2f:[{"id":"46594f65cabaaf3241fe48ae674e8ccfb61d0af61142c22160d9919f2993ae57d792521f051da26688cc7b3d4d4dd3fa412a1673ec4b8b46c06e1004d8961c88","href":"https://static.observableusercontent.com/files/46594f65cabaaf3241fe48ae674e8ccfb61d0af61142c22160d9919f2993ae57d792521f051da26688cc7b3d4d4dd3fa412a1673ec4b8b46c06e1004d8961c88","name":"langfr-220px-GDALLogoColor.svg.png","type":"image/png","lastModified":1688130588485.27,"size":21932},{"id":"4d8906f48dd77b1ccfcb64b73a1c0e1c923bf03952942e546fdc3f972cd75fcf6a8f8e83ca913baf237ed599b6275d38415232805d8b147ba68dd458085977ac","href":"https://static.observableusercontent.com/files/4d8906f48dd77b1ccfcb64b73a1c0e1c923bf03952942e546fdc3f972cd75fcf6a8f8e83ca913baf237ed599b6275d38415232805d8b147ba68dd458085977ac","name":"afghanistan.gpkg","type":"application/x-sqlite3","lastModified":1688130588485.27,"size":315392},{"id":"6cb2d9cf7f9f79849417f32d428a29042adab21ac93978bb671637ab93d7028a4a20b1c6cfea0d009c1a33f34ee5dfd9b69b748009b93b2c9119600e97e4db16","href":"https://static.observableusercontent.com/files/6cb2d9cf7f9f79849417f32d428a29042adab21ac93978bb671637ab93d7028a4a20b1c6cfea0d009c1a33f34ee5dfd9b69b748009b93b2c9119600e97e4db16","name":"cuba.zip","type":"application/zip","lastModified":1688130588485.27,"size":181996},{"id":"890df102d43820c396d49d31a6b09e0d868ff9a9f39d04f3d925e897785eeb9d6b193b8d693a405de0cb8ae9b98f8559f532eda9b4919201976d91b31820639f","href":"https://static.observableusercontent.com/files/890df102d43820c396d49d31a6b09e0d868ff9a9f39d04f3d925e897785eeb9d6b193b8d693a405de0cb8ae9b98f8559f532eda9b4919201976d91b31820639f","name":"world.json","type":"application/json","lastModified":1688130588485.27,"size":254454},{"id":"c6451fc8bbbdc92058449cc72a2f59c5d3071420b0cd6c40dcd239809267c2b10d07c9e65fdabc4b74bd90c30fdb1a5600702c3dd2259f07aeae0b933c74ee3a","href":"https://static.observableusercontent.com/files/c6451fc8bbbdc92058449cc72a2f59c5d3071420b0cd6c40dcd239809267c2b10d07c9e65fdabc4b74bd90c30fdb1a5600702c3dd2259f07aeae0b933c74ee3a","name":"bolivia.kmz","type":"application/zip","lastModified":1688130588485.27,"size":287144},{"id":"59a27242a85634b1129f78059ab023b8f114889ed700e79a942759cb46136ab5dba4efc91c50d33872eac5f84aaa8d7dc35673c3b412d6e641650c307627bee7","href":"https://static.observableusercontent.com/files/59a27242a85634b1129f78059ab023b8f114889ed700e79a942759cb46136ab5dba4efc91c50d33872eac5f84aaa8d7dc35673c3b412d6e641650c307627bee7","name":"stage-20-parcours.gpx","type":"application/xml","lastModified":1688130608642.365,"size":955886}] 2d:{"title":"gdal3.js library","cells":[{"id":0,"value":"# `gdal3.js` library","pinned":false,"hidden":false,"mode":"md"},{"id":132,"value":"langfr220pxGdallogocolorSvg = FileAttachment(\"langfr-220px-GDALLogoColor.svg.png\").image()","pinned":false,"hidden":false,"mode":"ojs"},{"id":180,"value":"**`gdal.js`** is a JavaScript version of GDAL (Geospatial Data Abstraction Library) via WebAssembly. it allows you to read and handle a very wide range of geographic formats, both vector and raster. It's very powerful. But be careful, the library is very heavy.","pinned":false,"hidden":false,"mode":"md"},{"id":219,"value":"- **`npm`** - https://www.npmjs.com/package/gdal3.js\n- **`github`** - https://github.com/bugra9/gdal3.js\n- **`doc`** - https://gdal3.js.org/docs/","pinned":false,"hidden":false,"mode":"md"},{"id":224,"value":"Loading the library is a bit tricky. Thanks to [@rjsheperd](https://observablehq.com/@rjsheperd) for solving this complicated issue. See https://observablehq.com/@rjsheperd/gdal","pinned":false,"hidden":false,"mode":"md"},{"id":31,"value":"gdal = {\n const initGdalJS =\n await require(\"gdal3.js@2.4.0/dist/package/gdal3.js\").catch(\n () => window[\"initGdalJs\"]\n );\n return await initGdalJS({\n path: \"https://cdn.jsdelivr.net/npm/gdal3.js@2.4.0/dist/package\",\n