QUT Research Data Respository Dataset and Resources

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.

Mobile LiDAR Mapping Service (Civil Works) Market Outlook

According to our latest research, the global Mobile LiDAR Mapping Service (Civil Works) market size reached USD 2.15 billion in 2024, driven by the growing demand for high-precision geospatial data in infrastructure projects. The market is expected to expand at a robust CAGR of 13.7% during the forecast period, reaching a projected value of USD 6.01 billion by 2033. This growth is fueled by the rapid adoption of advanced mapping technologies across civil engineering, urban planning, and infrastructure maintenance sectors, as organizations increasingly recognize the efficiency, accuracy, and cost-effectiveness of Mobile LiDAR for large-scale civil works applications.

One of the primary growth factors for the Mobile LiDAR Mapping Service (Civil Works) market is the surging demand for precise and real-time geospatial data in the construction and maintenance of transportation infrastructure. As roadways, bridges, and railways undergo expansion and modernization globally, stakeholders are turning to Mobile LiDAR solutions for topographic mapping, corridor analysis, and asset management. The ability of Mobile LiDAR to quickly and safely collect highly accurate three-dimensional data, even in challenging or inaccessible environments, is transforming how civil works projects are planned, executed, and monitored. This not only accelerates project timelines but also reduces costs associated with manual surveying and minimizes risks to personnel, further driving market adoption.

Another significant growth driver is the integration of Mobile LiDAR mapping with digital twin technologies and Building Information Modeling (BIM). As governments and private enterprises invest in smart city initiatives and infrastructure digitalization, the need for comprehensive, up-to-date, and interoperable geospatial data has never been greater. Mobile LiDAR mapping services provide the foundational datasets required for creating accurate digital replicas of physical assets, enabling predictive maintenance, enhanced asset management, and data-driven urban planning. This convergence of LiDAR with advanced analytics and visualization platforms is opening new avenues for service providers, while also pushing the boundaries of what is possible in civil works engineering and management.

Environmental regulations and the growing emphasis on sustainability in infrastructure development are also propelling the Mobile LiDAR Mapping Service (Civil Works) market forward. Regulatory authorities increasingly mandate detailed environmental impact assessments and ongoing monitoring for large-scale projects. Mobile LiDAR’s ability to deliver high-resolution terrain and vegetation data supports compliance, biodiversity management, and the minimization of ecological footprints. Furthermore, the technology’s non-intrusive nature ensures minimal disruption to natural habitats during data collection, aligning with global sustainability goals. Together, these factors are making Mobile LiDAR mapping indispensable for environmentally conscious civil engineering projects.

Regionally, North America continues to dominate the Mobile LiDAR Mapping Service (Civil Works) market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The United States leads in technology adoption, thanks to substantial investments in infrastructure modernization and a mature ecosystem of LiDAR service providers. Meanwhile, Asia Pacific is witnessing the fastest growth, buoyed by massive infrastructure development in China, India, and Southeast Asia, as well as increasing government initiatives supporting smart cities and digital mapping. Europe’s growth is underpinned by stringent environmental regulations and a strong focus on transportation safety and modernization. Latin America and the Middle East & Africa are emerging markets, gradually increasing their adoption rates as awareness and investment in advanced civil works technologies rise.

Service Type Analysis

Work in progress: data might be changed

The data set contains the locations of public roadside parking spaces in the northeastern part of Hanover Linden-Nord. As a sample data set, it explicitly does not provide a complete, accurate or correct representation of the conditions! It was collected and processed as part of the 5GAPS research project on September 22nd and October 6th 2022 as a basis for further analysis and in particular as input for simulation studies.

Vehicle Detections

Based on the mapping methodology of Bock et al. (2015) and processing of Leichter et al. (2021), the utilization was determined using vehicle detections in segmented 3D point clouds. The corresponding point clouds were collected by driving over the area on two half-days using a LiDAR mobile mapping system, resulting in several hours between observations. Accordingly, these are only a few sample observations. The trips are made in such a way that combined they cover a synthetic day from about 8-20 clock.

