Overview

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.

Our corresponding paper (published at ITSC 2022) is available here. Further, we have applied 3DHD CityScenes to map deviation detection here.

Moreover, we release code to facilitate the application of our dataset and the reproducibility of our research. Specifically, our 3DHD_DevKit comprises:

Python tools to read, generate, and visualize the dataset,

3DHDNet deep learning pipeline (training, inference, evaluation) for map deviation detection and 3D object detection.

The DevKit is available here:

https://github.com/volkswagen/3DHD_devkit.

The dataset and DevKit have been created by Christopher Plachetka as project lead during his PhD period at Volkswagen Group, Germany.

When using our dataset, you are welcome to cite:

@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}}

Acknowledgements

We thank the following interns for their exceptional contributions to our work.

Benjamin Sertolli: Major contributions to our DevKit during his master thesis

Niels Maier: Measurement campaign for data collection and data preparation

The European large-scale project Hi-Drive (www.Hi-Drive.eu) supports the publication of 3DHD CityScenes and encourages the general publication of information and databases facilitating the development of automated driving technologies.

The Dataset

After downloading, the 3DHD_CityScenes folder provides five subdirectories, which are explained briefly in the following.

1. Dataset

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.

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.

To read any JSON-encoded data provided by 3DHD CityScenes in Python, you can use the following code snipped as an example.

import json

json_path = r"E:\3DHD_CityScenes\Dataset\train.json" with open(json_path) as jf: data = json.load(jf) print(data)

1. HD_Map

Map items are stored as lists of items in JSON format. In particular, we provide:

traffic signs,

traffic lights,

pole-like objects,

construction site locations,

construction site obstacles (point-like such as cones, and line-like such as fences),

line-shaped markings (solid, dashed, etc.),

polygon-shaped markings (arrows, stop lines, symbols, etc.),

lanes (ordinary and temporary),

relations between elements (only for construction sites, e.g., sign to lane association).

1. HD_Map_MetaData

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.

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.

1. HD_PointCloud_Tiles

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.

x-coordinates: 4 byte integer

y-coordinates: 4 byte integer

z-coordinates: 4 byte integer

intensity of reflected beams: 2 byte unsigned integer

ground classification flag: 1 byte unsigned integer

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.

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 = ['

Shapefiles for Ethiopia's Administrative boundaries: Regions, Zones and Woredas

This Python script (Shape2DJI_Pilot_KML.py) will scan a directory, find all the ESRI shapefiles (.shp), reproject to EPSG 4326 (geographic coordinate system WGS84 ellipsoid), create an output directory and make a new Keyhole Markup Language (.kml) file for every line or polygon found in the files. These new *.kml files are compatible with DJI Pilot 2 on the Smart Controller (e.g., for M300 RTK). The *.kml files created directly by ArcGIS or QGIS are not currently compatible with DJI Pilot.

The High Resolution Digital Elevation Model (HRDEM) product is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The complete coverage of the Canadian territory is gradually being established. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived data. For DTM datasets, derived data available are slope, aspect, shaded relief, color relief and color shaded relief maps and for DSM datasets, derived data available are shaded relief, color relief and color shaded relief maps. The productive forest line is used to separate the northern and the southern parts of the country. This line is approximate and may change based on requirements. In the southern part of the country (south of the productive forest line), DTM and DSM datasets are generated from airborne LiDAR data. They are offered at a 1 m or 2 m resolution and projected to the UTM NAD83 (CSRS) coordinate system and the corresponding zones. The datasets at a 1 m resolution cover an area of 10 km x 10 km while datasets at a 2 m resolution cover an area of 20 km by 20 km. In the northern part of the country (north of the productive forest line), due to the low density of vegetation and infrastructure, only DSM datasets are generally generated. Most of these datasets have optical digital images as their source data. They are generated at a 2 m resolution using the Polar Stereographic North coordinate system referenced to WGS84 horizontal datum or UTM NAD83 (CSRS) coordinate system. Each dataset covers an area of 50 km by 50 km. For some locations in the north, DSM and DTM datasets can also be generated from airborne LiDAR data. In this case, these products will be generated with the same specifications as those generated from airborne LiDAR in the southern part of the country. The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013), which is now the reference standard for heights across Canada. Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The tiles are aligned within each project. The product High Resolution Digital Elevation Model (HRDEM) is part of the CanElevation Series created in support to the National Elevation Data Strategy implemented by NRCan. Collaboration is a key factor to the success of the National Elevation Data Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

Survey results are available in two seperate formats. The .csv output contains all non-spatial data from the main survey form, and can be loaded in spreadsheet programs such as Microsoft Excel. The spatial content of the survey is available as a zipped collection of one or more shapefiles. These files can be opened in GIS applications such as ArcGISor QGIS. Please note, only completed survey responses are exported. Those still in draft will be excluded.Output columns in both the CSV and shapefile formats are named based on the exportidspecified in the form field configuration. If you are looking to analyze spatial data from the shapefiles based on attributes collected in the main response form, you can join fields from the CSV file with spatial features by joining on the RESPONSE_ID field.

