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Articles 1 - 30 of 38
Full-Text Articles in Engineering
Improving Insar Accuracy For Slow Deformation And Change Detection With Lidar And Gps, Yusupujiang Aimaiti, Vasit Sagan, Cagri Gul, Jeremy Maurer, Jackson D. Cothren, Carla Klehm, Elizabeth A. Koenig, Nathan Scott
Improving Insar Accuracy For Slow Deformation And Change Detection With Lidar And Gps, Yusupujiang Aimaiti, Vasit Sagan, Cagri Gul, Jeremy Maurer, Jackson D. Cothren, Carla Klehm, Elizabeth A. Koenig, Nathan Scott
Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works
Interferometric Synthetic Aperture Radar (InSAR) uses two or more SAR images over the same region for mapping ground surface displacements through time. InSAR displacements are relative to a reference point due to the 2π phase ambiguity that from unwrapping, and as a result ground control points (GCPs) are often used to calibrate the displacement model and obtain absolute measurements. Errors in InSAR time-series are typically measured with respect to the reference point, but absolute errors are not currently well constrained. To develop absolute error models for InSAR, we generate Sentinel-1 InSAR time-series for Belleville, Illinois and compare time-series with Global …
Scan2drawing: Use Of Deep Learning For As-Built Model Landscape Architecture, Sisi Han, Yuhan Jiang, Yilei Huang, Mingzhu Wang, Yong Bai, Andrea Spool-White
Scan2drawing: Use Of Deep Learning For As-Built Model Landscape Architecture, Sisi Han, Yuhan Jiang, Yilei Huang, Mingzhu Wang, Yong Bai, Andrea Spool-White
Biomedical Engineering Faculty Research and Publications
This paper presents an innovative and fully automatic solution of generating as-built computer-aided design (CAD) drawings for landscape architecture (LA) with three dimensional (3D) reality data scanned via drone, camera, and LiDAR. To start with the full pipeline, 2D feature images of ortho-image and elevation-map are converted from the reality data. A deep learning-based light convolutional encoder–decoder was developed, and compared with U-Net (a binary segmentation model), for image pixelwise segmentation to realize automatic site surface classification, object detection, and ground control point identification. Then, the proposed elevation clustering and segmentation algorithms can automatically extract contours for each instance from …
Point Density For Soil Specimen Volume Measurements In Image-Based Methods During Triaxial Testing, Sara Fayek, Xiong Zhang, Javad Galinmoghadam, Jeffrey D. Cawlfield
Point Density For Soil Specimen Volume Measurements In Image-Based Methods During Triaxial Testing, Sara Fayek, Xiong Zhang, Javad Galinmoghadam, Jeffrey D. Cawlfield
Civil, Architectural and Environmental Engineering Faculty Research & Creative Works
Discrete Measurement Targets Were Frequently Utilized in Image-Based Methods on the Specimen's Surface to Monitor the Soil Specimen during Triaxial Testing. However, the Required Density of Measurement Targets that Should Be Used in Triaxial Testing to Achieve Highly Accurate Volume Measurement Has Not Been Investigated. to overcome This Limitation, This Paper Presents a Parametric Study to Determine the Optimum Target/point Densities to Be Utilized on the Triaxial Soil Specimen Surface to Achieve the Desired Level of Volume Measurement Accuracy in Image-Based Methods. LiDAR Scanning Was Applied to Establish the "Ground Truth" Volume of the Specimen. the Effects of Deformation and …
Aerial Lidar-Based 3d Object Detection And Tracking For Traffic Monitoring, Baya Cherif, Hakim Ghazzai, Ahmad Alsharoa, Hichem Besbes, Yehia Massoud
Aerial Lidar-Based 3d Object Detection And Tracking For Traffic Monitoring, Baya Cherif, Hakim Ghazzai, Ahmad Alsharoa, Hichem Besbes, Yehia Massoud
Electrical and Computer Engineering Faculty Research & Creative Works
