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20152021
most citedTree Annotations in LiDAR Data Using Point Densities and Convolutional Neural Networks

19 citations · 38 across the 9 of their papers we have counts for

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cs.CV2021

Sparse Spatial Attention Network for Semantic Segmentation

Mengyu Liu, Hujun Yin

The spatial attention mechanism captures long-range dependencies by aggregating global contextual information to each query location, which is beneficial for semantic segmentation.…

cs.CV20211 cited

Graph Convolutional Networks in Feature Space for Image Deblurring and Super-resolution

Boyan Xu, Hujun Yin

Graph convolutional networks (GCNs) have achieved great success in dealing with data of non-Euclidean structures. Their success directly attributes to fitting graph structures effe…

cs.CV20207 cited

CNN-Based Semantic Change Detection in Satellite Imagery

Ananya Gupta, Elisabeth Welburn, Simon Watson +1

Timely disaster risk management requires accurate road maps and prompt damage assessment. Currently, this is done by volunteers manually marking satellite imagery of affected areas…

cs.CV202019 cited

Tree Annotations in LiDAR Data Using Point Densities and Convolutional Neural Networks

Ananya Gupta, Jonathan Byrne, David Moloney +2

LiDAR provides highly accurate 3D point clouds. However, data needs to be manually labelled in order to provide subsequent useful information. Manual annotation of such data is tim…

cs.CV20201 cited

3D Point Cloud Feature Explanations Using Gradient-Based Methods

Ananya Gupta, Simon Watson, Hujun Yin

Explainability is an important factor to drive user trust in the use of neural networks for tasks with material impact. However, most of the work done in this area focuses on image…

cs.CV2019

Multi-Temporal Aerial Image Registration Using Semantic Features

Ananya Gupta, Yao Peng, Simon Watson +1

A semantic feature extraction method for multitemporal high resolution aerial image registration is proposed in this paper. These features encode properties or information about te…