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222 results for “point cloud”
Point cloud data from terrestrial laser scanning for stem volume modelling of Scots pine trees
<p>Stem volume is a key forest inventory attribute characterizing growth and yield of individual trees and forest stands. Three-dimensional information from terrestrial laser scanning (TLS) can be used to reconstruct tree stems and provide information on stem volume as well as stem shape. We collected diameter at breast height and height information with traditional field measurements as well as preprocessed TLS point cloud data on 230 Scots pine trees (<em>Pinus sylvestris L.</em>) from southern Finland. The data set here includes three-dimensional information on Scots pine tree stems derived from TLS point clouds. The usage of this data set can include, but is not limited to, development of point cloud processing algorithms for single tree stem reconstruction and investigations of of stem volume modelling for Scot pine. </p> <p>This data set includes two files: Scots_pines.txt includes DBH and height information based on field measurements from the 230 Scots pine trees. File includes the following columns: treeID, DBH, and h, where DBH is presented in cm and h (i.e. tree height) in m. Stem_points.zip, on the other hand, includes 230 laz-files where figure in the name of the laz-file refers to the tree ID in Scots_pines.txt-file. Laz-files include three columns that describe x, y, and z, coordinates (in meters) of stem points in a local coordinate system extracted from the normalized TLS point clouds (i.e. z coordinate describes height above ground).</p>
LAUTx - Individual Tree Point Clouds From Austrian Forest Inventory Plots
<p>This dataset contains manually segmented tree point clouds from Personal Laser Scanning (PLS) data, and additionally automatic segmented trees from the same point clouds. The raw point cloud data has been published in LAUT - Terrestrial and Personal laser scanner data from Austrian forest Inventory plots (<a href="https://doi.org/10.5281/zenodo.3698956">https://doi.org/10.5281/zenodo.3698956</a>) and six of those plots were processed for this data. Purpose of this data is to serve as benchmarking for automatic tree segmentation algorithms.</p>
Processing of 3-D Polygon Mesh Model and Radio Propagation Simulations in a Cave: Surface Reconstruction from Point Cloud, Simplification of the Mesh, and Ray Tracing
<p><strong>ABOUT</strong></p><p>This repository includes mesh data from cave geometry scanning and processing, and radio propagation data from ray tracing simulations.</p><p>The geometry data is obtained with laser scanning in a cave in Slovenija. </p><p>The geometry processing includes (i) 3-D shape reconstruction - surface reconstruction from point cloud data and (ii) simplification - reduction of the geometric complexity of the 3-D mesh model. </p><p>The radio propagation data is obtained using CloudRT [1] ray-tracing simulator. </p><p>The obtained propagation-related quantities include information about the propagation mechanism, interactions with the geometry, received power, delay, azimuth and elevation angles of arrival and departure, and path loss. </p><p> </p><p><strong>AUTHORS</strong></p><p>Teodora Kocevska, Andrej Hrovat, Tomaž Javornik</p><p>Department of Communication Systems</p><p>Jožef Stefan Institute, SI-1000 Ljubljana, Slovenia</p><p>teodora.kocevska@ijs.si</p><p> </p><p><strong>GEOMETRY PROCESSING</strong></p><p>The cave segment used for the propagation calculations is selected from a point cloud obtained in a cave in Litia, Slovenia. The point cloud is obtained with 3-D laser scanning of the environment. The selected segment is approx. 58 m long. Several parameter configurations were considered for 3-D shape reconstruction, including Poisson surface reconstruction with octree depths of 8, 10, and 12. Geometries that represent the cave shape and have different levels of complexity were created and studied. In the simplification process, one and two-stage simplification was explored using the Quadric Edge Collapse Decimation approach. </p><p> </p><p><strong>RADIO SETUP</strong></p><p>The transmitter (Tx) is fixed at the entrance of the cave and the receiver (Rx) is moved along the cave in 40 positions with a step of 1 m.