The collected point clouds were georeferenced, processed, and automatically segmented semantically (see Leichter et al., 2021). To automatically extract cars, those points with car labels were clustered by observation epoch and bounding boxes were estimated for the clusters as a representation of car instances. The boxes serve both to filter out unrealistically small and large objects, and to rudimentarily complete the vehicle footprint that may not be fully captured from all sides.

https://data.uni-hannover.de/dataset/0945cd36-6797-44ac-a6bd-b7311f0f96bc/resource/807618b6-5c38-4456-88a1-cb47500081ff/download/detection_map.png" alt="Overview map of detected vehicles" title="Overview map of detected vehicles"> Figure 1: Overview map of detected vehicles

Parking Areas

The public parking areas were digitized manually using aerial images and the detected vehicles in order to exclude irregular parking spaces as far as possible. They were also tagged as to whether they were aligned parallel to the road and assigned to a use at the time of recording, as some are used for construction sites or outdoor catering, for example. Depending on the intended use, they can be filtered individually.

https://data.uni-hannover.de/dataset/0945cd36-6797-44ac-a6bd-b7311f0f96bc/resource/16b14c61-d1d6-4eda-891d-176bdd787bf5/download/parking_area_example.png" alt="Example parking area occupation pattern" title="Visualization of example parking areas on top of an aerial image [by LGLN]"> Figure 2: Visualization of example parking areas on top of an aerial image [by LGLN]

Parking Occupancy

For modelling the parking occupancy, single slots are sampled as center points every 5 m from the parking areas. In this way, they can be integrated into a street/routing graph, for example, as prepared in Wage et al. (2023). Own representations can be generated from the parking area and vehicle detections. Those parking points were intersected with the vehicle boxes to identify occupancy at the respective epochs.

https://data.uni-hannover.de/dataset/0945cd36-6797-44ac-a6bd-b7311f0f96bc/resource/ca0b97c8-2542-479e-83d7-74adb2fc47c0/download/datenpub-bays.png" alt="Overview map of parking slots' average load" title="Overview map of parking slots' average load"> Figure 3: Overview map of average parking lot load

However, unoccupied spaces cannot be determined quite as trivially the other way around, since no detected vehicle can result just as from no measurement/observation. Therefore, a parking space is only recorded as unoccupied if a vehicle was detected at the same time in the neighborhood on the same parking lane and therefore it can be assumed that there is a measurement.

To close temporal gaps, interpolations were made by hour for each parking slot, assuming that between two consecutive observations with an occupancy the space was also occupied in between - or if both times free also free in between. If there was a change, this is indicated by a proportional value. To close spatial gaps, unobserved spaces in the area are drawn randomly from the ten closest occupation patterns around.

This results in an exemplary occupancy pattern of a synthetic day. Depending on the application, the value could be interpreted as occupancy probability or occupancy share.

https://data.uni-hannover.de/dataset/0945cd36-6797-44ac-a6bd-b7311f0f96bc/resource/184a1f75-79ab-4d0e-bb1b-8ed170678280/download/occupation_example.png" alt="Example parking area occupation pattern" title="Example parking area occupation pattern"> Figure 4: Example parking area occupation pattern

References

• F. Bock, D. Eggert and M. Sester (2015): On-street Parking Statistics Using LiDAR Mobile Mapping, 2015 IEEE 18th International Conference on Intelligent Transportation Systems, Gran Canaria, Spain, 2015, pp. 2812-2818. https://doi.org/10.1109/ITSC.2015.452
• A. Leichter, U. Feuerhake, and M. Sester (2021): Determination of Parking Space and its Concurrent Usage Over Time Using Semantically Segmented Mobile Mapping Data, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 185–192. https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-185-2021
• O. Wage, M. Heumann, and L. Bienzeisler (2023): Modeling and Calibration of Last-Mile Logistics to Study Smart-City Dynamic Space Management Scenarios. In 1st ACM SIGSPATIAL International Workshop on Sustainable Mobility (SuMob ’23), November 13, 2023, Hamburg, Germany. ACM, New York, NY, USA, 4 pages. https://doi.org/10.1145/3615899.3627930

This is a point cloud sampe data which was collected by a mobile Lidar system (MLS).

