Convert YOLO To JSON Format

## Overview

Convert YOLO To JSON Format is a dataset for object detection tasks - it contains Fire annotations for 7,706 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The geopusher extension for CKAN automatically converts KML and Shapefile resources uploaded to a CKAN instance into GeoJSON resources. This conversion process allows users to easily access and utilize geospatial data in a modern, web-friendly format without needing to manually reformat the files. The extension operates as a celery task, meaning it can be configured to run automatically when resources are added or updated within CKAN. Key Features: Automatic GeoJSON Conversion: Converts KML and Shapefile resource uploads into GeoJSON format, increasing data usability and accessibility. Celery Task Integration: Operates as a Celery task, enabling asynchronous and automatic conversion upon resource creation or update and allowing other asynchronous operations to be processed at defined times. Batch Conversion: Provides functionality to convert all Shapefile resources on a CKAN instance or a specific subset of datasets at once. Technical Integration: The geopusher extension integrates with CKAN by listening to resource update events. The celery daemon needs to be running for automatic conversion to occur. The extension requires GDAL to be installed on the server to handle the geospatial data conversion. The README shows that the installation and usage involve updating the CKAN configuration Benefits & Impact: By automatically converting geospatial data into GeoJSON, the geopusher extension simplifies the use of KML and Shapefile data within web applications. This automation reduces manual effort, increases accessibility, and helps users to more readily integrate CKAN data into mapping and analysis tools. The automatic conversion ensures that when geospatial data is uploaded to a CKAN repository, users are able to immediately access the data in a suitable format for a wide range of web-based mapping applications, supporting improved data dissemination and collaboration.

Convert COCO JSON To YOLOv8

## Overview

Convert COCO JSON To YOLOv8 is a dataset for object detection tasks - it contains Objects annotations for 1,200 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

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

Convert JSON To XML

## Overview

Convert JSON To XML is a dataset for object detection tasks - it contains Market Products annotations for 2,049 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

Introduction

Geographical scale, in terms of spatial extent, provide a basis for other branches of science. This dataset contains newly proposed geographical and geological GIS boundaries for the Pan-Tibetan Highlands (new proposed name for the High Mountain Asia), based on geological and geomorphological features. This region comprises the Tibetan Plateau and three adjacent mountain regions: the Himalaya, Hengduan Mountains and Mountains of Central Asia, and boundaries are also given for each subregion individually. The dataset will benefit quantitative spatial analysis by providing a well-defined geographical scale for other branches of research, aiding cross-disciplinary comparisons and synthesis, as well as reproducibility of research results.

The dataset comprises three subsets, and we provide three data formats (.shp, .geojson and .kmz) for each of them. Shapefile format (.shp) was generated in ArcGIS Pro, and the other two were converted from shapefile, the conversion steps refer to 'Data processing' section below. The following is a description of the three subsets:

(1) The GIS boundaries we newly defined of the Pan-Tibetan Highlands and its four constituent sub-regions, i.e. the Tibetan Plateau, Himalaya, Hengduan Mountains and the Mountains of Central Asia. All files are placed in the "Pan-Tibetan Highlands (Liu et al._2022)" folder.

(2) We also provide GIS boundaries that were applied by other studies (cited in Fig. 3 of our work) in the folder "Tibetan Plateau and adjacent mountains (Others’ definitions)". If these data is used, please cite the relevent paper accrodingly. In addition, it is worthy to note that the GIS boundaries of Hengduan Mountains (Li et al. 1987a) and Mountains of Central Asia (Foggin et al. 2021) were newly generated in our study using Georeferencing toolbox in ArcGIS Pro.

(3) Geological assemblages and characters of the Pan-Tibetan Highlands, including Cratons and micro-continental blocks (Fig. S1), plus sutures, faults and thrusts (Fig. 4), are placed in the "Pan-Tibetan Highlands (geological files)" folder.

