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.

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). {"references": ["Bolch, T., Kulkarni, A., K\u00e4\u00e4b, A., Huggel, C., Paul, F., Cogley, J. G., Stoffel, M. (2012). The state and fate of Himalayan glaciers. Science, 336, 310-314. https...

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).

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are as of January 1, 2017, primarily as reported through the Census Bureau's Boundary and Annexation Survey (BAS).

The data disseminated correspond to the parcels declared in organic farming and in conversion during aid applications of the Common Agricultural Policy (CAP) for the 2019, 2020, 2021 and 2022 marketing years – in their known situation and decided by the administration at the end of the investigation, after 30 June of year N+ 1. These data include 80-85 % of the total organically produced parcels, with not all organic parcels being the subject of an application for CAP aid. These data cover metropolitan France and DROMs outside the overseas communities of Saint-Barthélemy and Saint-Martin. This is anonymised data, i.e. information about the natural or legal person cultivating these parcels is missing. The productions grown on these plots are informed. | attribute name | Type ▲ Comment | – | – ▲ — | ‘Millesime’ | Within the PAC year of declaration. | ‘bio’ | Entier ⋆ Vaut “1” (useful to recombine with the classic RPG), present before the 2021 vintage. | ‘CODE_CULTU’ | Character string ▲ Code culture of the plot (see Crop list and details (Métropole and DOM), PDF, 372 KB). | ‘LBL_CULTU’ | Character string ▲ Label associated with culture code. | ‘GRP_CULTU’ | Character string ▲ Culture group. | ‘SURFACE_HA’ | Number ▲ Graphic area of the plot (in hectares). | ‘CODE_COMMUNE_INSEE’ | | INSEE code of the municipality attached to the plot (from the 2021 vintage). | ‘LBL_COMMUNE’ | String of the municipality attached to the plot (from the 2021 vintage). | ‘CODE_EPCI’ | The EPCI code of the municipality attached to the plot (from the 2021 vintage). | ‘CODE_DEPARTEMENT’ | Code of the department attached to the plot (from the 2021 vintage). | ‘CODE_REGION’ | | INSEE code of the region attached to the plot (from the 2021 vintage). Cutting and available formats We provide you with a download by region, for the CAP 2019, 2020, 2021 and 2022 campaigns. The proposed formats are Shapefile, and GeoJSON. These are geographical data, legible with software such as QGIS, [Google Earth Pro], or even the [GéoPortail]. Spatial data projections **Before the 2021 vintage: ** — Whole France: ESPG 2154 — Metropolitan France: ESPG 2154 — DROM: ESPG 4326 **From the 2021 vintage: ** — Whole France: ESPG 4326 — Metropolitan France: ESPG 2154 — Guadeloupe and Martinique: ESPG 5490 — Guyana: ESPG 2972 — Meeting: ESPG 2975 — Mayotte: ESPG 4471 Privacy and reuse The re-use of this data is free of charge for all uses, including commercial ones, under the terms of the “Open License” version 2.0. We invite you to deposit the fruit of your work on this data in the category Reuses: by creating an account on data.gouv.fr or by sending it to us at this email address cartobio@beta.gouv.fr. Other useful resources