CanVec contains more than 60 topographic features classes organized into 8 themes: Transport Features, Administrative Features, Hydro Features, Land Features, Manmade Features, Elevation Features, Resource Management Features and Toponymic Features. This multiscale product originates from the best available geospatial data sources covering Canadian territory. It offers quality topographic information in vector format complying with international geomatics standards. CanVec can be used in Web Map Services (WMS) and geographic information systems (GIS) applications and used to produce thematic maps. Because of its many attributes, CanVec allows for extensive spatial analysis. Related Products: Constructions and Land Use in Canada - CanVec Series - Manmade Features Lakes, Rivers and Glaciers in Canada - CanVec Series - Hydrographic Features Administrative Boundaries in Canada - CanVec Series - Administrative Features Mines, Energy and Communication Networks in Canada - CanVec Series - Resources Management Features Wooded Areas, Saturated Soils and Landscape in Canada - CanVec Series - Land Features Transport Networks in Canada - CanVec Series - Transport Features Elevation in Canada - CanVec Series - Elevation Features Map Labels - CanVec Series - Toponymic Features

This dataset contains the PyTorch model checkpoint file (also called the state_dict) of ESSA - a Mask R-CNN instance segmentation Deep Learning model trained to detect pits and skylights within Lunar Reconnaissance Orbiter Narrow Angle Camera (LROC NAC) imagery of the Moon. Instructions on how to load a model checkpoint in order to re-train or infer it on your own data can be found here.

The .zip file ESSA_shapefiles.zip contains the locations of the pit and skylight detections made by ESSA within eight Regions of Interest for searching for potential cave entrances ('ESSA_detections.shp'). These detections are in ESRI shapefile format ready for viewing in GIS software such as QGIS or ArcGIS. We also provide shapefiles of the mapping of Lunar rilles within the Marius Hills region of the Moon ('marius_hills_rilles.shp'). Both of these sets of shapefiles have attribute fields containing the confidence scores that they were detected with and the images that they were found to be within.

Le format fichier de formes (shapefile) est destiné aux systèmes d'informations géographiques (SIG). Il contient toute l'information liée à la géométrie des objets décrits. Il est composé de trois fichiers portant le même nom, avec les extensions SHP, DBF (attributs), SHX (index de la géométrie) et PRJ (système de projection de données).Le fichier reprend l’ensemble des lignes du réseau ferré national.La colonne MNEMO reprend les statuts de chaque ligne : projet (PROJET), exploitée (EXPLOITE), neutralisée (NEUT), neutralisée et conservée (NEUT DEF), transférée en voie de service (VS), fermée non déposée (FERME ND), fermée avec maintien en place de la voie (FERME MV), fermée et déposée (FERME D), fermée (FERME), fermée mise à disposition de tiers (FERME DT), retranchée (RETRANCHE), déclassée non vendue (DEC NV), déclassée vendue (DEC V).Un tronçon de ligne est une partie physiquement continue d’une ligne, délimitée par deux nœuds d’extrémités (origine et fin) de tronçon de ligne, et possédant un système de repérage cohérent. Lorsqu’il y a discontinuité, la ligne est découpée en plusieurs tronçon, avec un rang croissant.La précision du shapefile des lignes est décamétrique : usage à moyenne échelle (1/50.000ème).date de mise à jour 18/03/2025

There have been a number of requests for "State Roads" data. This data is currently available via the Data.NSW Spatial Collaboration Portal. To access Road Segment Data please follow the instructions below; From https://portal.spatial.nsw.gov.au/ Click the Browse Data tile Click NSW Data themes tile Click the Transport icon

NSW Cadastre web service is a dynamic map of cadastral features extracted from the NSW Digital Cadastral Database (DCDB). It provides access to a state wide integrated database and a component of the foundation spatial datasets within the New South Wales.

A “cadastre” is an official register of property showing boundaries. The DCDB contains current land titles only.

The cadastral feature class layers provided through this web service includes:

• Large Rural Plan Extent

• Rural Plan Extent

• Section Extent

• Plan Extent

• Lot

• Plan Extent Labels

• Section Extent Labels

• Lot Labels

The available attributes for point queries are:

• Lot/Section/Plan string

• CadID

This web service allows users to easily integrate NSW Cadastre into Open Geospatial Consortium (OGC) compliant spatial platforms and applications. The NSW Cadastral web service can be used for resource management, environmental management, land use planning, agriculture management, emergency management and recreational purposes This service can be used to aggregate information for analytical purposes.

Cadastral boundary data in combination with geo-coded address data, imagery, demographic information and agency specific business information underpins the ability to perform high quality spatial analysis.