useWorker: false\n }).then((Gdal) => Gdal);\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":177,"value":"You can also write : \n\n```js\nimport { gdal } from \"@rjsheperd/gdal\"\n```","pinned":false,"hidden":false,"mode":"md"},{"id":385,"value":"## Open any GIS formats","pinned":false,"hidden":false,"mode":"md"},{"id":523,"value":"In the context of observable notebooks, we'll use gdal3.js to import any type of geometry format. Here, we focus on vector data. ","pinned":false,"hidden":false,"mode":"md"},{"id":273,"value":"md`For that, **${\n Object.keys(gdal.drivers.vector).length\n} drivers** are available`","pinned":false,"hidden":false,"mode":"ojs"},{"id":266,"value":"Object.keys(gdal.drivers.vector).sort()","pinned":false,"hidden":false,"mode":"ojs"},{"id":432,"value":"Let's import a **geopackage**","pinned":false,"hidden":false,"mode":"md"},{"id":411,"value":"input = gdal.open(await FileAttachment(\"stage-20-parcours.gpx\")) // See https://gdal3.js.org/docs/module-f_open.html","pinned":true,"hidden":false,"mode":"ojs"},{"id":418,"value":"And let's see what's in the dataset","pinned":false,"hidden":false,"mode":"md"},{"id":421,"value":"gdal.getInfo(input.datasets[0]) // See https://gdal.org/programs/gdalinfo.html","pinned":true,"hidden":false,"mode":"ojs"},{"id":528,"value":"If you want to open a **shapefile**, then the geometries take the form of a zip file containing several files. You can proceed as follows. ","pinned":false,"hidden":false,"mode":"md"},{"id":490,"value":"shp = FileAttachment(\"cuba.zip\").zip()","pinned":true,"hidden":false,"mode":"ojs"},{"id":480,"value":"input2 = gdal.open(shp.filenames.map((d) => shp.file(d)))","pinned":true,"hidden":false,"mode":"ojs"},{"id":577,"value":"gdal.getInfo(input2.datasets[0])","pinned":true,"hidden":false,"mode":"ojs"},{"id":571,"value":"## Retrieve geoJSON","pinned":false,"hidden":false,"mode":"md"},{"id":616,"value":"**ogr2ogr** can be used to convert geometric data between file formats. In this case, we specify the output format (geoJSON) and projection (EPSG:4326). we specify this in an array of options. See https://gdal.org/programs/ogr2ogr.html#index-0","pinned":false,"hidden":false,"mode":"md"},{"id":471,"value":"mygeojson = JSON.parse(\n new TextDecoder().decode(\n await gdal.getFileBytes(\n await gdal.ogr2ogr(input.datasets[0], [\n \"-f\",\n \"GeoJSON\",\n \"-t_srs\",\n \"EPSG:4326\"\n ])\n )\n )\n)","pinned":true,"hidden":false,"mode":"ojs"},{"id":461,"value":"Plot.plot({\n marks: [Plot.geo(mygeojson)]\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":656,"value":"mygeojson2 = JSON.parse(\n new TextDecoder().decode(\n await gdal.getFileBytes(\n await gdal.ogr2ogr(input2.datasets[0], [\n \"-f\",\n \"GeoJSON\",\n \"-t_srs\",\n \"EPSG:4326\"\n ])\n )\n )\n)","pinned":true,"hidden":false,"mode":"ojs"},{"id":690,"value":"Plot.plot({\n marks: [Plot.geo(mygeojson2)]\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":594,"value":"## A useful function","pinned":false,"hidden":false,"mode":"md"},{"id":692,"value":"To speed things up, you can put all these steps into a single function. Thanks to [@mthh](https://observablehq.com/@mthh) for doing this. See https://observablehq.com/@mthh/geographical-features-imports-gdal3-js","pinned":false,"hidden":false,"mode":"md"},{"id":159,"value":"toGeoJSON = async (fileOrFiles, params = { opts: [], openOpts: [] }) => {\n const openOptions = params.openOpts || [];\n const input = await gdal.open(fileOrFiles, openOptions);\n const options = [\n \"-f\",\n \"GeoJSON\",\n \"-t_srs\",\n \"EPSG:4326\",\n \"-lco\",\n \"RFC7946=YES\"\n ].concat(params.opts || []);\n const output = await gdal.ogr2ogr(input.datasets[0], options);\n const bytes = await gdal.getFileBytes(output);\n await gdal.close(input);\n return JSON.parse(new