</p><p>Omnidirectional antennas at the Tx and Rx sites and vertical polarization are considered. The antenna is mounted 1.5 m above the ground.</p><p>The start frequency is 3.5 GHz, the end frequency is 3.6 GHz and the step is 10 MHz. Direct propagation and first-order reflection are considered. </p><p>The cave geometry is represented by a triangular mesh, and the material of the cave is wet earth. The material electromagnetic properties are selected according to the specifications presented in [2].</p><p> </p><p><strong>FOLDER STRUCTURE</strong></p><p>The folder structure is:</p><p> - Polygon_Mesh_Models</p><p> <i># 3-D environment models with varying </i>levels<i> of geometry complexity</i></p><p> - Reconstruction_Segmen1_Poisson_Surface_Reconstruction</p><p> - Simplification_Segment1_Quadric_Edge_Collapse_Decimation</p><p> - Propagation_Data</p><p> <i># Propagation quantities of all rays between a transmitter and receiver</i></p><p> - AllRay_PropData</p><p> - PathLoss</p><p> - readme.txt</p><p> - RayTracing_EnvironmentModel</p><p> <i> # Final environment model used for ray tracing simulations</i></p><p> - Cave_MeshModel.json</p><p> - Cave_MeshModel.skb</p><p> - Cave_MeshModel.skp</p><p> - RayTracing_MaterialProperties</p><p> <i># Properties of the materials in the environment</i></p><p> - materials.json</p><p> - materials.mtl</p><p> - readme.txt</p><p> - Cave_Length.txt</p><p> <i># Length between selected locations in the environment</i></p><p> - Cave_Segment1_visual.png</p><p> <i> # Visualization of the environment segment used for propagation calculation</i></p><p> - readme.txt</p><p> <i># Overall description </i></p><p><strong>REFERENCES</strong></p><p>[1] D. He, B. Ai, K. Guan, L. Wang, Z. Zhong, and T. Kürner, "The Design and Applications of High-Performance Ray-Tracing Simulation Platform for 5G and Beyond Wireless Communications: A Tutorial," in IEEE Communications Surveys & Tutorials, vol. 21, no. 1, pp. 10-27, First quarter 2019, doi: 10.1109/COMST.2018.2865724.</p><p>[2] R. sector of International Telecommunication Union (ITU-R), "Effects of building materials and structures on radio wave propagation above about 100 MHz," International Telecommunication Union, ITU-R Recommendation P.2040-2, 2021.</p><p> </p><p><strong>ACKNOWLEDGEMENT</strong></p><p>This work was supported by the Slovenian Research Agency under grant <strong>J2-3048</strong>.</p><p> </p>
New Challenges in Point Cloud Visual Quality Assessment: A Systematic Review (Dataset)
<p>This dataset is a collection of annotated information on the scientific papers screened and analyzed for the systematic review of the literature in Point Cloud Visual Quality Assessment. </p> <p>The data is structured as follows:</p> <ul> <li>General information <ul> <li>Document title</li> <li>Authors</li> <li>Year of publication</li> <li>Venue (Conference or Journal title)</li> <li>Citations (number)</li> <li>URL/DOI</li> </ul> </li> </ul> <ul> <li>About the content <br> <ul> <li>Content Type: Point clouds (PC), Colored Point clouds (CPC), Meshes, Dynamic Point Clouds (DPC)</li> <li>Content source: Source of the content used in a subjective QA test or the evaluation of one or more QA metrics</li> </ul> </li> </ul> <ul> <li>About metric benchmarks <ul> <li>Subjective Ground-truth Data: Dataset(s) Source of the subjective scores used as ground-truth in a QA metric benchmark</li> <li>Assessed Metrics: Types of metrics assessed in a benchmark (JPEG standards, IQM, NR, State-of-the-art, others)</li> <li>Performance Measures: PLCC, SROCC, KRCC, RMSE, OR, others</li> </ul> </li> </ul> <ul> <li>About Objective QA metrics <ul> <li>Metric: Name given to the metric introduced in this paper</li> <li>Base: 3D-based or Projection-based</li> <li>Categories: Categories that characterize the approach of the proposed metric (Feature-based, Learning-Based, Perceptual-based, IQM, others) </li> <li>Reference: Full-Reference (FR), Reduced-Reference (RR) or No-Reference (NR)</li> </ul> </li> </ul> <ul> <li>About Subjective QA experiments <ul> <li>Display: Type of display (2D, 3D, AR, MR, VR) and interaction approach (passive, interactive, 3DoF, 6DoF) used in the described experiment.