The proliferation of Light Detection and Ranging (LiDAR) technology in the automotive industry has quickly promoted its use in many emerging areas in smart cities and internet-of-things. Compared to other sensors, like cameras and radars, LiDAR provides up to 64 scanning channels, vertical and horizontal field of view, high precision, high detection range, and great performance under poor weather conditions. In this paper, we propose a novel aerial traffic monitoring solution based on Light Detection and Ranging (LiDAR) technology. By equipping unmanned aerial vehicles (UAVs) with a LiDAR sensor, we generate 3D point cloud data that can be used for …
Use Of High-Resolution Multi-Temporal Dem Data For Landslide Detection, Behnam Azmoon, Aynaz Biniyaz, Zhen (Leo) Liu
Use Of High-Resolution Multi-Temporal Dem Data For Landslide Detection, Behnam Azmoon, Aynaz Biniyaz, Zhen (Leo) Liu
Michigan Tech Publications
Landslides in urban areas have been relatively well-documented in landslide inventories despite issues in accuracy and completeness, e.g., the absence of small landslides. By contrast, less attention has been paid to landslides in sparsely populated areas in terms of their occurrences and locations. This study utilizes high-resolution and LiDAR-derived digital elevation models (DEMs) at two different times for landslide detection to (1) improve the localization and detection accuracies in landslide inventories, (2) minimize human intervention in the landslide detection process, and (3) identify landslides that cannot be easily documented in the current state of the practice. To achieve this goal, …
Increasing Bridge Durability And Service Life With Lidar Enhanced Unmanned Aerial Systems (Uas), Fernando Moreu, Mahsa Sanei, Chris Lippitt
Increasing Bridge Durability And Service Life With Lidar Enhanced Unmanned Aerial Systems (Uas), Fernando Moreu, Mahsa Sanei, Chris Lippitt
Data
Bridge construction inspections require quantitative measurements and location information. The conventional approach is visual inspection, which in general, is rather time-consuming, expensive due to traffic closure, subjective, and needs special access. Therefore an automated rebar layout detection algorithm was developed to quickly extract quantitative rebar layout information from the LiDAR data. This systematic method can automatically cluster the bridge elements from a 3D point cloud by using LiDAR-equipped UAS data collection and unsupervised machine learning techniques. A new automated inspection system using a LIDAR-equipped UAS can eventually if developed and tested be more reliable as well as less expensive. In …
Increasing Bridge Durability And Service Life With Lidar Enhanced Unmanned Aerial Systems (Uas), Fernando Moreu, Mahsa Sanei, Chris Lippitt
Increasing Bridge Durability And Service Life With Lidar Enhanced Unmanned Aerial Systems (Uas), Fernando Moreu, Mahsa Sanei, Chris Lippitt
Publications
Bridge construction inspections require quantitative measurements and location information. The conventional approach is visual inspection, which in general, is rather time-consuming, expensive due to traffic closure, subjective, and needs special access. Therefore an automated rebar layout detection algorithm was developed to quickly extract quantitative rebar layout information from the LiDAR data. This systematic method can automatically cluster the bridge elements from a 3D point cloud by using LiDAR-equipped UAS data collection and unsupervised machine learning techniques. A new automated inspection system using a LIDAR-equipped UAS can eventually if developed and tested be more reliable as well as less expensive. In …
Temporal Lidar Scanning In Quantifying Cumulative Rockfall Volume And Hazard Assessment: A Case Study At Southwestern Saudi Arabia, Abdullah A. Alotaibi, Norbert H. Maerz, Kenneth J. Boyko, Ahmed M. Youssef, Biswajeet Pradhan
Temporal Lidar Scanning In Quantifying Cumulative Rockfall Volume And Hazard Assessment: A Case Study At Southwestern Saudi Arabia, Abdullah A. Alotaibi, Norbert H. Maerz, Kenneth J. Boyko, Ahmed M. Youssef, Biswajeet Pradhan
Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works
Rockfalls and unstable slopes pose a serious threat to people and property along roads/highways in the southwestern mountainous regions of Saudi Arabia. In this study, the application of terrestrial light detection and ranging (LiDAR) technology was applied aiming to propose a strategy to analyze and accurately depict the detection of rockfall changes, calculation of rockfall volume, and evaluate rockfall hazards along the Habs Road, Jazan Region, Saudi Arabia. A series of temporal LiDAR scans were acquired at three selected sites. Our results show that these three sites have different degrees of hazard due to their geological differences. The mean volume …
Temporal Lidar Scanning In Quantifying Cumulative Rockfall Volume And Hazard Assessment: A Case Study At Southwestern Saudi Arabia, Abdullah A. Alotaibi, Norbert H. Maerz, Kenneth J. Boyko, Ahmed M. Youssef, Biswajeet Pradhan
Temporal Lidar Scanning In Quantifying Cumulative Rockfall Volume And Hazard Assessment: A Case Study At Southwestern Saudi Arabia, Abdullah A. Alotaibi, Norbert H. Maerz, Kenneth J. Boyko, Ahmed M. Youssef, Biswajeet Pradhan
Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works
Rockfalls and unstable slopes pose a serious threat to people and property along roads/highways in the southwestern mountainous regions of Saudi Arabia. In this study, the application of terrestrial light detection and ranging (LiDAR) technology was applied aiming to propose a strategy to analyze and accurately depict the detection of rockfall changes, calculation of rockfall volume, and evaluate rockfall hazards along the Habs Road, Jazan Region, Saudi Arabia. A series of temporal LiDAR scans were acquired at three selected sites. Our results show that these three sites have different degrees of hazard due to their geological differences. The mean volume …
The Winter Adverse Driving Dataset (Wads) - Sequence 16, Akhil Kurup, Jeremy Bos
The Winter Adverse Driving Dataset (Wads) - Sequence 16, Akhil Kurup, Jeremy Bos
Michigan Tech Research Data
Collected in the snow belt region of Michigan's Upper Peninsula, WADS is the first multi-modal dataset featuring dense point-wise labeled sequential LiDAR scans collected in severe winter weather. Over 26 TB of multi modal data has been collected of which over 7 GB of LiDAR point clouds (3.6 billion points) have been labeled (semanticKITTI format) and made available here. This outdoor dataset introduces falling snow and accumulated snow along with all the semanticKITTI classes. We believe this dataset will further AV tasks like semantic and panoptic segmentation, object detection and tracking, and localization and mapping in conditions of moderate to …
The Winter Adverse Driving Dataset (Wads) - Sequence 35, Akhil Kurup, Jeremy Bos
The Winter Adverse Driving Dataset (Wads) - Sequence 35, Akhil Kurup, Jeremy Bos
Michigan Tech Research Data
Collected in the snow belt region of Michigan's Upper Peninsula, WADS is the first multi-modal dataset featuring dense point-wise labeled sequential LiDAR scans collected in severe winter weather. Over 26 TB of multi modal data has been collected of which over 7 GB of LiDAR point clouds (3.6 billion points) have been labeled (semanticKITTI format) and made available here. This outdoor dataset introduces falling snow and accumulated snow along with all the semanticKITTI classes. We believe this dataset will further AV tasks like semantic and panoptic segmentation, object detection and tracking, and localization and mapping in conditions of moderate to …
The Winter Adverse Driving Dataset (Wads) - Sequence 34, Akhil Kurup, Jeremy Bos
The Winter Adverse Driving Dataset (Wads) - Sequence 34, Akhil Kurup, Jeremy Bos
Michigan Tech Research Data
