gieseke, Christian Igel +2
Large-scale surveys make huge amounts of photometric data available. Because of the sheer amount of objects, spectral data cannot be obtained for all of them. Therefore it is impor…
Finding New High-Redshift Quasars by Asking the Neighbours
Kai Lars Polsterer, Peter-Christian Zinn, Fabian Gieseke
Quasars with a high redshift (z) are important to understand the evolution processes of galaxies in the early universe. However only a few of these distant objects are known to thi…
Detecting Quasars in Large-Scale Astronomical Surveys
Fabian Gieseke, Kai Lars Polsterer, Andreas Thom +5
We present a classification-based approach to identify quasi-stellar radio sources (quasars) in the Sloan Digital Sky Survey and evaluate its performance on a manually labeled trai…