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3DHD CityScenes: High-Definition Maps in High-Density Point Clouds
<p><strong>Overview</strong></p> <p>3DHD CityScenes is the most comprehensive, large-scale high-definition (HD) map dataset to date, annotated in the three spatial dimensions of globally referenced, high-density LiDAR point clouds collected in urban domains. Our HD map covers 127 km of road sections of the inner city of Hamburg, Germany including 467 km of individual lanes. In total, our map comprises 266,762 individual items.</p> <p>Our corresponding paper (published at ITSC 2022) is available <a href="https://www.researchgate.net/publication/364309881_3DHD_CityScenes_High-Definition_Maps_in_High-Density_Point_Clouds">here</a>.<br> Further, we have applied 3DHD CityScenes to map deviation detection <a href="https://www.researchgate.net/publication/368983255_DNN-Based_Map_Deviation_Detection_in_LiDAR_Point_Clouds">here</a>. </p> <p>Moreover, we release code to facilitate the application of our dataset and the reproducibility of our research. Specifically, our 3DHD_DevKit comprises:</p> <ul> <li>Python tools to read, generate, and visualize the dataset,</li> <li>3DHDNet deep learning pipeline (training, inference, evaluation) for<br> map deviation detection and 3D object detection.</li> </ul> <p>The DevKit is available here:</p> <p><a href="https://github.com/volkswagen/3DHD_devkit">https://github.com/volkswagen/3DHD_devkit</a>.</p> <p>The dataset and DevKit have been created by <a href="https://de.linkedin.com/in/christopher-plachetka-42b325115">Christopher Plachetka</a> as project lead during his PhD period at Volkswagen Group, Germany.</p> <p>When using our dataset, you are welcome to cite:</p> <pre><code>@INPROCEEDINGS{9921866, author={Plachetka, Christopher and Sertolli, Benjamin and Fricke, Jenny and Klingner, Marvin and Fingscheidt, Tim}, booktitle={2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)}, title={3DHD CityScenes: High-Definition Maps in High-Density Point Clouds}, year={2022}, pages={627-634}}</code></pre> <p><strong>Acknowledgements </strong></p> <p>We thank the following interns for their exceptional contributions to our work.</p> <ul> <li><a href="https://www.linkedin.com/in/sertolli/">Benjamin Sertolli</a>: Major contributions to our DevKit during his master thesis</li> <li>Niels Maier: Measurement campaign for data collection and data preparation</li> </ul> <p>The European large-scale project Hi-Drive (<a href="http://www.Hi-Drive.eu">www.Hi-Drive.eu</a>) supports the publication of 3DHD CityScenes and encourages the general publication of information and databases facilitating the development of automated driving technologies.</p> <p><strong>The Dataset</strong></p> <p>After downloading, the 3DHD_CityScenes folder provides five subdirectories, which are explained briefly in the following.</p> <p>1. Dataset</p> <p>This directory contains the training, validation, and test set definition (train.json, val.json, test.json) used in our publications. Respective files contain samples that define a geolocation and the orientation of the ego vehicle in global coordinates on the map.</p> <p>During dataset generation (done by our DevKit), samples are used to take crops from the larger point cloud. Also, map elements in reach of a sample are collected. Both modalities can then be used, e.g., as input to a neural network such as our 3DHDNet.</p> <p>To read any JSON-encoded data provided by 3DHD CityScenes in Python, you can use the following code snipped as an example.</p> <pre><code class="language-python">import json json_path = r"E:\3DHD_CityScenes\Dataset\train.json" with open(json_path) as jf: data = json.load(jf) print(data)</code></pre> <p>2. HD_Map</p> <p>Map items are stored as lists of items in JSON format. In particular, we provide:</p> <ul> <li>traffic signs,</li> <li>traffic lights,</li> <li>pole-like objects,</li> <li>construction site locations,</li> <li>construction site obstacles (point-like such as cones, and line-like such as fences),</li> <li>line-shaped markings (solid, dashed, etc.),</li> <li>polygon-shaped markings (arrows, stop lines, symbols, etc.),</li> <li>lanes (ordinary and temporary),</li> <li>relations between elements (only for construction sites, e.g., sign to lane association).