Collected in the snow belt region of Michigan's Upper Peninsula, WADS is the first multi-modal dataset featuring dense point-wise labeled sequential LiDAR scans collected in severe winter weather. Over 26 TB of multi modal data has been collected of which over 7 GB of LiDAR point clouds (3.6 billion points) have been labeled (semanticKITTI format) and made available here. This outdoor dataset introduces falling snow and accumulated snow along with all the semanticKITTI classes. We believe this dataset will further AV tasks like semantic and panoptic segmentation, object detection and tracking, and localization and mapping in conditions of moderate to …
The Winter Adverse Driving Dataset (Wads) - Sequence 13, Akhil Kurup, Jeremy Bos
The Winter Adverse Driving Dataset (Wads) - Sequence 13, Akhil Kurup, Jeremy Bos
Michigan Tech Research Data
Collected in the snow belt region of Michigan's Upper Peninsula, WADS is the first multi-modal dataset featuring dense point-wise labeled sequential LiDAR scans collected in severe winter weather. Over 26 TB of multi modal data has been collected of which over 7 GB of LiDAR point clouds (3.6 billion points) have been labeled (semanticKITTI format) and made available here. This outdoor dataset introduces falling snow and accumulated snow along with all the semanticKITTI classes. We believe this dataset will further AV tasks like semantic and panoptic segmentation, object detection and tracking, and localization and mapping in conditions of moderate to …
The Winter Adverse Driving Dataset (Wads) - Sequence 14, Akhil Kurup, Jeremy Bos
The Winter Adverse Driving Dataset (Wads) - Sequence 14, Akhil Kurup, Jeremy Bos
Michigan Tech Research Data
Collected in the snow belt region of Michigan's Upper Peninsula, WADS is the first multi-modal dataset featuring dense point-wise labeled sequential LiDAR scans collected in severe winter weather. Over 26 TB of multi modal data has been collected of which over 7 GB of LiDAR point clouds (3.6 billion points) have been labeled (semanticKITTI format) and made available here. This outdoor dataset introduces falling snow and accumulated snow along with all the semanticKITTI classes. We believe this dataset will further AV tasks like semantic and panoptic segmentation, object detection and tracking, and localization and mapping in conditions of moderate to …
The Winter Adverse Driving Dataset (Wads) - Sequence 18, Akhil Kurup, Jeremy Bos
The Winter Adverse Driving Dataset (Wads) - Sequence 18, Akhil Kurup, Jeremy Bos
Michigan Tech Research Data
Collected in the snow belt region of Michigan's Upper Peninsula, WADS is the first multi-modal dataset featuring dense point-wise labeled sequential LiDAR scans collected in severe winter weather. Over 26 TB of multi modal data has been collected of which over 7 GB of LiDAR point clouds (3.6 billion points) have been labeled (semanticKITTI format) and made available here. This outdoor dataset introduces falling snow and accumulated snow along with all the semanticKITTI classes. We believe this dataset will further AV tasks like semantic and panoptic segmentation, object detection and tracking, and localization and mapping in conditions of moderate to …
The Winter Adverse Driving Dataset (Wads) - Sequence 24, Akhil Kurup, Jeremy Bos
The Winter Adverse Driving Dataset (Wads) - Sequence 24, Akhil Kurup, Jeremy Bos
Michigan Tech Research Data
Collected in the snow belt region of Michigan's Upper Peninsula, WADS is the first multi-modal dataset featuring dense point-wise labeled sequential LiDAR scans collected in severe winter weather. Over 26 TB of multi modal data has been collected of which over 7 GB of LiDAR point clouds (3.6 billion points) have been labeled (semanticKITTI format) and made available here. This outdoor dataset introduces falling snow and accumulated snow along with all the semanticKITTI classes. We believe this dataset will further AV tasks like semantic and panoptic segmentation, object detection and tracking, and localization and mapping in conditions of moderate to …
The Winter Adverse Driving Dataset (Wads) - Sequence 30, Akhil Kurup, Jeremy Bos
