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Found 8 papers, 0 papers with code
3D photogrammetry point cloud segmentation using a model ensembling framework
In this paper, we introduce a model ensembling framework for segmenting a 3D photogrammetry point cloud into top-level terrain elements (i. e., ground, human-made objects, and vegetation).
Utilizing Satellite Imagery Datasets and Machine Learning Data Models to Evaluate Infrastructure Change in Undeveloped Regions
A goal of this research is to allow automated monitoring for largescale infrastructure projects, such as railways, to determine reliable metrics that define and predict the direction construction initiatives could take, allowing for a directed monitoring via narrowed and targeted satellite imagery requests.
Semantic Segmentation and Data Fusion of Microsoft Bing 3D Cities and Small UAV-based Photogrammetric Data
This paper discusses the next steps in extending our designed data segmentation framework for segmenting 3D city data.
Generating synthetic photogrammetric data for training deep learning based 3D point cloud segmentation models
At I/ITSEC 2019, the authors presented a fully-automated workflow to segment 3D photogrammetric point-clouds/meshes and extract object information, including individual tree locations and ground materials (Chen et al., 2019).
Fully Automated Photogrammetric Data Segmentation and Object Information Extraction Approach for Creating Simulation Terrain
The results showed that 3D mesh trees could be replaced with geo-typical 3D tree models using the extracted individual tree locations.
Photogrammetric point cloud segmentation and object information extraction for creating virtual environments and simulations
Thus, segmenting generated point clouds and meshes and extracting the associated object information is a necessary step.
Semantic Modeling of Outdoor Scenes for the Creation of Virtual Environments and Simulations
Efforts from both academia and industry have adopted photogrammetric techniques to generate visually compelling 3D models for the creation of virtual environments and simulations.
Small Drone Field Experiment: Data Collection & Processing
Following an initiative formalized in April 2016 formally known as ARL West between the U. S. Army Research Laboratory (ARL) and University of Southern California's Institute for Creative Technologies (USC ICT), a field experiment was coordinated and executed in the summer of 2016 by ARL, USC ICT, and Headwall Photonics.
