The geojsonview extension for CKAN provides a simple and direct way to visualize GeoJSON resources directly within the CKAN interface. By leveraging the Leaflet JavaScript library, this extension renders geospatial data from GeoJSON files, making it easier for users to explore and understand geographic datasets. It offers a streamlined solution for integrating interactive maps into CKAN-powered data portals. Key Features: GeoJSON Visualization: Enables the display of GeoJSON resources as interactive maps within CKAN's resource views. Leaflet Integration: Utilizes the Leaflet JavaScript library for rendering maps, providing a lightweight and efficient mapping experience. CKAN Integration: Seamlessly integrates with CKAN's resource view system, allowing users to view GeoJSON data alongside other resource formats.

Dataset from Info-communications Media Development Authority. For more information, visit https://data.gov.sg/datasets/d_d8644084f8b54f851a1acbb2f04d5089/view

Hand drawn Alabama state border in geojson polygon format. This resource was created to test NWM viewer app.

geodata data package providing geojson polygons for all the world's countries

Dataset from National Environment Agency. For more information, visit https://data.gov.sg/datasets/d_dbfabf16158d1b0e1c420627c0819168/view

• Description: Trees managed by the Municipality of The Hague ***Limitations:** This dataset is not suitable for legal or surveying purposes
• Possibilities: This dataset is suitable for providing insight into the location on the map
• Viewer: https://arcg.is/9L4We
• Coordinate system: RDNew
• Data model: IMGEO for info: http://imgeo.geostandaarden.nl/

Dataset from People's Association. For more information, visit https://data.gov.sg/datasets/d_ddae2233aec5ca47e1d485b54b37fd34/view

.geojson files created for National Water Model Forecast Viewer

• Description: Smallest functionally independent piece of a NEN 3610 Road with uniform, homogeneous properties and relationships and primarily intended for use by road, rail and air traffic on land.
• Objective registration: Manage public space ***Limitations:** This dataset is not suitable for legal or surveying purposes
• Possibilities: This dataset is suitable for providing insight into the location on the map
• Viewer: https://arcg.is/jCLHD
• Coordinate system: RDnew
• Data model: IMGEO for info: http://www.geonovum.nl/sites/default/files/BGTData Catalogue111.pdf

Dataset from National Environment Agency. For more information, visit https://data.gov.sg/datasets/d_5d060d8b7838a15e8906fb22c50dbf51/view

Dataset from National Heritage Board. For more information, visit https://data.gov.sg/datasets/d_31e16b12809e66673e90d8b04fdee1b2/view

JSON Schema Dataset

This dataset consists of a collection of JSON Schema documents collected from GitHub by searching using the Sourcegraph API.

  Step 1: Find a list of JSON Schema paths

The Sourcegraph code search API is used to find files with a .json extension and containing { "$schema": "https://json-schema.org/". This is somewhat restrictive, but still manages to find a large number of schemas. pipenv run python slurp.py --outfile repos.csv

  Step 2:… See the full description on the dataset page: https://huggingface.co/datasets/dataunitylab/json-schema.

Demo to save data from a Space to a Dataset. Goal is to provide reusable snippets of code.

Documentation: https://huggingface.co/docs/huggingface_hub/main/en/guides/upload#scheduled-uploads Space: https://huggingface.co/spaces/Wauplin/space_to_dataset_saver/ JSON dataset: https://huggingface.co/datasets/Wauplin/example-space-to-dataset-json Image dataset: https://huggingface.co/datasets/Wauplin/example-space-to-dataset-image Image (zipped) dataset:… See the full description on the dataset page: https://huggingface.co/datasets/Wauplin/example-space-to-dataset-json.

vicky4s4s/Json-datasets dataset hosted on Hugging Face and contributed by the HF Datasets community

The UNI-CEN Digital Boundary File Series facilitates the mapping of UNI-CEN census data tables. Boundaries are provided in multiple formats for different use cases: Esri Shapefile (SHP), geoJson, and File Geodatabase (FGDB). SHP and FGDB files are provided in two projections: NAD83 CSRS for print cartography and WGS84 for web applications. The geoJson version is provided in WGS84 only. The UNI-CEN Standardized Census Data Tables are readily merged to these boundary files. For more information about file sources, the methods used to create them, and how to use them, consult the documentation at https://borealisdata.ca/dataverse/unicen_docs. For more information about the project, visit https://observatory.uwo.ca/unicen.

Hand drawn Utah state border in geojson featurecollection format Projection: WGS84 (EPSG: 4326). This resource was created to test NWM viewer app.

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 = ['

Nethriya/json dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset from Monetary Authority of Singapore. For more information, visit https://data.gov.sg/datasets/d_b47c770f3ff44972bc73ea717e8fa87d/view

The AbrirCon extension for CKAN enhances data accessibility by enabling users to seamlessly open various resource types with external online applications like Plotly, Carto, and Geojson.io. This extension adds "Abrir con" links to resource pages, providing users with a direct way to visualize and interact with data using their preferred tools. By supporting a range of file formats, AbrirCon extends CKAN's utility for data exploration and analysis. Key Features: Plotly Integration: Allows users to open CSV, TSV, XLS, and XLSX files directly in Plotly for interactive data visualization. Carto Integration: Enables opening CSV, XLS, XLSX, KML, KMZ, GeoJSON, and SHP files in Carto for geospatial analysis and mapping. Geojson.io Integration: Facilitates opening GeoJSON files in Geojson.io for quick viewing and editing of geospatial data. Easy Installation: Simple installation process involving cloning the repository, installing the extension, and adding abrircon to the ckan.plugins configuration. Configuration Parameters: Requires configuration of specific parameters (not detailed in the Readme), likely to configure the integration with Plotly, Carto and Geojson.io (e.g. API keys or URLs). Technical Integration: The AbrirCon extension integrates with CKAN by adding itself to the ckan.plugins configuration, as described in the readme. This suggests that it likely modifies the resource view templates— specifically the resourceitemexplore block of the resource_item.html file — to insert the "Abrir con" links. When installing, the readme explicitly mentions the order of plugins in ckan.plugins being important, specifically that abrircon should precede any plugins which modify the resourceitemexplore block of resource_item.html. Benefits & Impact: The AbrirCon extension simplifies the process of visualizing and working with data stored in CKAN. By allowing users to quickly open resources in external applications, it reduces the need for manual downloading and uploading of files. This streamlined workflow enhances data exploration and analysis capabilities, making CKAN a more valuable tool for data users. The fact that several city councils contributed to the extension points to its value in the open data ecosystem.