</li> <li>Rendering: Type of rendering used to display the stimuli (Points, Squares, Cubes, Surface)</li> <li>Lab/Remote: The experiment was run in one or more lab environments, or remotely (Lab, Cross-Lab, Remote)</li> <li>Rating: Subjective rating methodology used in the experiment (ACR, DSIS, PWC, others)</li> <li>Dataset: Name of the new subjective dataset if the experiment's results were published.</li> <li>Observers: Number of observers </li> <li>Distortion type: Types of distortions applied to the stimuli and assessed in the experiment</li> </ul> </li> </ul>
LigPCDS: Labeled Dataset of X-ray Protein Ligand Images in 3D Point Cloud and Validated Deep Learning Models
<p>The difference electron density from X-ray protein crystallography was used to create the first dataset of labeled ligand images in 3D point clouds, named <strong>LigPCDS</strong>. The dataset contain 244,226 entries of free organic ligands containing 3D representations labeled with two major labeling approaches: SP-based and AtomSymbol-based.</p> <p> </p> <p>The data from free organic molecules (non-covalent ligands) was retrieved from the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) in december 2019 with resolutions ranging from 1.5 to 2.2 Å. The ligand images (blobs) were interpolated from their calculated difference electron density map in a 3D grid-like bounding box, around their atomic positions, and stored in point clouds. These ligand grid representations were further processed to retrive the final ligands representation in 3D point clouds using a mask of the shape of the ligand. A grid spacing of 0.5 Å gave the best results. The density value of the grid points was used as feature. The labeling approach used the structure of the ligands to propose vocabularies of chemical classes based on the chemical atoms themselves and their cyclic substructures. These structure annotations were applied pointwise to the ligand 3D representations using an atomic sphere model. Four proposed vocabularies were validated by successfully training good performance deep learning models for the semantic segmentation of a stratified dataset from LigPCDS, using 78902 entries.</p> <p>The four validated deep learning models are: (i) the LigandRegion, composed by generic atoms of any type; (ii) the AtomCycle, composed by generic atoms outside cycles and generic cycles; (iii) the AtomC347CA56, composed by generic atoms outside cycles, not aromatic cycles of size 3 to 7 and aromatic cycles of size 5 and 6; and (iv) the AtomSymbolGroups, composed by the atoms symbols with groupings. The mean accuracy of these models in their cross-validation was between 49.7% <span lang="EN-GB">[-19.4,20.</span><span lang="EN-GB">2]</span> and 77.4% <span lang="EN-GB">[-11.7,12.1]</span> in terms of Intersection over Union (mIoU) metric and between 62.4% <span lang="EN-GB">[-18.8,19.</span><span lang="EN-GB">7]</span> and 87.0% <span lang="EN-GB">[-8.4,8.8]</span> in F1-score (mF1), confidence interval between squared brackets. The models i, ii and iii and the used labeled representations in 3D point cloud are contained in the SP-based record; and model iv and its used labeled representations are contained in the AtomSymbol-based record.</p> <p>The dataset and validated models may be used to tackle problems regarding known and unknown ligand building to drug discovery and fragment screening pipelines. </p> <p>The code used to create and validated the LigPCDS is available at the following repository: https://github.com/danielatrivella/np3_ligand</p> <p>This repository also contains the NP³ Blob Label application for ligand building using the validated deep learning models from LigPCDS.</p>
Hong Kong Annotated Airborne LiDAR Point Clouds