TextDecoder().decode(bytes));\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":701,"value":"You can also write : \n\n```js\nimport {toGeoJSON} from \"@mthh/geographical-features-imports-gdal3-js\"\n```","pinned":false,"hidden":false,"mode":"md"},{"id":706,"value":"Then directly","pinned":false,"hidden":false,"mode":"md"},{"id":709,"value":"toGeoJSON(await FileAttachment(\"stage-20-parcours.gpx\"))","pinned":true,"hidden":false,"mode":"ojs"},{"id":310,"value":"## Projections","pinned":false,"hidden":false,"mode":"md"},{"id":383,"value":"Through parameters, ogr2ogr also lets us change projections. ","pinned":false,"hidden":false,"mode":"md"},{"id":735,"value":"Here, for example, I'm retrieving a geoJSON basemap in wgs84 and I want to transfrom it to epsg:3035.","pinned":false,"hidden":false,"mode":"md"},{"id":357,"value":"input3 = await gdal.open(await FileAttachment(\"world.json\"))","pinned":true,"hidden":false,"mode":"ojs"},{"id":753,"value":"mygeojson3 = JSON.parse(\n new TextDecoder().decode(\n await gdal.getFileBytes(\n await gdal.ogr2ogr(input3.datasets[0], [\n \"-f\",\n \"GeoJSON\",\n \"-t_srs\",\n \"EPSG:3035\"\n ])\n )\n )\n)","pinned":true,"hidden":false,"mode":"ojs"},{"id":774,"value":"Plot.plot({\n projection: { type: \"reflect-y\", domain: mygeojson3 },\n marks: [Plot.geo(mygeojson3)]\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":788,"value":"## Exports","pinned":false,"hidden":false,"mode":"md"},{"id":790,"value":"You may also wish to export geometry in a format other than geoJSON. Here, for example, we want to convert a kmz file to geopakage. ","pinned":false,"hidden":false,"mode":"md"},{"id":854,"value":"So, we import data","pinned":false,"hidden":false,"mode":"md"},{"id":801,"value":"input5 = FileAttachment(\"bolivia.kmz\")\n .zip()\n .then((d) => gdal.open(d.file(\"gadm41_BOL_2.kml\")))","pinned":true,"hidden":false,"mode":"ojs"},{"id":856,"value":"Then we export data","pinned":false,"hidden":false,"mode":"md"},{"id":883,"value":"blob = new Blob([\n await gdal.getFileBytes(\n await gdal.ogr2ogr(input5.datasets[0], [\n \"-f\",\n \"GPKG\",\n \"-t_srs\",\n \"EPSG:4326\"\n ])\n )\n])","pinned":true,"hidden":false,"mode":"ojs"},{"id":858,"value":"Now you can download the data and open it with [QGIS](https://www.qgis.org/)","pinned":false,"hidden":false,"mode":"md"},{"id":372,"value":"download = (downloadData, filename) => {\n const size = (downloadData.size / 1024).toFixed(0);\n const button = DOM.download(\n downloadData,\n filename,\n `Download ${filename} (~${size} KB)`\n );\n return button;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":886,"value":"download(blob, \"bolivia.gpkg\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":537,"value":"## Raster","pinned":false,"hidden":false,"mode":"md"},{"id":287,"value":"md`(${Object.keys(gdal.drivers.raster).length} drivers available)`","pinned":false,"hidden":false,"mode":"ojs"},{"id":269,"value":"Object.keys(gdal.drivers.raster).sort()","pinned":false,"hidden":false,"mode":"ojs"},{"id":345,"value":"

\n \n TODO

\nBuild examples with rasters

(feel free to fork this notebooks and suggest stuffs)

","pinned":false,"hidden":false,"mode":"html"},{"id":1056,"value":"## See also","pinned":false,"hidden":false,"mode":"md"},{"id":1058,"value":"thumbs = md`${previews([\n {\n path: \"@neocartocnrs/geoimport\",\n thumbnail:\n \"2c899b4bffe93c091c14b90215c644867758e2f0f3e1f954c95ee75735acc735\",\n title: \"Import any geographic features\"\n },\n {\n path: \"@neocartocnrs/projectgdal\",\n thumbnail:\n \"61d86776c9d64505cc689bc490544b1365fcd9bb23561934de89dd5907057fb0\",\n title: \"Project a geoJSON with gdal\"\n }\n])} \n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1052,"value":"import { previews } from \"@d3/gallery\"","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W2f","stdlib":"1","titling":"auto"}