The Winter Adverse Driving Dataset (Wads) - Sequence 30, Akhil Kurup, Jeremy Bos
Michigan Tech Research Data
Collected in the snow belt region of Michigan's Upper Peninsula, WADS is the first multi-modal dataset featuring dense point-wise labeled sequential LiDAR scans collected in severe winter weather. Over 26 TB of multi modal data has been collected of which over 7 GB of LiDAR point clouds (3.6 billion points) have been labeled (semanticKITTI format) and made available here. This outdoor dataset introduces falling snow and accumulated snow along with all the semanticKITTI classes. We believe this dataset will further AV tasks like semantic and panoptic segmentation, object detection and tracking, and localization and mapping in conditions of moderate to …
The Winter Adverse Driving Dataset (Wads) - Sequence 76, Akhil Kurup, Jeremy Bos
The Winter Adverse Driving Dataset (Wads) - Sequence 76, Akhil Kurup, Jeremy Bos
Michigan Tech Research Data
Collected in the snow belt region of Michigan's Upper Peninsula, WADS is the first multi-modal dataset featuring dense point-wise labeled sequential LiDAR scans collected in severe winter weather. Over 26 TB of multi modal data has been collected of which over 7 GB of LiDAR point clouds (3.6 billion points) have been labeled (semanticKITTI format) and made available here. This outdoor dataset introduces falling snow and accumulated snow along with all the semanticKITTI classes. We believe this dataset will further AV tasks like semantic and panoptic segmentation, object detection and tracking, and localization and mapping in conditions of moderate to …
The Winter Adverse Driving Dataset (Wads) - Sequence 17, Akhil Kurup, Jeremy Bos
The Winter Adverse Driving Dataset (Wads) - Sequence 17, Akhil Kurup, Jeremy Bos
Michigan Tech Research Data
Collected in the snow belt region of Michigan's Upper Peninsula, WADS is the first multi-modal dataset featuring dense point-wise labeled sequential LiDAR scans collected in severe winter weather. Over 26 TB of multi modal data has been collected of which over 7 GB of LiDAR point clouds (3.6 billion points) have been labeled (semanticKITTI format) and made available here. This outdoor dataset introduces falling snow and accumulated snow along with all the semanticKITTI classes. We believe this dataset will further AV tasks like semantic and panoptic segmentation, object detection and tracking, and localization and mapping in conditions of moderate to …
The Winter Adverse Driving Dataset (Wads) - Sequence 20, Akhil Kurup, Jeremy Bos
The Winter Adverse Driving Dataset (Wads) - Sequence 20, Akhil Kurup, Jeremy Bos
Michigan Tech Research Data
Collected in the snow belt region of Michigan's Upper Peninsula, WADS is the first multi-modal dataset featuring dense point-wise labeled sequential LiDAR scans collected in severe winter weather. Over 26 TB of multi modal data has been collected of which over 7 GB of LiDAR point clouds (3.6 billion points) have been labeled (semanticKITTI format) and made available here. This outdoor dataset introduces falling snow and accumulated snow along with all the semanticKITTI classes. We believe this dataset will further AV tasks like semantic and panoptic segmentation, object detection and tracking, and localization and mapping in conditions of moderate to …
The Winter Adverse Driving Dataset (Wads) - Sequence 23, Akhil Kurup, Jeremy Bos
The Winter Adverse Driving Dataset (Wads) - Sequence 23, Akhil Kurup, Jeremy Bos
Michigan Tech Research Data
Collected in the snow belt region of Michigan's Upper Peninsula, WADS is the first multi-modal dataset featuring dense point-wise labeled sequential LiDAR scans collected in severe winter weather. Over 26 TB of multi modal data has been collected of which over 7 GB of LiDAR point clouds (3.6 billion points) have been labeled (semanticKITTI format) and made available here. This outdoor dataset introduces falling snow and accumulated snow along with all the semanticKITTI classes. We believe this dataset will further AV tasks like semantic and panoptic segmentation, object detection and tracking, and localization and mapping in conditions of moderate to …