<p>The annotated point clouds were generated to train the weakly supervised semantic segmentation algorithm Semantic Query Network (SQN) to classify point clouds <sup>[1]</sup>. The dataset covers 16 tiles of airborne LiDAR data in an area of 7.2 km2 in Shatin, Hong Kong, China. 11 tiles were used for training, while 5 tiles were used for validation. There are multiple types of construction in the dataset including high-rise residential buildings, low-rise village houses, and large public buildings. Green spaces are mainly composed of wood areas in open spaces (e.g., in parks and hills) and planted trees in residential gardens and nearby roads. Point clouds are classified in ground, buildings, and trees.</p> <p>The LiDAR data is owned by the Hong Kong government. Please visit the Spatial Data Portal, Survey Division, CEDD (https://sdportal.cedd.gov.hk/#/en/) for more details.</p> <p> </p>
RGB orthophoto mosaic, DSM, 3d point cloud and LIDAR LAZ of the flash flood damages in Karavelovo and Bogdan vilages, Bulgaria- September 2, 2022
<p>The present dataset contains geospatial resources aimed at investigating and assessing the consequences of a flash flood of debris flow character, relatively significant in extent and magnitude of damage, in the area of two villages in the Municipality of Karlovo, located in central Bulgaria, which happened on September 2, 2022. For this purpose, an integrated approach based on the combination of digital photogrammetry with high spatial resolution and spatial accuracy, based on a fixed wing unmanned aerial system, and laser altimetry (LIDAR), based on a multirotor unmanned platform, was used. The data collection was carried out 2 days after the occurrence of the disaster, resulting in the generation of valuable information resources that allow not only to spatially and quantitatively determine the damage of the disaster, but also to reveal the mechanism of occurrence of the phenomenon: 1) orthophoto mosaic, Digital surface model-DSM and 3D point cloud (from photogrammetry) 2) Classified 3D point cloud- from LIDAR survey.</p>
Structural Diversity from the NEON Discrete-Return LiDAR Point Cloud in 2013-2022
Structural diversity, characterizing the volumetric capacity and physical arrangement of biotic components in an ecosystem, controls critical ecosystem functions like light interception, hydrology, and microclimate. This product generates structural diversity metrics for the NEON sites, sourced from the Discrete-Return LiDAR Point Cloud from the NEON Aerial Observation Platform (DP1.30003.001; collected in March 2023). Using R programming, we computed the metrics detailing height, heterogeneity, and density at 30 m, aligned to the Landsat grids, for 243 site years in 57 NEON sites from 2013 to 2022.
Sparse point clouds derived from UAV overflights of the fifteen NPP study sites at Jornada Basin LTER in 2019
This data package contains sparse point clouds for each of the 15 NPP study sites at the Jornada Basin LTER in southern New Mexico, USA. The point clouds were derived from raw images collected during uncrewed aerial vehicle (UAV) overflight missions conducted in late summer and early autumn of 2019 (1 overflight day per site). For each site one or two missions were flown during an afternoon using a DJI Phantom 4 UAV, and between 450 and 1300 12.4 megapixel RGB images were captured. A subset of images captured at each site were loaded into Agisoft Metashape software to derive points using a structure from motion method. The raw images are not provided but can be made available via project PIs. This data package includes point clouds for all 15 sites as geotiff raster files. Other derived products from these UAV missions, including orthomosaic photos, digital elevation models, and digital terrain models, are available in other EDI data packages (knb-lter-jrn.210543001, knb-lter-jrn.210543002, and knb-lter-jrn.210543003, respectively). This study is complete.
Photogrammetric Point Cloud and DSM from UAV campaign at Niwot Ridge, 2017.
Elevation data from 14 August 2017 collected as part of unmanned aerial vehicle (UAV)/drone campaign during Summer 2017. Investigating snow depth varaibility and spatiotemporal variations and controls on vegetation productivity within the Niwot Ridge LTER Saddle Catchment.
