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222 results for “point cloud”
Point clouds from terrestrial laser scanning of 30 trees along Malet Street, London
<p>Point clouds of 30 street trees scanned along <a href="https://goo.gl/maps/7x3dutn6vHcxVPdC6">Malet Street, London, UK</a>. </p> <p>Tree species is predominantly London Plane (<em>Platanus × hispanica</em>).</p> <p>Data was captured on 8/2/2017 (leaf-off) with a RIEGL VZ-400 terrestrial laser scanner. 24 scans were conducted from 12 positions along the street. The weather was good, with little to no noticeable wind.</p> <p>Data is a binary PLY format with <em>xyz</em> fields in an arbitrary coordinate system. Trees have been extracted from the global point cloud and have been "cleaned" to remove the ground and neighbouring trees (however there may be some errors). Data has been downsampled to a voxel size of 0.04 m.</p> <p>Raw data can be accessed from here.</p> <p>Please acknowledge the data set authors if using this data.</p>
Point clouds from terrestrial laser scanning of 74 trees in Russell Square, London
<p>Point clouds of 74 trees scanned in <a href="https://www.google.co.uk/maps/place/Russell+Square/@51.5217533,-0.1280787,17z/data=!3m1!4b1!4m5!3m4!1s0x48761b310bb300bf:0xd1190a08b331324a!8m2!3d51.52175!4d-0.12589">Russell Square, London</a></p> <p>Tree species is predominantly London Plane (<em>Platanus × hispanica</em>).</p> <p>Data was captured on 8/2/2017 (leaf-off) with a RIEGL VZ-400 terrestrial laser scanner. 22 scans were conducted from 11 positions. The weather was good, with little to no noticeable wind.</p> <p>Data is a binary PLY format with <em>xyz</em> fields in an arbitrary coordinate system. Trees have been extracted from the global point cloud and have been "cleaned" to remove the ground and neighbouring trees (however there may be some errors). Data has been downsampled to a voxel size of 0.04 m.</p> <p>Raw data can be downloaded from <a href="https://doi.org/10.5281/zenodo.5070681">10.5281/zenodo.5070681</a></p> <p>Please acknowledge the data set authors if using this data.</p>
ULS and UAV-MS Directly Georeferenced Point Clouds of the River Teme
<p>Directly Georeferenced Point Clouds of the River Teme for PhD research, provided as supporting information to methods described. These are unfiltered point clouds of a vegetated river reach in the UK, given in WGS UTM_30N CRS. UAV-MS are SfM derived point clouds and ULS UAV laser scanning based point clouds.</p>
Vegetation Stratum Occupancy Prediction from Airborne LiDAR 3D Point Clouds
<p>Our proposed dataset comprises 199 cylindrical plots of 10 m radius corresponding to typical pasture land parcels in South-Eastern France. Each plot contains between 3000 and 17000 3D points, and each point is attributed with a total of 10 features: (i) absolute 3D coordinates, (ii) RGB and Near-InfraRed reflectance obtained with aerial cameras, (iii) uncalibrated laser intensity, return number and number of returns provided by the aerial LiDAR.</p> <p>This dataset can be used as training data for our model deep learning model "Vegetation Stratum Occupancy Prediction from Airborne LiDAR 3D Point Clouds" available on <a href="https://github.com/ekalinicheva/plot_vegetation_coverage">https://github.com/ekalinicheva/plot_vegetation_coverage</a></p> <p> </p>
UAV RGB imagery dataset captured at nadir and oblique angles over pistachio trees in Spain, including images, GCPs, 3D point cloud and orthomosaic.