</li> </ul> <p>3. HD_Map_MetaData</p> <p>Our high-density point cloud used as basis for annotating the HD map is split in 648 tiles. This directory contains the geolocation for each tile as polygon on the map. You can view the respective tile definition using QGIS. Alternatively, we also provide respective polygons as lists of UTM coordinates in JSON.</p> <p>Files with the ending .dbf, .prj, .qpj, .shp, and .shx belong to the tile definition as “shape file” (commonly used in geodesy) that can be viewed using QGIS. The JSON file contains the same information provided in a different format used in our Python API.</p> <p>4. HD_PointCloud_Tiles</p> <p>The high-density point cloud tiles are provided in global UTM32N coordinates and are encoded in a proprietary binary format. The first 4 bytes (integer) encode the number of points contained in that file. Subsequently, all point cloud values are provided as arrays. First all x-values, then all y-values, and so on. Specifically, the arrays are encoded as follows.</p> <ul> <li>x-coordinates: 4 byte integer</li> <li>y-coordinates: 4 byte integer</li> <li>z-coordinates: 4 byte integer</li> <li>intensity of reflected beams: 2 byte unsigned integer</li> <li>ground classification flag: 1 byte unsigned integer</li> </ul> <p>After reading, respective values have to be unnormalized. As an example, you can use the following code snipped to read the point cloud data. For visualization, you can use the pptk package, for instance.</p> <pre><code class="language-python">import numpy as np import pptk file_path = r"E:\3DHD_CityScenes\HD_PointCloud_Tiles\HH_001.bin" pc_dict = {} key_list = ['x', 'y', 'z', 'intensity', 'is_ground'] type_list = ['<i4', '<i4', '<i4', '<u2', 'u1'] with open(file_path, "r") as fid: num_points = np.fromfile(fid, count=1, dtype='<u4')[0] # print(num_points) # Init for k, dtype in zip(key_list, type_list): pc_dict[k] = np.zeros([num_points], dtype=dtype) # Read all arrays for k, t in zip(key_list, type_list): pc_dict[k] = np.fromfile(fid, count=num_points, dtype=t) # Unnorm pc_dict['x'] = (pc_dict['x'] / 1000) + 500000 pc_dict['y'] = (pc_dict['y'] / 1000) + 5000000 pc_dict['z'] = (pc_dict['z'] / 1000) pc_dict['intensity'] = pc_dict['intensity'] / 2**16 pc_dict['is_ground'] = pc_dict['is_ground'].astype(np.bool_) fid.close() print(pc_dict) # Visualization # Normalize (due to large UTM values) x_utm = pc_dict['x'] - np.mean(pc_dict['x']) y_utm = pc_dict['y'] - np.mean(pc_dict['y']) z_utm = pc_dict['z'] xyz = np.column_stack((x_utm, y_utm, z_utm)) viewer = pptk.viewer(xyz) viewer.attributes(pc_dict['intensity']) viewer.set(point_size=0.03)</code></pre> <p>5. Trajectories</p> <p>We provide 15 real-world trajectories recorded during a measurement campaign covering the whole HD map. Trajectory samples are provided approx. with 30 Hz and are encoded in JSON.</p> <p>These trajectories were used to provide the samples in train.json, val.json. and test.json with realistic geolocations and orientations of the ego vehicle.</p> <ul> <li>OP1 – OP5 cover the majority of the map with 5 trajectories.</li> <li>RH1 – RH10 cover the majority of the map with 10 trajectories.</li> </ul> <p>Note that OP5 is split into three separate parts, a-c. RH9 is split into two parts, a-b. Moreover, OP4 mostly equals OP1 (thus, we speak of 14 trajectories in our paper). For completeness, however, we provide all recorded trajectories here. </p>
Figure 1. Landmark definition. A. Dorsoglandularia 1–4. B in Integrative approach of morphology and geometric morphometrics to species delimiation in Torrenticolidae (Acari: Hydrachnidiae)
Figure 1. Landmark definition. A. Dorsoglandularia 1–4. B. Infracapitulum (Torrenticola). C. Infracapitulum (Monatractides).