The Winter Adverse Driving Dataset (Wads) - Sequence 11, Akhil Kurup, Jeremy Bos
The Winter Adverse Driving Dataset (Wads) - Sequence 11, Akhil Kurup, Jeremy Bos
Michigan Tech Research Data
Collected in the snow belt region of Michigan's Upper Peninsula, WADS is the first multi-modal dataset featuring dense point-wise labeled sequential LiDAR scans collected in severe winter weather. Over 26 TB of multi modal data has been collected of which over 7 GB of LiDAR point clouds (3.6 billion points) have been labeled (semanticKITTI format) and made available here. This outdoor dataset introduces falling snow and accumulated snow along with all the semanticKITTI classes. We believe this dataset will further AV tasks like semantic and panoptic segmentation, object detection and tracking, and localization and mapping in conditions of moderate to …
The Winter Adverse Driving Dataset (Wads) - Sequence 12, Akhil Kurup, Jeremy Bos
The Winter Adverse Driving Dataset (Wads) - Sequence 12, Akhil Kurup, Jeremy Bos
Michigan Tech Research Data
Collected in the snow belt region of Michigan's Upper Peninsula, WADS is the first multi-modal dataset featuring dense point-wise labeled sequential LiDAR scans collected in severe winter weather. Over 26 TB of multi modal data has been collected of which over 7 GB of LiDAR point clouds (3.6 billion points) have been labeled (semanticKITTI format) and made available here. This outdoor dataset introduces falling snow and accumulated snow along with all the semanticKITTI classes. We believe this dataset will further AV tasks like semantic and panoptic segmentation, object detection and tracking, and localization and mapping in conditions of moderate to …
The Winter Adverse Driving Dataset (Wads) - Sequence 15, Akhil Kurup, Jeremy Bos
The Winter Adverse Driving Dataset (Wads) - Sequence 15, Akhil Kurup, Jeremy Bos
Michigan Tech Research Data
Collected in the snow belt region of Michigan's Upper Peninsula, WADS is the first multi-modal dataset featuring dense point-wise labeled sequential LiDAR scans collected in severe winter weather. Over 26 TB of multi modal data has been collected of which over 7 GB of LiDAR point clouds (3.6 billion points) have been labeled (semanticKITTI format) and made available here. This outdoor dataset introduces falling snow and accumulated snow along with all the semanticKITTI classes. We believe this dataset will further AV tasks like semantic and panoptic segmentation, object detection and tracking, and localization and mapping in conditions of moderate to …
The Winter Adverse Driving Dataset (Wads) - Sequence 22, Akhil Kurup, Jeremy Bos
The Winter Adverse Driving Dataset (Wads) - Sequence 22, Akhil Kurup, Jeremy Bos
Michigan Tech Research Data
Collected in the snow belt region of Michigan's Upper Peninsula, WADS is the first multi-modal dataset featuring dense point-wise labeled sequential LiDAR scans collected in severe winter weather. Over 26 TB of multi modal data has been collected of which over 7 GB of LiDAR point clouds (3.6 billion points) have been labeled (semanticKITTI format) and made available here. This outdoor dataset introduces falling snow and accumulated snow along with all the semanticKITTI classes. We believe this dataset will further AV tasks like semantic and panoptic segmentation, object detection and tracking, and localization and mapping in conditions of moderate to …
The Winter Adverse Driving Dataset (Wads) - Sequence 26, Akhil Kurup, Jeremy Bos
The Winter Adverse Driving Dataset (Wads) - Sequence 26, Akhil Kurup, Jeremy Bos
Michigan Tech Research Data