Note: High Mountain Asia: The name ‘High Mountain Asia’ is the only direct synonym of Pan-Tibetan Highlands, but this term is both grammatically awkward and somewhat misleading, and hence the term ‘Pan-Tibetan Highlands’ is here proposed to replace it. Third Pole: The first use of the term ‘Third Pole’ was in reference to the Himalaya by Kurz & Montandon (1933), but the usage was subsequently broadened to the Tibetan Plateau or the whole of the Pan-Tibetan Highlands. The mainstream scientific literature refer the ‘Third Pole’ to the region encompassing the Tibetan Plateau, Himalaya, Hengduan Mountains, Karakoram, Hindu Kush and Pamir. This definition was surpported by geological strcture (Main Pamir Thrust) in the western part, and generally overlaps with the ‘Tibetan Plateau’ sensu lato defined by some previous studies, but is more specific.

More discussion and reference about names please refer to the paper. The figures (Figs. 3, 4, S1) mentioned above were attached in the end of this document.

Data processing

We provide three data formats. Conversion of shapefile data to kmz format was done in ArcGIS Pro. We used the Layer to KML tool in Conversion Toolbox to convert the shapefile to kmz format. Conversion of shapefile data to geojson format was done in R. We read the data using the shapefile function of the raster package, and wrote it as a geojson file using the geojson_write function in the geojsonio package.

Version

Version 2022.1.

Acknowledgements

This study was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDB31010000), the National Natural Science Foundation of China (41971071), the Key Research Program of Frontier Sciences, CAS (ZDBS-LY-7001). We are grateful to our coauthors insightful discussion and comments. We also want to thank professors Jed Kaplan, Yin An, Dai Erfu, Zhang Guoqing, Peter Cawood, Tobias Bolch and Marc Foggin for suggestions and providing GIS files.

Citation

Liu, J., Milne, R. I., Zhu, G. F., Spicer, R. A., Wambulwa, M. C., Wu, Z. Y., Li, D. Z. (2022). Name and scale matters: Clarifying the geography of Tibetan Plateau and adjacent mountain regions. Global and Planetary Change, In revision

Jie Liu & Guangfu Zhu. (2022). Geographical and geological GIS boundaries of the Tibetan Plateau and adjacent mountain regions (Version 2022.1). https://doi.org/10.5281/zenodo.6432940

Contacts

Dr. Jie LIU: E-mail: liujie@mail.kib.ac.cn;

Mr. Guangfu ZHU: zhuguangfu@mail.kib.ac.cn

Institution: Kunming Institute of Botany, Chinese Academy of Sciences

Address: 132# Lanhei Road, Heilongtan, Kunming 650201, Yunnan, China

Copyright

This dataset is available under the Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).

GIS2DJI is a Python 3 program created to exports GIS files to a simple kml compatible with DJI pilot. The software is provided with a GUI. GIS2DJI has been tested with the following file formats: gpkg, shp, mif, tab, geojson, gml, kml and kmz. GIS_2_DJI will scan every file, every layer and every geometry collection (ie: MultiPoints) and create one output kml or kmz for each object found. It will import points, lines and polygons, and converted each object into a compatible DJI kml file. Lines and polygons will be exported as kml files. Points will be converted as PseudoPoints.kml. A PseudoPoints fools DJI to import a point as it thinks it's a line with 0 length. This allows you to import points in mapping missions. Points will also be exported as Point.kmz because PseudoPoints are not visible in a GIS or in Google Earth. The .kmz file format should make points compatible with some DJI mission software.

Dataset Format Conversion (yolo Txt To CreateML Json)

## Overview

Dataset Format Conversion (yolo Txt To CreateML Json) is a dataset for object detection tasks - it contains Drones annotations for 1,339 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

A curated prompt template for AI language models: convert a json/xml file to csv

chat: https://claude.ai/public/artifacts/aeb86230-187d-42a0-a22b-a96aadb4e1c3 h/t: https://docs.anthropic.com/en/resources/prompt-library/csv-converter

Convert COCO JSON To Yolov7 TXT

## Overview

Convert COCO JSON To Yolov7 TXT is a dataset for instance segmentation tasks - it contains Bone Fracture annotations for 3,998 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This JSON-LD documents were created using code in the GitHub repository at https://github.com/ChalkLab/scidata_trc