The i.c.sens Visual-Inertial-LiDAR Dataset is a data set for the evaluation of dead reckoning or SLAM approaches in the context of mobile robotics. It consists of street-level monocular RGB camera images, a front-facing 180° point cloud, angular velocities, accelerations and an accurate ground truth trajectory. In total, we provide around 77 GB of data resulting from a 15 minutes drive, which is split into 8 rosbags of 2 minutes (10 GB) each. Besides, the intrinsic camera parameters and the extrinsic transformations between all sensor coordinate systems are given. Details on the data and its usage can be found in the provided documentation file.

https://data.uni-hannover.de/dataset/0bcea595-0786-44f6-a9e2-c26a779a004b/resource/0ff90ef9-fa61-4ee3-b69e-eb6461abc57b/download/sensor_platform_small.jpg" alt="">

Image credit: Sören Vogel

The data set was acquired in the context of the measurement campaign described in Schoen2018. Here, a vehicle, which can be seen below, was equipped with a self-developed sensor platform and a commercially available Riegl VMX-250 Mobile Mapping System. This Mobile Mapping System consists of two laser scanners, a camera system and a localization unit containing a highly accurate GNSS/IMU system.

https://data.uni-hannover.de/dataset/0bcea595-0786-44f6-a9e2-c26a779a004b/resource/2a1226b8-8821-4c46-b411-7d63491963ed/download/vehicle_small.jpg" alt="">

Image credit: Sören Vogel

The data acquisition took place in May 2019 during a sunny day in the Nordstadt of Hannover (coordinates: 52.388598, 9.716389). The route we took can be seen below. This route was completed three times in total, which amounts to a total driving time of 15 minutes.

https://data.uni-hannover.de/dataset/0bcea595-0786-44f6-a9e2-c26a779a004b/resource/8a570408-c392-4bd7-9c1e-26964f552d6c/download/google_earth_overview_small.png" alt="">

The self-developed sensor platform consists of several sensors. This dataset provides data from the following sensors:

• Velodyne HDL-64 LiDAR
• LORD MicroStrain 3DM-GQ4-45 GNSS aided IMU
• Pointgrey GS3-U3-23S6C-C RGB camera

To inspect the data, first start a rosmaster and launch rviz using the provided configuration file:

roscore & rosrun rviz rviz -d icsens_data.rviz

Afterwards, start playing a rosbag with

rosbag play icsens-visual-inertial-lidar-dataset-{number}.bag --clock

Below we provide some exemplary images and their corresponding point clouds.

https://data.uni-hannover.de/dataset/0bcea595-0786-44f6-a9e2-c26a779a004b/resource/dc1563c0-9b5f-4c84-b432-711916cb204c/download/combined_examples_small.jpg" alt="">

Related publications:

• R. Voges, C. S. Wieghardt, and B. Wagner, “Finding Timestamp Offsets for a Multi-Sensor System Using Sensor Observations,” Photogrammetric Engineering & Remote Sensing, vol. 84, no. 6, pp. 357–366, 2018.

• R. Voges and B. Wagner, “RGB-Laser Odometry Under Interval Uncertainty for Guaranteed Localization,” in Book of Abstracts of the 11th Summer Workshop on Interval Methods (SWIM 2018), Rostock, Germany, Jul. 2018.

• R. Voges and B. Wagner, “Timestamp Offset Calibration for an IMU-Camera System Under Interval Uncertainty,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, Oct. 2018.

• R. Voges and B. Wagner, “Extrinsic Calibration Between a 3D Laser Scanner and a Camera Under Interval Uncertainty,” in Book of Abstracts of the 12th Summer Workshop on Interval Methods (SWIM 2019), Palaiseau, France, Jul. 2019.

• R. Voges, B. Wagner, and V. Kreinovich, “Efficient Algorithms for Synchronizing Localization Sensors Under Interval Uncertainty,” Reliable Computing (Interval Computations), vol. 27, no. 1, pp. 1–11, 2020.

• R. Voges, B. Wagner, and V. Kreinovich, “Odometry under Interval Uncertainty: Towards Optimal Algorithms, with Potential Application to Self-Driving Cars and Mobile Robots,” Reliable Computing (Interval Computations), vol. 27, no. 1, pp. 12–20, 2020.

• R. Voges and B. Wagner, “Set-Membership Extrinsic Calibration of a 3D LiDAR and a Camera,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA, Oct. 2020, accepted.

• R. Voges, “Bounded-Error Visual-LiDAR Odometry on Mobile Robots Under Consideration of Spatiotemporal Uncertainties,” PhD thesis, Gottfried Wilhelm Leibniz Universität, 2020.