Collected in the snow belt region of Michigan's Upper Peninsula, WADS is the first multi-modal dataset featuring dense point-wise labeled sequential LiDAR scans collected in severe winter weather. Over 26 TB of multi modal data has been collected of which over 7 GB of LiDAR point clouds (3.6 billion points) have been labeled (semanticKITTI format) and made available here. This outdoor dataset introduces falling snow and accumulated snow along with all the semanticKITTI classes. We believe this dataset will further AV tasks like semantic and panoptic segmentation, object detection and tracking, and localization and mapping in conditions of moderate to …
The Winter Adverse Driving Dataset (Wads) - Sequence 28, Akhil Kurup, Jeremy Bos
The Winter Adverse Driving Dataset (Wads) - Sequence 28, Akhil Kurup, Jeremy Bos
Michigan Tech Research Data
Collected in the snow belt region of Michigan's Upper Peninsula, WADS is the first multi-modal dataset featuring dense point-wise labeled sequential LiDAR scans collected in severe winter weather. Over 26 TB of multi modal data has been collected of which over 7 GB of LiDAR point clouds (3.6 billion points) have been labeled (semanticKITTI format) and made available here. This outdoor dataset introduces falling snow and accumulated snow along with all the semanticKITTI classes. We believe this dataset will further AV tasks like semantic and panoptic segmentation, object detection and tracking, and localization and mapping in conditions of moderate to …
The Winter Adverse Driving Dataset (Wads) - Sequence 36, Akhil Kurup, Jeremy Bos
The Winter Adverse Driving Dataset (Wads) - Sequence 36, Akhil Kurup, Jeremy Bos
Michigan Tech Research Data
Collected in the snow belt region of Michigan's Upper Peninsula, WADS is the first multi-modal dataset featuring dense point-wise labeled sequential LiDAR scans collected in severe winter weather. Over 26 TB of multi modal data has been collected of which over 7 GB of LiDAR point clouds (3.6 billion points) have been labeled (semanticKITTI format) and made available here. This outdoor dataset introduces falling snow and accumulated snow along with all the semanticKITTI classes. We believe this dataset will further AV tasks like semantic and panoptic segmentation, object detection and tracking, and localization and mapping in conditions of moderate to …
The Winter Adverse Driving Dataset (Wads) - Sequence 37, Akhil Kurup, Jeremy Bos
The Winter Adverse Driving Dataset (Wads) - Sequence 37, Akhil Kurup, Jeremy Bos
Michigan Tech Research Data
Collected in the snow belt region of Michigan's Upper Peninsula, WADS is the first multi-modal dataset featuring dense point-wise labeled sequential LiDAR scans collected in severe winter weather. Over 26 TB of multi modal data has been collected of which over 7 GB of LiDAR point clouds (3.6 billion points) have been labeled (semanticKITTI format) and made available here. This outdoor dataset introduces falling snow and accumulated snow along with all the semanticKITTI classes. We believe this dataset will further AV tasks like semantic and panoptic segmentation, object detection and tracking, and localization and mapping in conditions of moderate to …
Unmanned Aircraft Systems For Archaeology Using Photogrammetry And Lidar In Southwestern United States, Imai Bates-Domingo, Alexandra Gates, Patrick Hunter, Blake Neal, Kyle Snowden, Destin Webster
Unmanned Aircraft Systems For Archaeology Using Photogrammetry And Lidar In Southwestern United States, Imai Bates-Domingo, Alexandra Gates, Patrick Hunter, Blake Neal, Kyle Snowden, Destin Webster
Study America
Researchers can use small unmanned aircraft systems (sUAS), also known as drones, to make observations of historical sites, help interpret locations, and make new discoveries that may not be visible to the naked eye. A student team from Embry-Riddle Aeronautical University gathered data for historical site documentation in New Mexico using the DJI Phantom 4 Pro V2, DJI Mavic Pro 2, DJI M210 and DJI M600, and senseFly eBee. Utilizing these drones, student analysts were able to take the data gathered and create georectified orthomosaic images and 3D virtual objects. At Tularosa Canyon, at a site known as the Creekside …