<p>The dataset comprises 248 images taken in two flights on 29 July 2021 over a pistachio orchard in Spain. In addition, GCPs (ground control points) were collected to improve the photogrammetric process accuracy. The photos were taken using a UAV DJI Phantom Advance quadcopter equipped with a DJI FC6310 RGB 20-megapixel camera. The first flight mission was planned to take nadir images (-90º gimbal pitch degree), whereas the second flight was scheduled to take oblique images (-60º gimbal pitch degree), both at 55 metres above the ground. In addition, the images were used to generate a 3D point cloud, DEM and orthomosaic, which were included in the dataset.This dataset is useful for precision agriculture researchers interested in photogrammetric reconstruction.</p>
DEM, rock point clouds, and 3D morphological features of Martian rocks in the landing region of Zhurong rover
<p>Rocks on the Martian surface and their three-dimensional (3D) morphology record the geological evolution of Mars and its interaction history with outer space. China's Zhurong Mars rover has traveled on Mars for nearly 2 kilometers and obtained a wealth of data, enabling the fine-scale measurement and morphological analysis of 3D Martian rocks. With 178 high-resolution Zhurong NaTeCams stereo images acquired in rings covering large circular areas, we extracted 6,185 3D rocks from centimeter-scale to meter-scale distributed along the traverse of the Zhurong rover using an automatic approach. This dataset includes the digital elevation model (DEM), extracted rocks in point clouds, and the 3D morphological features of rocks derived from the Zhurong NaTeCams images, obtained in six areas along the traverse of the Zhurong rover.</p> <p>To use this dataset, please cite "Li, Y., Xiao, Z., Ma, C., Zeng, L., Zhang, W., Peng, M., & Li, A. (2023). Extraction and Analysis of Three‐dimensional Morphological Features of Centimeter‐scale Rocks in Zhurong Landing Region. <em>Journal of Geophysical Research: Planets</em>, e2022JE007656."</p>
Point clouds data "Waterfall height sets the mechanism and rate of upstream retreat"
<p>Point clouds data on the erosion shape of waterfalls in our experiment in the paper "Waterfall height sets the mechanism and rate of upstream retreat".</p>
UAV-based multispectral image data of a tree nursery, Eberswalde, Brandenburg, 2023 (Orthomosaics, DEMs, point clouds)
<p>This data set contains three multispectral surveys conducted with an unoccupied aerial vehicle (UAV DJI M300 RTK) of an oak tree nursery experiment</p> <ul> <li>Date of acquisition: 16.08.2023</li> <li>Location: Tree nursery, Eberswalde, Brandenburg, Germany</li> <li>UAV: DJI M300 RTK with active SAPOS connection</li> <li>Flight altitude above ground level: 30 m, 40 m, 60 m</li> <li>Image Overlap forward/side: 80 % / 80 %</li> <li>Camera: MicaSense Altum multispectral</li> <li>EPSG: 32632</li> </ul> <p>Data products: </p> <ul> <li>Orthomosaic multispectral (Resolution: 1.35 cm, 1.79 cm, 2.65 cm)</li> <li>Orthomosaic RGB</li> <li>Orthomosaic thermal LWIR in pseudo °C according to <a href="https://support.micasense.com/hc/en-us/articles/360022446473-Converting-Altum-Thermal-to-degrees-C-after-processing-in-Agisoft-or-Pix4D">Micasense</a> empirical formula</li> <li>DEM</li> <li>Dense point cloud RGB</li> <li>Spectral indices calculated: NDVI, NDRE</li> <li>Agisoft Report</li> </ul> <p><strong>Acknowledgment:</strong></p> <p><strong>Frank Becker</strong></p> <p>Landesbetrieb Forst Brandenburg</p> <p>Landeskompetenzzentrum Forst Eberswalde (LFE)</p>
Replication data for: Effect of Regional Marine Cloud Brightening Interventions on Climate Tipping Points
<p>Data for reproduction of Hirasawa, H., Hingmire, D., Singh, H., Rasch, P. J., & Mitra, P. (2023). Effect of regional marine cloud brightening interventions on climate tipping elements. Geophysical Research Letters, 50, e2023GL104314. https://doi.org/10.1029/2023GL104314</p> <p>Includes:</p> <ul> <li>Raw monthly 2m temperature (TREFHT) and precipitation (PRECT) data from CESM2 MCB simulations.</li> <li>Ensemble mean data from CESM2 Large Ensemble</li> <li>Tipping point metric timeseries from CESM2 LE Historical and SSP2-4.5 and CESM2 MCB simulations.</li> <li>Jupyter notebook displaying plotting script</li> <li>Scripts showing procedure for computing tipping point metrics</li> <li>CAM6 SourceMod changes to apply cloud droplet number concentration perturbations</li> <li>Top of atmosphere long and shortwave anomalies from fixed sea surface temperature simulations for computing effective radiative forcing</li> </ul>
Detection of standing retention trees in boreal forests with airborne laser scanning point clouds and multispectral imagery
Open the record for dataset details and reuse information.
