Deep learning for LiDAR point cloud classification in remote sensing
Point clouds are one of the most widely used data formats produced by depth sensors.
There is a lot of research into feature extraction from unordered and irregular point cloud …
There is a lot of research into feature extraction from unordered and irregular point cloud …
Review of automatic processing of topography and surface feature identification LiDAR data using machine learning techniques
Machine Learning (ML) applications on Light Detection And Ranging (LiDAR) data have
provided promising results and thus this topic has been widely addressed in the literature …
provided promising results and thus this topic has been widely addressed in the literature …
Pixset: An opportunity for 3d computer vision to go beyond point clouds with a full-waveform lidar dataset
JL Déziel, P Merriaux, F Tremblay… - 2021 ieee …, 2021 - ieeexplore.ieee.org
Leddar PixSet is a new publicly available dataset for autonomous driving research and
development. One key novelty of this dataset is the presence of full-waveform data from the …
development. One key novelty of this dataset is the presence of full-waveform data from the …
Improved anchor-free instance segmentation for building extraction from high-resolution remote sensing images
T Wu, Y Hu, L Peng, R Chen - Remote Sensing, 2020 - mdpi.com
Building extraction from high-resolution remote sensing images plays a vital part in urban
planning, safety supervision, geographic databases updates, and some other applications …
planning, safety supervision, geographic databases updates, and some other applications …
Convolutional Autoencoder‐Based Deep Learning Approach for Aerosol Emission Detection Using LiDAR Dataset
Quantifying atmospheric aerosols and their linkages to climatic repercussions is necessary
to understand the dynamics of climate forcing and enhance our knowledge of climate …
to understand the dynamics of climate forcing and enhance our knowledge of climate …
Urbanization Detection Using LiDAR‐Based Remote Sensing Images of Azad Kashmir Using Novel 3D CNNs
An important measurable indicator of urbanization and its environmental implications has
been identified as the urban impervious surface. It presents a strategy based on three …
been identified as the urban impervious surface. It presents a strategy based on three …
Convolutional neural networks-based object detection algorithm by jointing semantic segmentation for images
B Qiang, R Chen, M Zhou, Y Pang, Y Zhai, M Yang - Sensors, 2020 - mdpi.com
In recent years, increasing image data comes from various sensors, and object detection
plays a vital role in image understanding. For object detection in complex scenes, more …
plays a vital role in image understanding. For object detection in complex scenes, more …
[HTML][HTML] Diffusion unit: Interpretable edge enhancement and suppression learning for 3d point cloud segmentation
Abstract 3D point clouds are discrete samples of continuous surfaces which can be used for
various applications. However, the lack of true connectivity information, ie, edge information …
various applications. However, the lack of true connectivity information, ie, edge information …
Multi-dataset hyper-CNN for hyperspectral image segmentation of remote sensing images
This research paper presents novel condensed CNN architecture for the recognition of
multispectral images, which has been developed to address the lack of attention paid to …
multispectral images, which has been developed to address the lack of attention paid to …
Quantifying the Risk of Unmapped Associations for Mobile Robot Localization Safety
Integrity risk is a measure of localization safety that accounts for the presence of undetected
sensor faults. The metric has been used for decades in aviation and has recently been …
sensor faults. The metric has been used for decades in aviation and has recently been …