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Camera-LiDAR Datasets
UNB
Leahy, Jennifer; Jabari, Shabnam — 2024-08-23 The datasets are original and specifically collected for research aimed at reducing registration errors between Camera-LiDAR datasets. Traditional methods often struggle with aligning 2D-3D data from sources that have different coordinate systems and resolutions. Our collection comprises six datasets from two distinct setups, designed to enhance versatility in our approach and improve matching accuracy across both high-feature and low-feature environments. Survey-Grade Terrestrial Dataset: Collection Details: Data was gathered across various scenes on the University of New Brunswick campus, including low-feature walls, high-feature laboratory rooms, and outdoor tree environments. Equipment: LiDAR data was captured using a Trimble TX5 3D Laser Scanner, while optical images were taken with a Canon EOS 5D Mark III DSLR camera. Mobile Mapping System Dataset: Collection Details: This dataset was collected using our custom-built Simultaneous Localization and Multi-Sensor Mapping Robot (SLAMM-BOT) in several indoor mobile scenes to validate our methods. Equipment: Data was acquired using a Velodyne VLP-16 LiDAR scanner and an Arducam IMX477 Mini camera, controlled via a Raspberry Pi board.
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Crack Roboflow DTI UNB
UNB
Tello-Gil, Carlos; Jabari, Shabnam; Waugh, Lloyd; Masry, Mark; McGinn, Jared — 2024-02-28 The dataset is designed for instance segmentation tasks related to detecting cracks in concrete structure inspection. It is a valuable resource for training CNNs to identify and segment cracks in various surfaces accurately. It was assembled from three different sources: 1. The platform Roboflow-Universe developed by Dwyer et al (2022): We used the Roboflow-Universe-Crack (RUC) dataset (Nadar, 2022) with a total of 1551 samples. 2. The archives of the Department of Transportation and Infrastructure (DTI) from New Brunswick: We picked 540 original crack images, each of which underwent annotation using the CVAT annotation tool (Sekachev et al., 2020), ensuring precision and uniformity of labelling. 3. The Crack500 dataset (Yang et al., 2020; Zhang et al., 2016) We re-annotated and used 56 samples.
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Roof Types Dataset
UNB
Soleimani Vostikolaei, Faezeh; Jabari, Shabnam — 2024-02-06 The roof-type dataset includes 2224 samples of high-resolution RGB optical images of roofs alongside their elevation (DSM) data. This comprehensive dataset encompasses nine common roof types: Flat, Gable, Hip, Cross-Hip, Gable-Flat, Gambrel, Cross-Gable, Dutch-Gable, and Pyramid-Hip. The dataset has been structured into three parts, namely training, testing, and validation sets. The training set was created using buildings located in Fredericton, New Brunswick, Canada, while the test and validation sets were generated based on buildings in Moncton, New Brunswick, Canada. To ensure the highest level of accuracy for CNN-based classification, the training set was augmented to make 1000 samples for each class. This dataset contains information licensed under the Open Government Licence – New Brunswick.

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(Source Repository: University of New Brunswick Dataverse) AND (Author: Jabari, Shabnam)

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