A Simple Semantic-based Data Storage Layout for Querying Point Clouds
<p>Dataset contains three datasets: SMALL, MEDIUM and LARGE point cloud.</p> <p>Contains Python code which is made up of two files pc_new_semantics.py and paperutils.py</p> <p> </p>
Tiles of airborne laser scanning point clouds of Vienna, Austria (2016)
<p>Airborne laser scanning of the city of Vienna, which was organized by the survey department of the City Administration of Vienna (MA41). The data acquisition was performed in eight flight missions in November 2015.</p> <p>After strip adjustment relative accuracy of the point cloud was in the order of 2cm. The absolute accuracy measured as RMSE is better than 5cm in planimetry and better than 4cm in elevation. The dataset was cleaned and does not involve obvious gross errors, e.g. points high up in the air or points far below ground level. The point density is measured as by last echoes per unit area and is more than 15 points/m<sup>2</sup> for 97% of the area. The average point density is 33 points/m<sup>2</sup>.</p> <p>The LiDAR dataset of the city is organized in a number of 1270m×1020m tiles (including a 10m overlap of all neighboring tiles).</p> <p>Reference labels were generated semi-automatically: a rough filtering of main objects was firstly conducted by the software “Terrasolid”, and the final classification was refined by manual labelling. For the purpose of Vienna city administration, five classes are considered, namely ground, buildings, vegetation, others, water and bridges. All common street objects are categorized as others, such as (e.g.) streetlights, benches, shrubs, cars, construction sites and garbage bins. The different classes are defined by the following numeric integer codes: 2: Ground, 5: Vegetation, 6: Buildings, 8: Others, 9: Water and 17: Bridges.</p> <p>The quality of the labelling was manually checked. In 20 sites of 100m×100m the classification was manually verified, and the average accuracy of reference labels is 95%.</p> <p>Total 9 tiles are published, in which 4 tiles were used for the training and 5 tiles for the evaluation in the paper “A Comparison of Deep Learning Methods for Airborne LiDAR Point Clouds Classification”. Their locations in the city of Vienna can be found in the file of metadata, which also provides WGS84/GRS80 latitude and longitude coordinates of the extent corners of each tile (EPSG: 31256). This can be used to access images of the tiles from Google Maps or other public map service.</p> <p> </p>
Point Cloud - Plaza Mayor de Almagro, Spain
This is a point cloud of the Plaza Mayor de Almagro, located in Almagro Spain. The data were collected during the night in March 2017 using three FARO Focus3D laser scanners. Almagro's main square is in the historical and cultural center of the city, in the heart of the Military Order of Calatrava. It was built in the 16th century as a large public space for a market, celebrations, important or solemn events and leisure space. Its size allowed, the holding of bullfights, before the exempt bullrings were created. The plaza has a rectangular shape and is delimited by two large galleries formed by beautiful stone columns. Originally the upper galleries of the square were not closed as they served to contemplate the public shows organized in the square. At one end the Town Hall stands and at the other end the Church of San Bartolomé, now disappeared. In addition, on one of its sides it is still possible to visit the corral de comedias (theater) built in the late 16th century or early 17th century. Source: Objaverse 1.0 / Sketchfab
12th C CE Water-Moon Guanyin - point cloud
Twelfth-century Water-Moon Guanyin, now in the collection of the Minneapolis Institute of Art. From the sculpture's description on artsmia.org: "This form of the bodhisattva presides over his own paradise, Potolaka, which is described in scripture as a rugged seaside cave from which Avalokiteshvara could admire the reflection of the moon in the water. Appropriately, he appears meditative and relaxed and is seated informally in the "royal-ease" posture. Ornately dressed, with silk robes, fluttering sashes, jewelry, and an elaborate hairstyle, he also wears a headpiece that features an image of Amitābha, the buddha to whom Avalokiteshvara attends. Believers might look to a sculpture like this as a guide for their own journeys toward enlightenment." The sculpture on Mia's website: https://collections.artsmia.org/art/32122/seated-guanyin-china The mesh version of the object is here: https://skfb.ly/68PAA Source: Objaverse 1.0 / Sketchfab