The dataset includes 6 3D point cloud files collected with Velodyne VLP-16 mobile LiDAR (*.las) belonging to 4 cultural and natural heritage structures located in Cappadocia, Türkiye. The structures are:

1- St. Theodore Church (Interior & Exterior): The Church of St. Theodore is located in Yeşilöz Village in Ürgüp district of Nevşehir. Formerly known as Tagar, now known as Yesiloz Village is approximately 16 km from the center of Urgup district and is a settlement area built on the slope of the valley. The church was carved into a large rock mass on the hill northwest of the village. As a result of excavations near the village, a monastery with a courtyard on three sides was discovered. It is thought that the church belonged to this monastery. The church is called both St. Theodore and Tagar Church. Although it is not known where the name Theodore comes from, it is estimated that this name may have been given because the church was built in the name of St. Theodore.

2- Mustafa Efendi Mosque (Interior & Exterior): The masonry Mustafa Efendi Mosque in Bahçeli Village of Ürgüp District of Nevşehir Province is the oldest of the 3 mosques built in the village. It is estimated that it was built about 50 years before the Osman Efendi Mosque, which was presented as a proposed building within the scope of the project, with a construction date of 1746. Although it is known to have a small inscription with the date of construction, this inscription was not found during the survey. According to this information, Mustafa Efendi Mosque is estimated to be a 17th-18th century work.

3- Fairy Chimney: The distance between Bahçeli Village where the fairy chimney is located and Urgup district is 15 kilometers and the formations between these two areas are generally natural formations without caps and in the late fairy chimney period. It shows that the fairy chimney is a natural formation without a cap and in the late fairy chimney period. The fairy chimney is in the 1st degree natural protected area.

4- Masonry House: Bahçeli Village, where the building examined within the scope of the project is located, is 15 km away from Ürgüp district and is a mixed settlement type. There are approximately 200 cove-carved and masonry historical buildings in the village. A large part of the village, including the structures examined in the village, is a 3rd degree natural protected area. The masonry-rock-carved civil architecture dwelling in Bahçeli Village, Ürgüp District, Nevşehir has not been in use since the 1980s and some of the spaces have been completely lost.

The creation of this dataset was funded by the Scientific and Technological Research Council of Türkiye (TUBITAK) 1001 program under Project no. 122Y017.

Detroit Street View (DSV) is an urban remote sensing program run by the Enterprise Geographic Information Systems (EGIS) Team within the Department of Innovation and Technology at the City of Detroit. The mission of Detroit Street View is ‘To continuously observe and document Detroit’s changing physical environment through remote sensing, resulting in freely available foundational data that empowers effective city operations, informed decision making, awareness, and innovation.’ LiDAR (as well as panoramic imagery) is collected using a vehicle-mounted mobile mapping system.

Due to variations in processing, index lines are not currently available for all existing LiDAR datasets, including all data collected before September 2020. Index lines represent the approximate path of the vehicle within the time extent of the given LiDAR file. The actual geographic extent of the LiDAR point cloud varies dependent on line-of-sight.

Compressed (LAZ format) point cloud files may be requested by emailing gis@detroitmi.gov with a description of the desired geographic area, any specific dates/file names, and an explanation of interest and/or intended use. Requests will be filled at the discretion and availability of the Enterprise GIS Team. Deliverable file size limitations may apply and requestors may be asked to provide their own online location or physical media for transfer.

LiDAR was collected using an uncalibrated Trimble MX2 mobile mapping system. The data is not quality controlled, and no accuracy assessment is provided or implied. Results are known to vary significantly. Users should exercise caution and conduct their own comprehensive suitability assessments before requesting and applying this data.

Sample Dataset: https://detroitmi.maps.arcgis.com/home/item.html?id=69853441d944442f9e79199b57f26fe3

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2014 Mobile County, Alabama lidar survey. Beach width is included and is defined as the distance between the dune toe and shoreline along a cross-shore profile. The beach slope is calculated using this beach width and the elevation of the shoreline and dune toe.

These data provide an accurate high-resolution shoreline compiled from imagery of PORT OF MOBILE, AL . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://www.fisheries.noaa.gov/inport/item/39808

A digital elevation model (DEM) of a portion of the Mobile-Tensaw Delta region and Three Mile Creek in Alabama was produced from remotely sensed, geographically referenced elevation measurements by the U.S. Geological Survey (USGS). Elevation measurements were collected over the area (bathymetry was irresolvable) using the Experimental Advanced Airborne Research Lidar (EAARL), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 50 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters with an average point spacing of 2-3 meters. The EAARL, developed originally by the National Aeronautics and Space Administration (NASA) at Wallops Flight Facility in Virginia, measures ground elevation with a vertical resolution of +/-15 centimeters. A sampling rate of 3 kilohertz or higher results in an extremely dense spatial elevation dataset. Over 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development. For more information on Lidar science and the Experimental Advanced Airborne Research Lidar (EAARL) system and surveys, see http://ngom.usgs.gov/dsp/overview/index.php and http://ngom.usgs.gov/dsp/tech/eaarl/index.php .