Sila National Park - 3D Point cloud data
<p>This dataset contains 3 types of data.</p> <ul> <li>GPS data (the ones starting with <em>"GPS"</em>) of sampling plot centers collected with a Trimble GPS and post processed to ensure positioning errors lower than 2 meters.</li> <li>TLS data, (the ones starting with <em>"ID_"</em>): such data were collected in the end of August 2019 with a mobile terrestrial laser scanner (mobile ZEB TLS) in a squared area of approximatively 30x30m. Data have been normalized using TreeLS package in R.</li> <li>ALS data collected in the end of July 2019. For the entire study area, we upload 2 different ALS data: "<em>merged.las</em>" is the original point cloud; "<em>myLas_norm_lt22.las</em>" is the normalised point cloud, cut at 22 meters from the ground in order to perform specific analysis (i.e. paper under submission).</li> </ul> <p>Data collection was founded by the <em>AGRIDIGIT Selvicoltura</em> project.</p>
Detailed point cloud data on stem size and shape of Scots pine trees
<p>This data set is comprised of three packed zip files and they include text files of 3D information from terrestrial laser scanning (TLS) and aerial imagery from unmanned aerial vehicle (UAV) from individual Scots pine trees within 27 sample plots from three test sites located in southern Finland.</p> <p>TLS data acquisition was carried out with Trimble TX5 3D laser scanner (Trible Navigation Limited, USA) for all three study sites between September and October 2018. Eight scans were placed to each sample plot and scan resolution of point distance approximately 6.3 mm at 10-m distance was used. Artificial constant sized spheres (i.e. diameter of 198 mm) were placed around sample plots and used as reference objects for registering the eight scans onto a single, aligned coordinate system. The registration was carried out with FARO Scene software (version 2018). Aerial images were obtained by using an UAV with Gryphon Dynamics quadcopter frame. Two Sony A7R II digital cameras were mounted on the UAV in +15° and -15° angles. Images were acquired in every two seconds and image locations were recorded for each image. The flights were carried out on October 2, 2018. For each study site, eight ground control points (GCPs) were placed and measured. Flying height of 140 m and a flying speed of 5 m/s was selected for all the flights, resulting in 1.6 cm ground sampling distance. Total of 639, 614 and 663 images were captured for study site 1, 2, and 3, respectively, resulting in 93% and 75% forward and side overlaps, respectively. Photogrammetric processing of aerial images was carried out following the workflow as presented in Viljanen et al. (2018). The processing produced photogrammetric point clouds for each study site with point density of 804 points/m<sup>2</sup>, 976 points/m<sup>2</sup>, and 1030 points/m<sup>2</sup> for study site 1, 2, and 3, respectively.</p> <p>The sample plots within the three test sites have been managed with different thinning treatments in either 2005 or 2006. The experimental design of the sample plots includes two levels of thinning intensity and three thinning types resulting in six different thinning treatments, namely i) moderate thinning from below, ii) moderate thinning from above, iii) moderate systematic thinning, iv) intensive thinning from below, v) intensive thinning from above, and vi) intensive systematic thinning, as well as a control plot where no thinning has been carried out since the establishment. More information about the study sites and samples plots as well as the thinning treatments can be found in Saarinen et al. (2020a).</p> <p>The data set includes stem points of individual Scot pine trees extracted from the point clouds. More about the method of extraction can be found in Saarinen et al. (2020a, 2020b) and Yrttimaa et al. (2020). The title of the zip file refers to the study sites 1, 2, and 3. The title of the text files includes the information on the test site, the plot within the test site, and the tree within the plot. The text files contain stem points extracted from the TLS point clouds. The columns “x” and “y” contain x- and y-coordinates in a local coordinate system (in meters), in column “h” is the height of each point in meters above ground, and treeID is the tree identification number. The columns are separated by space.