Turgeon-Pelchat et al., 2021 - Google Patents

Deep learning-based classification of large-scale airborne LiDAR point cloud

Turgeon-Pelchat et al., 2021

Document ID
16292506931436506337
Author
Turgeon-Pelchat M
Foucher S
Bouroubi Y
Publication year
Publication venue
Canadian Journal of Remote Sensing

External Links

Snippet

Airborne LiDAR data allow the precise modeling of topography and are used in multiple contexts. To facilitate further analysis, the point cloud classification process allows the assignment of a class, object or feature, to each point. This research uses ConvPoint, a deep …
Continue reading at www.tandfonline.com (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G06F17/30312Storage and indexing structures; Management thereof
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G06F17/30587Details of specialised database models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30244Information retrieval; Database structures therefor; File system structures therefor in image databases
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00624Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • G06K9/0063Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas
    • G06K9/00657Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas of vegetation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10Complex mathematical operations
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/002D [Two Dimensional] image generation
    • G06T11/20Drawing from basic elements, e.g. lines or circles
    • G06T11/206Drawing of charts or graphs
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00Three dimensional [3D] modelling, e.g. data description of 3D objects
    • G06T17/05Geographic models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10032Satellite or aerial image; Remote sensing
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00Commerce, e.g. shopping or e-commerce

Similar Documents

Publication Publication Date Title
Vos et al. CoastSat: A Google Earth Engine-enabled Python toolkit to extract shorelines from publicly available satellite imagery
Barth et al. DINCAE 2.0: multivariate convolutional neural network with error estimates to reconstruct sea surface temperature satellite and altimetry observations
Sameen et al. Classification of very high resolution aerial photos using spectral‐spatial convolutional neural networks
Fu et al. Using convolutional neural network to identify irregular segmentation objects from very high-resolution remote sensing imagery
EP4348566B1 (en) Enhancing generative adversarial networks using combined inputs
Jakovljevic et al. Accuracy assessment of deep learning based classification of LiDAR and UAV points clouds for DTM creation and flood risk mapping
Hu et al. Improving the accuracy of landslide detection in “off-site” area by machine learning model portability comparison: a case study of Jiuzhaigou earthquake, China
Yu et al. Global chlorophyll-a concentration estimation from moderate resolution imaging spectroradiometer using convolutional neural networks
Virtriana et al. Machine learning remote sensing using the random forest classifier to detect the building damage caused by the Anak Krakatau Volcano tsunami
Collins et al. Bathymetric inversion and uncertainty estimation from synthetic surf-zone imagery with machine learning
Xiao et al. A point selection method in map generalization using graph convolutional network model
Kiratiratanapruk et al. Automatic detection of rice disease in images of various leaf sizes
Fanelli et al. Deep learning for the super resolution of Mediterranean sea surface temperature fields
Zhang et al. Segmentation model based on convolutional neural networks for extracting vegetation from Gaofen-2 images
Zhang et al. Large-scale point cloud contour extraction via 3D guided multi-conditional generative adversarial network
Okolie et al. Assessment of explainable tree-based ensemble algorithms for the enhancement of Copernicus digital elevation model in agricultural lands
Yamashkin et al. Improving the efficiency of the ers data analysis techniques by taking into account the neighborhood descriptors
Rehman et al. Long short term memory deep net performance on fused Planet-Scope and Sentinel-2 imagery for detection of agricultural crop
Merchant Classifying open water features using optical satellite imagery and an object-oriented convolutional neural network
Turgeon-Pelchat et al. Deep learning-based classification of large-scale airborne LiDAR point cloud
Zavala-Romero et al. Convolutional neural networks for sea surface data assimilation in operational ocean models: test case in the Gulf of Mexico
Pellerin Le Bas et al. A new open-source software to help design models for automatic 3D point cloud classification in coastal studies
Muna et al. Development of automatic counting system for palm oil tree based on remote sensing imagery
Rau et al. Quantifying spatial uncertainty to improve soil predictions in data-sparse regions
Sen et al. Unsupervised extraction of urban features from airborne lidar data by using self-organizing maps