Tree diameter and coordinate data obtained by iPhone, iPad, and conventional survey methods in a 1 ha forest plot in Hokkaido, Japan (42°59'57" N, 141°23'29" E). Tatsumi, Yamaguchi, Furuya (in press) ForestScanner: A mobile application for measuring and mapping trees with LiDAR-equipped iPhone and iPad. Methods in Ecology and Evolution.

We present The Newer College Dataset with a variety of mobile mapping sensors handcarried at typical walking speeds through New College, Oxford for nearly 6.7 km. The dataset uses two different devices made up of commercially available sensors. These datasets contain some challenging sequences such as fast motion, aggressive shaking, rapid lighting change, and textureless surface.

Work in progress: data might be changed The data set contains the locations of public roadside parking spaces in the northeastern part of Hanover Linden-Nord. As a sample data set, it explicitly does not provide a complete, accurate or correct representation of the conditions! It was collected and processed as part of the 5GAPS research project on September 22nd and October 6th 2022 as a basis for further analysis and in particular as input for simulation studies. Vehicle Detections Based on the mapping methodology of Bock et al. (2015) and processing of Leichter et al. (2021), the utilization was determined using vehicle detections in segmented 3D point clouds. The corresponding point clouds were collected by driving over the area on two half-days using a LiDAR mobile mapping system, resulting in several hours between observations. Accordingly, these are only a few sample observations. The trips are made in such a way that combined they cover a synthetic day from about 8-20 clock. The collected point clouds were georeferenced, processed, and automatically segmented semantically (see Leichter et al., 2021). To automatically extract cars, those points with car labels were clustered by observation epoch and bounding boxes were estimated for the clusters as a representation of car instances. The boxes serve both to filter out unrealistically small and large objects, and to rudimentarily complete the vehicle footprint that may not be fully captured from all sides. Figure 1: Overview map of detected vehicles Parking Areas

Acquisition of helicopter-based lidar data was accomplished with a Riegl VQ480II device. Due to the cold temperatures, a specially constructed externally front-mounted sensor pod with heaters and insulation was designed and deployed. The lidar sensor was configured to provide a maximum measurement range of 1,000 m, and an operating flight altitude of up to 530 m above ground level, at up to 6,400m elevation The raw lidar data (output to .LAS 1.2 or 1.4 format) and IMU trajectories were post-processed using Riegl RiProcess and Applanix POSPac Mobile Mapping Suite, respectively. RiProcess is proprietary Riegl software for processing raw lidar data into a .LAS 1.4 output file. The first step creates a trajectory from the IMU and GPS/GNSS data using the Applanix POSPac software into an SBET file, which is then combined with the .RPX files from the Riegl VQ480II lidar scanner. Ground control points were brought into the processing workflow during the alignment of the .RPX and SBET files to provide additional accuracy beyond that achieved with the on board GPS/GNSS. Further alignment of each individual scan line is processed through a combination of the Riegl RiPrecision software extension and BayesMap StripAlign. This Dryad repository contains an airborne lidar dataset of the Khumbu Glacier from the base of the Lhotse Face in the Western Cwm to the trekking village of Dugla at the toe of the glacier. The data were collected by helicopter borne lidar on 27 May 2019 to 28 May 2019. The data were collected as part of the National Geographic and Rolex Perpetual Planet Everest Expedition. This research was conducted in partnership with National Geographic Society, Rolex, Tribhuvan University, and the Nepal Survey Department, with approval from all relevant agencies of the Government of Nepal. Note that there is sparser point density in some of the snow and ice areas of the glacier, higher density in rock and rubble covered areas. The data are provided for scientific use and should credit the National Geographic and Rolex’s Perpetual Planet Everest Expedition and National Geographic Society.