Want to keep the data in your Hosted Feature Service current? Not interested in writing a lot of code?Leverage this Python Script from the command line, Windows Scheduled Task, or from within your own code to automate the replacement of data in an existing Hosted Feature Service. It can also be leveraged by your Notebook environment and automatically managed by the MNCD Tool!See the Sampler Notebook that features the OverwriteFS tool run from Online to update a Feature Service. It leverages MNCD to cache the OverwriteFS script for import to the Notebook. A great way to jump start your Feature Service update workflow! RequirementsPython v3.xArcGIS Python APIStored Connection Profile, defined by Python API 'GIS' module. Also accepts 'pro', to specify using the active ArcGIS Pro connection. Will require ArcGIS Pro and Arcpy!Pre-Existing Hosted Feature ServiceCapabilitiesOverwrite a Feature Service, refreshing the Service Item and DataBackup and reapply Service, Layer, and Item properties - New at v2.0.0Manage Service to Service or Service to Data relationships - New at v2.0.0Repair Lost Service File Item to Service Relationships, re-enabling Service Overwrite - New at v2.0.0'Swap Layer' capability for Views, allowing two Services to support a View, acting as Active and Idle role during Updates - New at v2.0.0Data Conversion capability, able to invoke following a download and before Service update - New at v2.0.0Includes 'Rss2Json' Conversion routine, able to read a RSS or GeoRSS source and generate GeoJson for Service Update - New at v2.0.0Renamed 'Rss2Json' to 'Xml2GeoJSON' for its enhanced capabilities, 'Rss2Json' remains for compatability - Revised at v2.1.0Added 'Json2GeoJSON' Conversion routine, able to read and manipulate Json or GeoJSON data for Service Updates - New at v2.1.0Can update other File item types like PDF, Word, Excel, and so on - New at v2.1.0Supports ArcGIS Python API v2.0 - New at v2.1.2RevisionsSep 29, 2021: Long awaited update to v2.0.0!Sep 30, 2021: v2.0.1, Patch to correct Outcome Status when download or Coversion resulted in no change. Also updated documentation.Oct 7, 2021: v2.0.2, workflow Patch correcting Extent update of Views when Overwriting Service, discovered following recent ArcGIS Online update. Enhancements to 'datetimeUtil' Support script.Nov 30, 2021: v2.1.0, added new 'Json2GeoJSON' Converter, enhanced 'Xml2GeoJSON' Converter, retired 'Rss2Json' Converter, added new Option Switches 'IgnoreAge' and 'UpdateTarget' for source age control and QA/QC workflows, revised Optimization logic and CRC comparison on downloads.Dec 1, 2021: v2.1.1, Only a patch to Conversion routines: Corrected handling of null Z-values in Geometries (discovered immediately following release 2.1.0), improve error trapping while processing rows, and added deprecation message to retired 'Rss2Json' conversion routine.Feb 22, 2022: v2.1.2, Patch to detect and re-apply case-insensitive field indexes. Update to allow Swapping Layers to Service without an associated file item. Added cache refresh following updates. Patch to support Python API 2.0 service 'table' property. Patches to 'Json2GeoJSON' and 'Xml2GeoJSON' converter routines.Sep 5, 2024: v2.1.4, Patch service manager refresh failure issue. Added trace report to Convert execution on exception. Set 'ignore-DataItemCheck' property to True when 'GetTarget' action initiated. Hardened Async job status check. Update 'overwriteFeatureService' to support GeoPackage type and file item type when item.name includes a period, updated retry loop to try one final overwrite after del, fixed error stop issue on failed overwrite attempts. Removed restriction on uploading files larger than 2GB. Restores missing 'itemInfo' file on service File items. Corrected false swap success when view has no layers. Lifted restriction of Overwrite/Swap Layers for OGC. Added 'serviceDescription' to service detail backup. Added 'thumbnail' to item backup/restore logic. Added 'byLayerOrder' parameter to 'swapFeatureViewLayers'. Added 'SwapByOrder' action switch. Patch added to overwriteFeatureService 'status' check. Patch for June 2024 update made to 'managers.overwrite' API script that blocks uploads > 25MB, API v2.3.0.3. Patch 'overwriteFeatureService' to correctly identify overwrite file if service has multiple Service2Data relationships.Includes documentation updates!