Nosferatu - Point Cloud
Nosferatu, eine Symphonie des Grauens (translated as Nosferatu: A Symphony of Horror; or simply Nosferatu) is a 1922 German Expressionist horror film, directed by F. W. Murnau, starring Max Schreck as the vampire Count Orlok. The film, shot in 1921 and released in 1922, was an unauthorized adaptation of Bram Stoker's Dracula (1897). Various names and other details were changed from the novel: for instance, "vampire" became "Nosferatu" and "Count Dracula" became "Count Orlok". Stoker's heirs sued over the adaptation, and a court ruling ordered that all copies of the film be destroyed. However, a few prints of Nosferatu survived, and the film came to be regarded as an influential masterpiece of cinema. The film was released in the United States on 3 June 1929, seven years after its original premiere in Germany. Source: Wikipedia Source: Objaverse 1.0 / Sketchfab
Lanyon Quoit - point cloud
Neolithic quoit between Morvah and Madron in West Penwith, Cornwall UK. The quoit collapsed in 1815 and was rebuilt in 1824. Originally it had four upright supports, but only three were used in the reconstruction. This 3D scan revealed the date AD 1824 carved into one of the supporting stones. This view of the 3D model is called a point cloud. It represents the many measurements taken to create the 3D model, but without creating a 'solid' shell, allowing the monument to be inspected in a novel way. In this model we have reduced the density of measurement points to enable them to be viewed here. 3D scan for Penwith Landscape Partnership by the [Curatorial Research Centre](https://curatorialresearch.com). Source: Objaverse 1.0 / Sketchfab
Casa Rodriguez 52 point-cloud-2500k-pts
Casa Rodriguez 52 point-cloud-2500k-pts [[ 10m points decimated to 2.5m with a slight extra soak on the sign :) ]] Visited Old Town in Saint Augustine, FL – brought back a door -- simple textured version here: https://skfb.ly/6Lx8M 92 images taken from 50 seconds of 3kp30 video, scan made with colmap, points cleaned up in Houdini ;) (slightly different from the textured version) – https://www.instagram.com/organiccomputer/ Source: Objaverse 1.0 / Sketchfab
Point Cloud -- Puerta de Toledo, Spain
This is a point cloud of the so-called Puerta de Toledo, located in Ciudad Real (Spain) that was scanned with three FARO Focus3D scanners in 2017. It receives that name for being the gate that gave access to the road that linked Ciudad Real with Toledo. This gate was part of the walls that surrounded and protected the city in the Middle Ages. Originally it had two heights, but only half being preserved. It was also equipped with doors and rakes capable of preventing entry. From an artistic point of view it is a mixture of Gothic and Islamic art. During the restoration work on the gate, carried out a few years ago, several sculpted heads wearing a crown were discovered in the vaults, so it is interpreted that it could be a representation of the king, perhaps Alfonso X. Source: Objaverse 1.0 / Sketchfab
Nicholas Hawksmoor, Conduit Head. Point cloud
Greenwich Park, Nicholas Hawksmoor's eastern conduit head. Hawksmoor reffered to Roman prototypes for the conduit entrance both in form and in using red bricks as being appropriate for for a civil engineering project. The conduit was built to supply water to the Royal Hospital for Seamen. Fujifilm x100v > Metashape > Cloud Compare Source: Objaverse 1.0 / Sketchfab
Roman Catholic Cathedral_St.Michael_point cloud
This is the point cloud used to generate this model: https://skfb.ly/66Qq8 Just a display test, with download enabled. Source: Objaverse 1.0 / Sketchfab
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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.