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Found 13 papers, 3 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.
Predicting urban tree cover from incomplete point labels and limited background information
This paper studies how to combine accurate point labels of urban trees along streets with crowd-sourced annotations from an open geographic database to delineate city trees in remote sensing images, a task which is challenging even for humans.
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.
Remember to correct the bias when using deep learning for regression!
We suggest to adjust the bias of the machine learning model after training as a default postprocessing step, which efficiently solves the problem.
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 #651 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.
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.