Text-fig. 3. Litho- and biostratigraphic position of fossil floras treated herein, based on lithostratigraphic standard section of upper Oligocene and Miocene in central and eastern Germany (Standke et al. 2010, Escher et al. 2020); only exception from standard section: ** – Thierbach Member restricted to central Germany, replaces Branitz Member in eastern Germany; correlated to global scale of International Chronostratigraphic Chart 2022/02 (Cohen et al. 2013); maximum age ranges of sites/floras indicated by black bars; floristic complexes according to definitions by Mai and Walther 1991 for upper Oligocene, Mai 2000b, 2001b for Miocene; age range of MCO from Steinthorsdottir et al. 2021. in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)
Text-fig. 3. Litho- and biostratigraphic position of fossil floras treated herein, based on lithostratigraphic standard section of upper Oligocene and Miocene in central and eastern Germany (Standke et al. 2010, Escher et al. 2020); only exception from standard section: ** – Thierbach Member restricted to central Germany, replaces Branitz Member in eastern Germany; correlated to global scale of International Chronostratigraphic Chart 2022/02 (Cohen et al. 2013); maximum age ranges of sites/floras indicated by black bars; floristic complexes according to definitions by Mai and Walther 1991 for upper Oligocene, Mai 2000b, 2001b for Miocene; age range of MCO from Steinthorsdottir et al. 2021.
Figure 3. Using the ADX agent for obtaining definitions, synonyms and antonyms, for a given word, during MS Word editing-ADX – Agent for Morphologic Analysis of Lexical Entries in a Dictionary
<p>In figure 3 we present a capture screen of using the ADX1 agent in editing the text in<br> Microsoft Word. It displays the definition of the current word, but it also generates the synonyms,<br> used in the application of some web searching rules, used by another intelligent agent, called ASR.<br> The ASR agent will automatically compose some search strings to use for a web search engine, like<br> Google, Yahoo, or other. For example, if the user will search the word zăpadă (snow) on Yahoo<br> search engine, then one can also generate a search for the word nea (synonym of zăpadă) by using<br> the following ASR rule:<br> # Yahoo search<br> IF<br> http://search.yahoo.com/search?p=^X^&fr=yfp-t-309&toggle=1&cop=mss&ei=UTF-8<br> THEN<br> http://search.yahoo.com/search?p=^Clasa(X)^&fr=yfp-t-309&toggle=1&cop=mss&ei=UTF-8</p>
Figure 3. Activated change-Definition of Information
<p>A typical example of this kind is an electric light activated by an electrical button or lever<br> when someone (something) switches it on and the electrical bulb lights up. The sequence of actions<br> includes following four activities:<br> • external force presses the button;<br> • the button closes the electrical circuit thereby unlocking the energy source;<br> • electricity flows to the bulb;<br> • bulb lights up.<br> The general graphical schema of this change is as follows (Figure 3).</p>
Figure 2. Forced change-Definition of Information
<p>The second basic kind of causal relation consists of changes that occur as a result of<br> externally working forces. According to Britannica force is “any action that tends to maintain or<br> alter the motion of a body or to distort it” [“Force”, in: Britannica 2009].<br> The mechanism of this change is simple: the influence of a force alters the affected object or its<br> movement: A cosmic body changes its orbit due to the influence of gravity produced by some<br> external entity. A bullet accelerates because of the powder explosion in the cartridge; glass breaks<br> when it falls to the floor etc. This form of causal mechanism is called forced change here.<br> Forced changes represent the simplest form of a causal relation between two objects, first of which<br> ― a causal action of force ― delivers energy causing the effectual change of a stable object. Forced<br> change is represented as follows (Figure 2).</p>
Figure 4. Communicated change-Definition of Information
<p>In this work, a variable is not considered a mathematical construction, but an object in the<br> discernable world. Since the features of communicators and communications can differ, the<br> structure of the variable can differ too. Thus, in implementation where the crosswalk light directly<br> differentiates between pressed and non-pressed buttons, the variable consists of the complete button<br> mechanism. In alternative implementations where the button mechanism is connected to the input<br> port of the traffic light’s internal processor the mediated variable is the input computer port and the<br> button mechanism is an auxiliary appliance used by a pedestrian for setting this variable.</p>
Figure 1. Internal change-Definition of Information
<p>Internal change will be represented with the help of a horizontal rectangle that is split along<br> the x-axis into two parts. The upper half refers to the changed entity and the bottom half describes<br> the change to this entity. Because this object changes spontaneously without any apparent external<br> force or influence, there is only one rectangle and no other objects or arrow directions contained in<br> this graphic (Figure 1).</p>