</p> <p>Based on the study site and plot number, files from different thinning treatments can be identified by using the information in Table 1 in Saarinen et al. (2020b).</p> <p> </p> <p><strong>References</strong></p> <p>Saarinen, N., Kankare, V., Yrttimaa, T., Viljanen, N., Honkavaara, E., Holopainen, M., Hyyppä, J., Huuskonen, S., Hynynen, J., Vastaranta, M. 2020a. Assessing the effects of stand dynamics on stem growth allocation of individual Scots pines. bioRxiv 2020.03.02.972521. <a href="https://doi.org/10.1101/2020.03.02.972521">https://doi.org/10.1101/2020.03.02.972521</a></p> <p>Saarinen, N., Kankare, V., Yrttimaa, T., Viljanen, N., Honkavaara, E., Holopainen, M., Hyyppä, J., Huuskonen, S., Hynynen, J., Vastaranta, M. 2020b. Detailed point cloud data on stem size and shape of Scots pine trees. bioRxiv 2020.03.09.983973. <a href="https://doi.org/10.1101/2020.03.09.983973">https://doi.org/10.1101/2020.03.09.983973</a></p> <p>Viljanen, N., Honkavaara, E., Näsi, R., Hakala, T., Niemeläinen, O., Kaivosoja, J. 2018. A Novel Machine Learning Method for Estimating Biomass of Grass Swards Using a Photogrammetric Canopy Height Model, Images and Vegetation Indices Captured by a Drone. Agriculture 8: 70. <a href="https://doi.org/10.3390/agriculture8050070">https://doi.org/10.3390/agriculture8050070</a></p> <p>Yrttimaa, T., Saarinen, N., Kankare, V., Hynynen, J., Huuskonen, S., Holopainen, M., Hyyppä, J., Vastaranta, M. 2020. Performance of terrestrial laser scanning to characterize managed Scots pine (<em>Pinus sylvestris</em> L.) stands is dependent on forest structural variation. EarthArXiv. March 5. <a href="https://doi.org/10.31223/osf.io/ybs7c">https://doi.org/10.31223/osf.io/ybs7c</a></p>
Terrestrial laser scanning - RIEGL VZ-1000, individual tree point clouds and cylinder models, Belgian hedgerows and tree rows
<p>Terrestrial laser scans were acquired for 69 trees (<em>Quercus robur</em>: 39 trees; <em>Alnus glutinosa</em>: 19 trees; <em>Betula pendula: </em>11 trees) in hedgerows and tree rows in agricultural lands in Flanders, Belgium. We used a RIEGL VZ-1000 terrestrial laser scanner (RIEGL Laser Measurement Systems GmbH, Austria) with a beam divergence of 0.35 mrad operating in the infrared (wavelength 1550 nm) with a range up to 1000 m. We scanned leaf-off and all recorded variables are valid for overbark measurements. Individual trees were manually extracted from the co-registered point cloud in RiSCAN PRO software (provided by RIEGL). To the extracted trees, quantitative structure models (QSM) were fitted. We used the QSMs to derive branch length (m), total wood volume (m³) and merchantable wood volume (m³, using only cylinders with diameter > 7 cm). From the point clouds, we extracted the tree structural features such as crown projection (m²), maximum crown diameter (m) and tree height (m). Biomass expansion factors (BEF) were calculated by dividing total tree volume to merchantable tree volume. We expressed the age dependency of the BEF values via non-linear regression models. See Van Den Berge et al. (2021) for further information (DOI: 10.1007/s12155-021-10250-y).</p>
Precision viticulture dataset for detailed vineyard mapping composed of geotagged smartphone ground images, phytosanitary status, UAV orthomosaics, 3D point clouds, and RTK GNSS data - Northern Spain, July 2022
<p>This dataset offers a rich multimodal collection of data from vineyards, designed to enhance agricultural research with a focus on vineyard management and disease monitoring. It includes geotagged smartphone ground images in ".7z" format for detailed plant-level analysis, a ".csv" file detailing plants' phytosanitary status for health assessment, UAV-derived 3D Point Clouds and orthomosaics in ".las" and ".tiff" formats for aerial landscape views, and RTK GNSS data in ".shp" format for precise plant geolocations.</p> <p>This dataset can be combined with other datasets to enable a comprehensive view of the vineyards and improve its value:</p> <div> <ul> <li>Ariza-Sentís, Mar, Sergio Vélez, and João Valente. ‘Dataset on UAV RGB Videos Acquired over a Vineyard Including Bunch Labels for Object Detection and Tracking’. <em>Data in Brief</em> 46 (February 2023): 108848. <a href="https://doi.org/10.1016/j.dib.2022.108848">https://doi.org/10.1016/j.dib.2022.108848</a>.</li> <li>Vélez, Sergio, Mar Ariza-Sentís, and João Valente. ‘VineLiDAR: High-Resolution UAV-LiDAR Vineyard Dataset Acquired over Two Years in Northern Spain.’ <em>Data in Brief</em>, October 2023, 109686. <a href="https://doi.org/10.1016/j.dib.2023.109686">https://doi.org/10.1016/j.dib.2023.109686</a>.</li> <li> <div> <div>Vélez, Sergio, Mar Ariza-Sentís, and João Valente. ‘Dataset on Unmanned Aerial Vehicle Multispectral Images Acquired over a Vineyard Affected by Botrytis Cinerea in Northern Spain’. <em>Data in Brief</em> 46 (February 2023): 108876. <a href="https://doi.org/10.1016/j.dib.2022.108876">https://doi.org/10.1016/j.dib.2022.108876</a>.</div> <div> </div> </div> </li> </ul> </div>
