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Found 8 papers, 4 papers with code
3DMASC: Accessible, explainable 3D point clouds classification. Application to Bi-spectral Topo-bathymetric lidar data
In this work, we introduce explainable machine learning for 3D data classification using Multiple Attributes, Scales, and Clouds under 3DMASC, a new workflow.
Estimation of Physical Parameters of Waveforms With Neural Networks
By leveraging the power of neural networks, the proposed solution successfully learned the inversion model, was able to do prediction of parameters such as depth, attenuation coefficient, and bottom reflectance.
DC3DCD: unsupervised learning for multiclass 3D point cloud change detection
In this paper, we propose an unsupervised method, called DeepCluster 3D Change Detection (DC3DCD), to detect and categorize multiclass changes at point level.
Deep Unsupervised Learning for 3D ALS Point Cloud Change Detection
To circumnavigate this dependence, we propose an unsupervised 3D point cloud change detection method mainly based on self-supervised learning using deep clustering and contrastive learning.
Change detection needs change information: improving deep 3D point cloud change detection
While deep learning has recently proven its effectiveness for this particular task by encoding the information through Siamese networks, we investigate herein the idea of also using change information in the early steps of deep networks.
Learning Digital Terrain Models from Point Clouds: ALS2DTM Dataset and Rasterization-based GAN
Despite the popularity of deep neural networks in various domains, the extraction of digital terrain models (DTMs) from airborne laser scanning (ALS) point clouds is still challenging.
Convolutional Neural Network Modelling for MODIS Land Surface Temperature Super-Resolution
Our proposed network is a modified version of U-Net architecture, which aims at super-resolving the input LST image from 1Km to 250m per pixel.
A deep neural network for multi-species fish detection using multiple acoustic cameras
1 However the results point a new solution for dealing with complex data, such as sonar data, which can also be reapplied in other cases where the signal-to-noise ratio is a challenge.