The fifth dimension, the source of energy and definition of time
<p>It's a new approach to physics, this theory changed physics modern </p>
Рис. 1. 1, 2 — Отчет А. А. ЕмеΛьянова о поезΑке по рекам Ботчи и Коппи в 1925 гоΑу, разΑеΛ Lepidoptera. ОпреΑеΛения А. К. МоΛьтрехта Fig. 1. 1, 2 — A. A. Yemelyanov's report about the trip along the rivers Botchi and Koppi in 1925, Section: Lepidoptera. Definitions by A. K. Moltrecht in Hesperioidea And Papilionoidea (Lepidoptera) Of Coniferous Forests From The Nature Reserve Botchinskii
Рис. 1. 1, 2 — Отчет А. А. ЕмеΛьянова о поезΑке по рекам Ботчи и Коппи в 1925 гоΑу, разΑеΛ Lepidoptera. ОпреΑеΛения А. К. МоΛьтрехта Fig. 1. 1, 2 — A. A. Yemelyanov's report about the trip along the rivers Botchi and Koppi in 1925, Section: Lepidoptera. Definitions by A. K. Moltrecht
Lexical Semantic Change Cause-Type-Definitions Benchmark
<p>The Lexical Semantic Change Cause-Type-Definitions (LSC-CTD) Benchmark is a digitised dataset that builds on and extends the Blank's seminal 1997 taxonomy of semantic change. This collection categorises 657 instances of linguistic evolution across the vocabulary of the Romance languages, with additional entries of German and English instances. Each entry is accompanied by a new pair (Old and New Meaning) of english definitions, manually curated by a historical linguist.</p> <p>The dataset includes a detailed classification of causes of change such as semantic wear, lexical gap, orphaned word, lexical complexity, atypical actant structure, frame, socio-cultural change, abstract concept, atypical part of speech, new concept, taboo, expressivity and prototype. It also includes types of semantic shift as classified by Blank, i.e. specialisation, generalisation, co-hyponymous transfer, auto-antonym, metaphor, antiphrasis, metonymy, auto-converse, ellipsis, folk etymology, analogy, meaning dilution, meaning reinforcement and doubtful cases. </p> <p><br><strong>Reference</strong></p> <p>The accompanying paper where this resource is described in detail will be published at ACL 2024.<br><br><span>Pierluigi Cassotti, Stefano De Pascale, and Nina Tahmasebi. 2024. <a href="https://aclanthology.org/2024.acl-long.249">Using Synchronic Definitions and Semantic Relations to Classify Semantic Change Types</a>. In <em>Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</em>, pages 4539–4553, Bangkok, Thailand. Association for Computational Linguistics.</span></p>
Cross section shape: circular elliptical unknown Siphuncle position: central marginal unknown Fig. 4 Morphometrics of orthoconic cephalopods from the Besano Formation. Measurements are compared with discrete characters of the shell. Orange circles represent definite and orange crosses likely orthoceratoids, while blue triangles represent definite and blue crosses likely coleoids. Black crosses are indeterminable. A Apical angle, calculated from length and diameters of the specimens. B Maximum diameter in Orthoceratoid and coleoid cephalopods from the Middle Triassic of Switzerland with an updated taxonomic framework for Triassic Orthoceratoidea
Cross section shape: circular elliptical unknown Siphuncle position: central marginal unknown Fig. 4 Morphometrics of orthoconic cephalopods from the Besano Formation. Measurements are compared with discrete characters of the shell. Orange circles represent definite and orange crosses likely orthoceratoids, while blue triangles represent definite and blue crosses likely coleoids. Black crosses are indeterminable. A Apical angle, calculated from length and diameters of the specimens. B Maximum diameter
Dataset: Definitive Healthcare Corp. (DH) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
HYBRID WARFARE AS A PHENOMENON: EMERGENCE AND DEFINITIONAL PROBLEMS
<p><span>The article analyzes the problems of the emergence of the phenomenon of hybrid warfare. Different approaches to the definition of this category in domestic and foreign scientific literature are considered. The key theoretical concepts for the interpretation of the term hybrid warfare are formulated. A number of distinctive features in the general characterization of the phenomenon of hybrid warfare are given.</span></p>
Figure 1 in Accounting for variability in life-history traits for the definition of amphidromous goby fry fisheries closure periods
Figure 1. – Ranking of the 4096 alternatives of fisheries closure periods based on the MULTIMOORA analysis on the abundance and life-history traits of Sicyopterus lagocephalus and Cotylopus acutipinnis. The best alternative is ranked 1 and the worst 4096. Fisheries closure periods are colour coded.
FIGURE 2 in Chromosomal analysis of Centromochlus heckelii (Siluriformes: Auchenipteridae), with a contribution to Centromochlus definition
FIGURE 2 | Idiograms representing the karyotype and locations of heterochromatin and AgNORs in Centromochlus heckelii (present study) in comparison to Tatia neivai and Tatia jaracatia (Lui et al., 2013a). m: metacentric chromosomes; sm: submetacentric chromosomes; st: subtelocentric chromosomes; a: acrocentric chromosomes.