Mangrove terrestrial laser scanning (TLS) point clouds and quantitative structural models (QSMs)
<p>Datasets for a publication entitled, "Terrestrial laser scanning for the estimation of above ground biomass of mangrove roots by modelling them as inverted trees."</p> <p>See the file "Data dictionary for Mangrove terrestrial laser scanning.pdf" for a description of the datasets included in the zipped folder. </p>
Large-scale 3D building and tree datasets constructed from airborne LiDAR point clouds in Glasgow, UK
<p>This is the updated version of building 3D model data. The revision includes appending attributes to the lod1 and lod2 shapefile and creating cityjson file for each 3D building model. All 3D building models are available in mesh (.obj), multipath shapefile, and cityjson (.json) now.</p> <p><strong>IMPORTANT NOTE: We suggest using the building footprint, lod1, and lod2 data of this version (Version v4).</strong></p> <p>Urban Big Data Centre of the University of Glasgow generates 3D city models via the airborne LiDAR point clouds acquired between 2020-2021 on behalf of Glasgow City Council. It is a large-scale 3D city model containing 3D information on terrain, trees, and buildings in Glasgow City. This dataset comprises terrain, tree canopy, and building products derived from high-density airborne LiDAR point clouds. </p> <p>The terrain products include Digital Terrain Model (DTM), Digital Surface Model (DSM), and normalized Digital Surface Model (nDSM) in 0.5 m spatial resolution. The DTM and DSM rasters were provided by the vendor and nDSM rasters were obtained by subtracting DTM from DSM. Terrain products are provided in 5 km by 5 km GeoTIF format raster.</p> <p>The tree canopy products are composed of canopy height models (CHM) and tree top locations. Classified tree point clouds were applied with pit-free algorithm to generate CHM in 0.5 m grid raster in GeoTIF format [1]-[2]. Treetop locations were identified by using Local Maximum Filter based on CHM and are recorded as points in Shapefile format. The tree canopy products are provided in 5 km by 5 km tiles.</p> <p>Building 3D model products include footprint polygons with building height attributes and 3D mesh of building models in LoD1 and LoD2 levels. A series of processes such as converting building point clouds to building height models (BHM), converting BHM to polygons, and polygon regularization were conducted to obtain the building footprint polygons. Building height attributes were calculated from BHM for each footprint. The building footprint data are provided in Shapefile format. LoD1 models were generated based on the footprint and average height of the building. LoD2 models were constructed based on footprint and building point cloud with City3D tool[3]. LoD1 and LoD2 models are provided in OBJ and shapefile format. Building 3D model products are provided in 5 km by 5 km tiles. The RMSE of Euclidean distances between each point in the point cloud to the reconstructed model was calculated to evaluate the LoD2 model construction. A table of RMSE and a note for a few problematic models are provided.</p>
Point clouds from terrestrial laser scanning from crowns of individual Scots pine trees
<p>Trees adapt to their growing conditions by regulating the sizes of their parts and their relationships. For example, removal or death of adjacent trees increases the growing space and the amount of light received by the remaining trees enabling their crowns to expand. Knowledge about the effects of silvicultural practices on crown size and shape as well as about the quality of branches affecting the shape of a crown is, however, still limited. Laser scanning (or Light detecting and ranging LiDAR) has provided new opportunities for characterizing trees in more detail in three-dimensional space. Especially terrestrial laser scanning (TLS) has increasingly been used in producing a variety of tree attributes. This data set includes 3D reconstruction of crowns of Scots pine (<em>Pinus sylvestris</em> L.) trees from sample plots with different thinning treatments. The thinning treatments include two intensities of thinning, three thinning types as well as control (i.e. no thinning treatment since the establishment). This data set can be used in developing point cloud processing algorithms for single tree crown characterization and for investigating variation in crown size and shape as well as the effects of various thinning treatments on crown size and shape of Scots pine trees grown in boreal forests.</p>
