4 citations · 11 across the 4 of their papers we have counts for
4 papers
Utilizing Satellite Imagery Datasets and Machine Learning Data Models to Evaluate Infrastructure Change in Undeveloped Regions
Kyle McCullough, Andrew Feng, Meida Chen +1
In the globalized economic world, it has become important to understand the purpose behind infrastructural and construction initiatives occurring within developing regions of the e…
Semantic Segmentation and Data Fusion of Microsoft Bing 3D Cities and Small UAV-based Photogrammetric Data
Meida Chen, Andrew Feng, Kyle McCullough +3
With state-of-the-art sensing and photogrammetric techniques, Microsoft Bing Maps team has created over 125 highly detailed 3D cities from 11 different countries that cover hundred…
Generating synthetic photogrammetric data for training deep learning based 3D point cloud segmentation models
Meida Chen, Andrew Feng, Kyle McCullough +3
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 locati…
Fully Automated Photogrammetric Data Segmentation and Object Information Extraction Approach for Creating Simulation Terrain
Meida Chen, Andrew Feng, Kyle McCullough +4
Our previous works have demonstrated that visually realistic 3D meshes can be automatically reconstructed with low-cost, off-the-shelf unmanned aerial systems (UAS) equipped with c…