Population average atlas for RecobundlesX (BundleSeg) - TractSeg Definitions
<p><strong>Multi-atlas bundle segmentation</strong></p> <p>This data is made to be used with the following script:<br><a href="https://github.com/scilus/scilpy/blob/master/scripts/scil_tractogram_segment_with_bundleseg.py">https://github.com/scilus/scilpy/blob/master/scripts/scil_tractogram_segment_with_bundleseg.py</a></p> <blockquote> <p><br>Etienne St-Onge, Kurt Schilling, Francois Rheault, "BundleSeg: A versatile, reliable and reproducible approach to whitte matter bundle segmentation.", arXiv, 2308.10958 (2023)<br>Rheault, François. "Analyse et reconstruction de faisceaux de la matière blanche." Computer Science (Université de Sherbrooke) (2020), https://savoirs.usherbrooke.ca/handle/11143/17255</p> </blockquote> <p><strong>Usage</strong><br>Here is an example (for more details use `scil_tractogram_segment_with_bundleseg.py -h`) :</p> <p><code>antsRegistrationSyNQuick.sh -d 3 -f ${T1} -m mni_masked.nii.gz -t a -n 4</code><br><code>scil_tractogram_segment_with_bundleseg.py ${TRACTOGRAM} config_fss_1.json atlas/ output0GenericAffine.mat --out_dir ${OUTPUT_DIR}/ --log_level DEBUG --processes 8 --seeds 0 --inverse -f</code><br><br></p> <p>To facilitate interpretation, all endpoints were uniformized head/tail. To see, which side of a bundle is head or tail, you can load the atlas bundle into the software MI-Brain <a href="https://github.com/imeka/mi-brain">https://github.com/imeka/mi-brain</a></p> <p>(If you are processing multiple subjects, this pipeline could be useful for you <a href="https://github.com/scilus/rbx_flow">https://github.com/scilus/rbx_flow</a>)</p> <p><strong>Notes on bundles</strong><br>- The bundles follow the overall anatomical definition of TractSeg (initially from TractQuerier) but are a heavily processed union to discard false positives, outliers, unrealistic paths, etc.<br>- CG has 3 possible endpoint locations. However, the full extent of the tail is difficult to track <br>- The cerebellum is often cut due to acquisition FOV. In such a case, all projection bundles will be more difficult to recognize and most cerebellum bundles will be missing (ICP, MCP, SCP).<br>- All the bundles starting with T_ (Thalamo) or ST_ (Striato) are based on region of interest, and are not usually part of classical major pathways.</p>
Fig. 1 in A survey of Angiostrongylus species in definitive hosts in Queensland
Fig. 1. Images of two principal Rattus spp., Angiostrongylus cantonensis and its putative intermediate host from a survey conducted in Brisbane, Australia, 2012—2014. A: Rattus rattus was the most prevalent species of rat found in this survey. B: Rattus fuscipes. C: Adult A. cantonensis in pulmonary arteries of Rattus rattus. D: Helix aspersa found is Brisbane harbouring Angiostrongylus larvae.
Fig. 2 in A survey of Angiostrongylus species in definitive hosts in Queensland
Fig. 2. Adult Angiostrongylus spp. recovered from pulmonary arteries of Rattus rattus (A. cantonensis) and Rattus fuscipes (A. mackerrasae) trapped in Brisbane, Australia, 2012—2015. A: Posterior end of female A. cantonensis; B: Posterior end of female A. mackerrasae: The distance between vulva and posterior end is very similar between the two species; C: Male A. cantonensis and D: Male A. mackerrasae: Spicule length is about 2.5 times longer in A. cantonensis.
Fig. 3 in Environmental determinants of spatial and temporal variations in the transmission of Toxoplasma gondii in its definitive hosts
Fig. 3. Predicted relationships between NAO winter index and the probability of seropositivity for Toxoplasma gondii in all cats sampled; (a) juveniles and (b) adults. Points represent the observed seoprevalence values with 95% confidence intervals as whiskers. A median farm density (0.68 farm/km2) was used to calculate the predictions (full line). Minimal farm density (0 farm/km2) observed among sampled communes was used to calculate the minimal predictions of the model (dotted lines). High values of farm density (2 and 4 farms/km2) were used to calculate the maximal predictions of the model (dashed and dotted-dashed lines).
ScienceDex guides
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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.