Automotive Lidar and Vibration: Resonance, Inertial Measurement Unit and Effects on the Point Cloud
<p>This data repository contains vibration tests of an Ouster OS1-64 lidar including a docker based python environment and documentation.</p> <p>It consists of movement data, Fotos, IMU data, pointclouds and a ground truth measurements with an Riegl VZ6000 laser scaner.</p> <p>For further details see the linked publication and the example.ipynb notebook.</p> <p><strong>Quick start</strong></p> <ul> <li> <p>download the repo</p> </li> <li> <p>unzip the archive</p> </li> <li> <p>install VS code with the remote development extension</p> </li> <li> <p>install docker desktop</p> </li> <li> <p>open the folder in a new VS code window</p> </li> <li> <p>Say "yes" to open the folder inside a docker container</p> </li> <li> <p>wait for the container to start</p> </li> <li> <p>open the example jupyter notebook</p> </li> </ul> <p><strong>Structure</strong></p> <blockquote> <pre>├── .devcontainer .... VS code devcontainer (arm64 and amd64)</pre> <pre>├── Acceleromenter_A_z_deflection .... Accelerometer data</pre> <pre>├── Foto .... Fotos of the setup</pre> <pre>│ ├── VZ6000 .... Fotos of the ground truth measurements</pre> <pre>│ └── test_setup .... Fotos of the test setup</pre> <pre>├── Notebook .... A jupyter notebook with examples</pre> <pre>├── OS1_64_IMU .... Ouster IMU data</pre> <pre>├── OS1_64_pointcloud .... point cloud data</pre> <pre>├── VZ6000_groundtruth .... Ground truth data from Riegl VZ6000</pre> <pre> └── targets .... contains 2 different sets of reference</pre> <pre>│ ├── scene_aligned_by_reflectors</pre> <pre>│ └── targets_aligned</pre> <pre>└── files.csv .... an overview of the files and meta data</pre> </blockquote>
Internal morphology point clouds of lunar pits
<p>This archive contains point clouds showing the internal geometry of six pits on the Moon. These point clouds were generated via manual feature matching in "oblique stereo pairs": pairs of Lunar Reconnaissance Orbiter Narrow Angle Camera (LROC NAC) images at two different off-nadir angles observing one wall of a pit under similar lighting conditions. These images have pixel scales of ~0.3-2.2 m/pixel, allowing the creation of point clouds with point spacing on the order of 5-10 m, depending on the density of identifiable features on the pit walls.</p> <p>The manually-generated point clouds from multiple stereo pairs have been merged together, and aligned to and merged with dense digital terrain models (DTMs) from more nadir-looking NAC stereo images where available, to produce point clouds that cover the upper walls, floors, and immediate surroundings of the pits.</p> <p>For a full description of the processing method, see Wagner and Robinson (2022), linked in this archive's metadata.</p> <p>This archive contains models for the following pits:<br> Lacus Mortis Pit (LMP)<br> Mare Ingenii Pit (MIP)<br> Mare Tranquillitatis Pit (MTP)<br> Marius Hills Pit (MHP)<br> Schlüter Crater Pit (SCP)<br> Southwest Mare Fecunditatis Pit (SWFP)</p>
Point cloud data sets of real and virtual Chenopodium alba
<p>This data set contains:</p> <p>- 5 annotated point clouds of real Chenopodium alba plants obtained from multi-view 2D camera imaging. Annotations consist of 5 classes: leaf blade, petiole, apex, main stem, branch. .txt files contain both 3D coordinates and annotations. .ply files are also provided for raw 3D point data without annotations.</p> <p>- 24 annotated point clouds of virtual Chenopodium alba that were generated by a L-system simulation program. Annotations consist of 3 classes: leaf blade, petiole, stem. 3D coordinates and annotations are in separated .txt files. </p> <p>These files have been used in a companion paper.</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.