1.6k citations · 2.2k across the 44 of their papers we have counts for
11 papers · 1 filter
Ben-ge: Extending BigEarthNet with Geographical and Environmental Data
Michael Mommert, Nicolas Kesseli, Joëlle Hanna +3
Deep learning methods have proven to be a powerful tool in the analysis of large amounts of complex Earth observation data. However, while Earth observation data are multi-modal in…
Saliency Diversified Deep Ensemble for Robustness to Adversaries
Alex Bogun, Dimche Kostadinov, Damian Borth
Deep learning models have shown incredible performance on numerous image recognition, classification, and reconstruction tasks. Although very appealing and valuable due to their pr…
Learning Interpretable Concept Groups in CNNs
Saurabh Varshneya, Antoine Ledent, Robert A. Vandermeulen +4
We propose a novel training methodology -- Concept Group Learning (CGL) -- that encourages training of interpretable CNN filters by partitioning filters in each layer into concept…
Power Plant Classification from Remote Imaging with Deep Learning
Michael Mommert, Linus Scheibenreif, Joëlle Hanna +1
Satellite remote imaging enables the detailed study of land use patterns on a global scale. We investigate the possibility to improve the information content of traditional land us…
Characterization of Industrial Smoke Plumes from Remote Sensing Data
Michael Mommert, Mario Sigel, Marcel Neuhausler +2
The major driver of global warming has been identified as the anthropogenic release of greenhouse gas (GHG) emissions from industrial activities. The quantitative monitoring of the…
Overcoming Missing and Incomplete Modalities with Generative Adversarial Networks for Building Footprint Segmentation
Benjamin Bischke, Patrick Helber, Florian König +2
The integration of information acquired with different modalities, spatial resolution and spectral bands has shown to improve predictive accuracies. Data fusion is therefore one of…
What do Deep Networks Like to See?
Sebastian Palacio, Joachim Folz, Jörn Hees +3
We propose a novel way to measure and understand convolutional neural networks by quantifying the amount of input signal they let in. To do this, an autoencoder (AE) was fine-tuned…
Multi-Task Learning for Segmentation of Building Footprints with Deep Neural Networks
Benjamin Bischke, Patrick Helber, Joachim Folz +2
The increased availability of high resolution satellite imagery allows to sense very detailed structures on the surface of our planet. Access to such information opens up new direc…
EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
Patrick Helber, Benjamin Bischke, Andreas Dengel +1
In this paper, we address the challenge of land use and land cover classification using Sentinel-2 satellite images. The Sentinel-2 satellite images are openly and freely accessibl…
Mapping Images to Sentiment Adjective Noun Pairs with Factorized Neural Nets
Takuya Narihira, Damian Borth, Stella X. Yu +2
We consider the visual sentiment task of mapping an image to an adjective noun pair (ANP) such as "cute baby". To capture the two-factor structure of our ANP semantics as well as t…
DeepSentiBank: Visual Sentiment Concept Classification with Deep Convolutional Neural Networks
Tao Chen, Damian Borth, Trevor Darrell +1
This paper introduces a visual sentiment concept classification method based on deep convolutional neural networks (CNNs). The visual sentiment concepts are adjective noun pairs (A…