Beaver Lake was constructed in 1966 on the White River in the northwest corner of Arkansas for flood control, hydroelectric power, public water supply, and recreation. The surface area of Beaver Lake is about 27,900 acres and approximately 449 miles of shoreline are at the conservation pool level (1,120 feet above the North American Vertical Datum of 1988). Sedimentation in reservoirs can result in reduced water storage capacity and a reduction in usable aquatic habitat. Therefore, accurate and up-to-date estimates of reservoir water capacity are important for managing pool levels, power generation, water supply, recreation, and downstream aquatic habitat. Many of the lakes operated by the U.S. Army Corps of Engineers are periodically surveyed to monitor bathymetric changes that affect water capacity. In October 2018, the U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, completed one such survey of Beaver Lake using a multibeam echosounder. The echosounder data was combined with light detection and ranging (lidar) data to prepare a bathymetric map and a surface area and capacity table. Collection of bathymetric data in October 2018 at Beaver Lake near Rogers, Arkansas, used a marine-based mobile mapping unit that operates with several components: a multibeam echosounder (MBES) unit, an inertial navigation system (INS), and a data acquisition computer. Bathymetric data were collected using the MBES unit in longitudinal transects to provide complete coverage of the lake. The MBES was tilted in some areas to improve data collection along the shoreline, in coves, and in areas that are shallower than 2.5 meters deep (the practical limit of reasonable and safe data collection with the MBES). Two bathymetric datasets collected during the October 2018 survey include the gridded bathymetric point data (BeaverLake2018_bathy.zip) computed on a 3.28-foot (1-meter) grid using the Combined Uncertainty and Bathymetry Estimator (CUBE) method, and the bathymetric quality-assurance dataset (BeaverLake2018_QA.zip). The gridded point data used to create the bathymetric surface (BeaverLake2018_bathy.zip) was quality-assured with data from 9 selected resurvey areas (BeaverLake2018_QA.zip) to test the accuracy of the gridded bathymetric point data. The data are provided as comma delimited text files that have been compressed into zip archives. The shoreline was created from bare-earth lidar resampled to a 3.28-foot (1-meter) grid spacing. A contour line representing the flood pool elevation of 1,135 feet was generated from the gridded data. The data are provided in the Environmental Systems Research Institute shapefile format and have the common root name of BeaverLake2018_1135-ft. All files in the shapefile group must be retrieved to be useable.

These files are to support the published journal and thesis about the IMU and LIDAR SLAM for indoor mapping. They include datasets and functions used for point clouds generation. Date Submitted: 2022-02-21

Forest operations can cause long-term soil disturbance, leading to environmental and economic losses. Mobile LiDAR technology has become increasingly popular in forest management for mapping and monitoring disturbances. Low-cost mobile LiDAR technology, in particular, has attracted significant attention due to its potential cost-effectiveness, ease of use, and ability to capture high-resolution data. The LiDAR technology, which is integrated in the iPhone 13–14 Pro Max series, has the potential to provide high accuracy and precision data at a low cost, but there are still questions on how this will perform in comparison to professional scanners. In this study, an iPhone 13 Pro Max equipped with SiteScape and 3D Scanner apps, and the GeoSlam Zeb Revo scanner were used to collect and generate point cloud datasets for comparison in four plots showing variability in soil disturbance and local topography. The data obtained from the LiDAR devices were analyzed in CloudCompare using the Iterative Closest Point (ICP) and Least Square Plane (LSP) methods of cloud-to-cloud comparisons (C2C) to estimate the accuracy and intercloud precision of the LiDAR technology. The results showed that the low-cost mobile LiDAR technology was able to provide accurate and precise data for estimating soil disturbance using both the ICP and LSP methods. Taking as a reference the point clouds collected with the Zeb Revo scanner, the accuracy of data derived with SiteScape and 3D Scanner apps varied from RMS = 0.016 to 0.035 m, and from RMS = 0.017 to 0.025 m, respectively. This was comparable to the precision or repeatability of the professional LiDAR instrument, Zeb Revo (RMS = 0.019–0.023 m). The intercloud precision of the data generated with SiteScape and 3D Scanner apps varied from RMS = 0.015 to 0.017 m and from RMS = 0.012 to 0.014 m, respectively, and were comparable to the precision of Zeb Revo measurements (RMS = 0.019–0.023 m). Overall, the use of low-cost mobile LiDAR technology fits well to the requirements to map and monitor soil disturbances and it provides a cost-effective and efficient way to gather high resolution data, which can assist the sustainable forest management practices.

Datasets containing 2D images from roads. The images were either rendered from 3D lidar point clouds or captured by a camera on a so called Mobile Mapping vehicle. For more information on how to use this dataset, visit the following repository:

https://github.com/Snagnar/CompetitiveReconstructionNetworks