This data set is a conversion of Califonia building footprint file from GeoJSON format to shapefile format.  The California building footprint file which contains 10,988,525 computer generated building footprints in California state is extracting from US building footprint dataset by Microsoft (2018).

Attention: this data is obsolete. You can retrieve data via WFS web services which you can then convert to GeoJSON https://geoservices.ign.fr/services-web-experts-transports#2314 using GDAL (advanced users) or QGIS (office GIS tool)

Data on http://cartelie.application.developpement-durable.gouv.fr/cartelie/voir.do?carte=PGS_Metropole_I&service=DGAC can only be consulted when they should be available under the law, which is available elsewhere than on Cartelie or the Geoportail.

You will find zones 1, 2 and 3 of Genoa Sonore Plans (PGS) in the form of GeoJSON.

The ckanext-mvt extension enhances CKAN's geospatial data capabilities by adding support for large-size GeoJSON previewing. It achieves this by converting GeoJSON resources into Mapbox Vector Tiles (MVT), which are more efficient for displaying large datasets on interactive maps. The extension includes two plugins: one for converting GeoJSON to MVT and uploading the tiles to Amazon S3, and another to preview these S3-hosted tiles directly within CKAN. Key Features: GeoJSON to MVT Conversion: Automatically converts GeoJSON resources into the Mapbox Vector Tile format, enabling efficient rendering of large geospatial datasets.

DOJ Press Release Converter

This script converts Department of Justice press releases from a JSON format to a JSONL (JSON Lines) format suitable for fine-tuning language models.

  Description

The convert-axios.py script performs the following operations:

Reads a source JSON file (doj_press.json) containing DOJ press releases Converts each press release into a format with: An instruction prompt An empty input field The press release content as output

Writes the… See the full description on the dataset page: https://huggingface.co/datasets/matthiaskos/doj-press-rlhf.

1. The numerical range of the geological sensitive area is the data of the geological sensitive area delineation process, which is only for planning reference. When overlaying the relevant map layers, attention should be paid to whether coordinate transformation, base map type, and accuracy will produce errors in order to avoid misinterpretation of the overlaid data. The actual range is still subject to the announced map data. Please download the relevant announcement data from the website of the Central Geological Survey, Ministry of Economic Affairs (https://www.moeacgs.gov.tw) in the Geological Law Zone.2. The boundaries of geological sensitive areas are delineated based on relevant map data, and are subsequently overlaid on topographic maps of equal or smaller scales for announcement. The digitalization of map data is subject to scale and accuracy limitations. When using the geological sensitive area digitalized map layers for overlay, consideration should still be given to the existence of errors. For the delineation method of geological sensitive areas, please refer to the respective delineation plan.3. This dataset has been converted from the original SHP format to GeoJSON format for convenient use through a program. It only includes data related to Tainan, and the conversion results are for reference only.

Convert Yolo To Json

## Overview

Convert Yolo To Json is a dataset for object detection tasks - it contains Json annotations for 1,012 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Automatic conversions Download GeoJSON Download NeTEx Automatic NeTEx conversion are created from the associated GTFS file and don't contain additional information which can be described in NeTEx. Automatic NeTEx conversions will not be available after 2025-06-01. Download the automatic NeTEx conversion Cancel Real-time data Données en temps réel au format GTFS-RT (ServiceAlert) real-time Latest modification 100% Availability rate No error detected during validation Features available in the resource: service_alerts gtfs-rt details Browse the API Données en temps réel au format GTFS-RT (TripUpdate) real-time Latest modification 100% Availability rate 56 errors during validation Features available in the resource: trip_updates gtfs-rt details Browse the API Données en temps réel au format GTFS-RT (VehiclePosition) real-time Latest modification 100% Availability rate No error detected

CSV To COCO JSON COnversion

## Overview

CSV To COCO JSON COnversion is a dataset for instance segmentation tasks - it contains CSV annotations for 1,233 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).