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Found 19 papers, 8 papers with code
Tree Counting by Bridging 3D Point Clouds with Imagery
We leverage the fusion of three-dimensional LiDAR measurements and 2D imagery to facilitate the accurate counting of trees.
End-to-End Neural Network Training for Hyperbox-Based Classification
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.
BuildSeg: A General Framework for the Segmentation of Buildings
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.
LR-CSNet: Low-Rank Deep Unfolding Network for Image Compressive Sensing
In this work, we propose a DUN called low-rank CS network (LR-CSNet) for natural image CS.
Deep Learning Based 3D Point Cloud Regression for Estimating Forest Biomass
Quantification of forest biomass stocks and their dynamics is important for implementing effective climate change mitigation measures.
Creating cloud-free satellite imagery from image time series with deep learning
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.
Attentional Feature Fusion
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
Attention as Activation
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.
Inferring astrophysical X-ray polarization with deep learning
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.
Magnitude and Uncertainty Pruning Criterion for Neural Networks
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.
Detecting Hardly Visible Roads in Low-Resolution Satellite Time Series Data
We evaluate our approaches on a dataset that is based on Sentinel~2 satellite imagery and OpenStreetMap vector data.
End-To-End Input Selection for Deep Neural Networks
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.
Input Selection for Bandwidth-Limited Neural Network Inference
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.
Return of the features. Efficient feature selection and interpretation for photometric redshifts
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
Training Big Random Forests with Little Resources
Without access to large compute clusters, building random forests on large datasets is still a challenging problem.
Deep-Learnt Classification of Light Curves
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.
Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy
Astrophysics and cosmology are rich with data.
Bigger Buffer k-d Trees on Multi-Many-Core Systems
A buffer k-d tree is a k-d tree variant for massively-parallel nearest neighbor search.
Sacrificing information for the greater good: how to select photometric bands for optimal accuracy
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.
