Gist 0bdc45cce93d15fce0d1991d2b4b9e9d

100%

Gist 0bdc45cce93d15fce0d1991d2b4b9e9d

✓ Published0🌍 Public

Last edited Feb 8, 2022

Created on Feb 8, 2022

This example demonstrates how to convert a compressed LiDAR point-cloud dataset from IGN’s French national archive into a ZIP archive for easier distribution. The shell script downloads a 7z file via `wget`, decompresses it with `unp`, recompresses the contents into a ZIP using `zip`, and then reports the final disk usage with `du -cksh`. It relies on standard command-line utilities rather than JavaScript libraries, showing a practical data-preparation workflow for large geospatial files.

AI-generated description

Similar vizzes

Loading thumbnail…

7z vs zip avec données IGN

This example compares the storage size and remote read performance of 7z versus zip compression for French geographic data from the IGN ADMIN-EXPRESS-COG dataset. It shows that while 7z produces a smaller file (562M vs 693M), zip is vastly faster for remote consumption, with `ogrinfo` reading a shapefile in 0.504 seconds versus 36.717 seconds via the `vsicurl` virtual filesystem. The benchmark also experiments with GeoParquet, demonstrating even faster access at 0.338 seconds. Tests use GDAL's OGR tools for shapefile and Parquet reading.

TThomasG77

68% match

Loading thumbnail…

Gist 4ab7840c00aa352f278636ea3f3f0d3e

This example demonstrates streaming merge of two French cadastral parcel GeoJSON files into a single unified feature collection, output as a continuous JSON stream to standard output. It shows how to combine multiple geospatial datasets without loading them entirely into memory. The code uses the `geojson-stream` library for parsing and stringifying GeoJSON features, Node.js `fs.createReadStream` for file input, and the `zlib` module to decompress gzipped source files. Data originates from the Etalab cadastre dataset, with separate scripts handling zipped and unzipped input files.

TThomasG77

65% match

Loading thumbnail…

Gist 349fc7fda8932252cbcb4ee8a0b605c8

This example tracks the availability and licensing of the French IGN’s BD TOPO® departmental datasets by organizing URLs for both December 2021 and March 2021 releases. It shows a structured index of geographic data files, with each entry linking to a specific departmental Shapefile resource. The visualization uses a data-driven list format, likely rendered with JavaScript’s `fetch` to parse the provided CSV or JSON and display the sorted URLs. It highlights the variation in resource IDs across departments and time periods, emphasizing the dataset’s scope without mapping or spatial rendering.

TThomasG77

62% match

Loading thumbnail…

Process to convert laz file to DTM

This example demonstrates a command-line workflow for converting a LIDAR point cloud in LAZ format into a digital terrain model (DTM) as a GeoTIFF raster. It shows two approaches using the PDAL library, both relying on its `pipeline` command and `translate` subcommand, paired with the `writers.gdal` writer to output a 0.5-meter resolution TIFF. The process ingests a sample LAZ file from the French IGN LIDAR HD dataset, then uses PDAL’s GDAL writer to generate the DTM, with a failed attempt at using the `writers.ogr` output to shapefile also noted.

TThomasG77

62% match

Loading thumbnail…

Export DVF geolocalisées en Geoparquet

This example converts French land-registry transaction data (DVF) from compressed CSV files into a single spatially-indexed GeoParquet file. It downloads yearly datasets from data.gouv.fr, loads them into a DuckDB database, and creates a point geometry for each transaction before exporting with a Hilbert-curve spatial ordering. The script leverages DuckDB’s spatial extension, the `ST_Point` and `ST_Hilbert` functions, and parquet compression via zstd.

TThomasG77

61% match

Loading thumbnail…

Gist 8dafcac4cab52f5242b00c077430c08d

This example demonstrates how to select cadastral parcels from the IGN Cadastre API based on a drawn polygon’s extent. It shows the process of converting a GeoJSON polygon into an encoded URL parameter, then sending a GET request via Bash with `jq` or Python with `urllib.parse` and `urllib.request`. The API returns parcel geometries, which are saved as a GeoJSON file. The code also warns against using overly complex or overly large geometries due to URL length limits and server load.

TThomasG77

60% match

Loading thumbnail…

Gist ab738ee0d721842fbea501cc96296160

This example demonstrates how to load and manage LiDAR point cloud layers in QGIS using Python, showing both automatic and manual index generation for LAZ files from the French IGN LIDAR HD dataset. It uses the QgsPointCloudLayer class with the PDAL provider, QgsProject for layer management, and QgsPointCloudDataProvider to check indexing state, generate indexes, and retrieve polygon bounds, point counts, and renderer information. The script also prints coordinate reference system details and renderer type, illustrating direct provider-level access to point cloud metadata.

TThomasG77

60% match

Loading thumbnail…

Gist 4784ecb60635f2f453efe59c8817f188

This visualization shows the resulting file structure and extraction output from the command-line tool Unrar, specifically using the `e` and `x` flags to either flatten or preserve directory hierarchies. The code demonstrates a terminal-based workflow for handling `.rar` archives, initially updating the Ubuntu package manager with `sudo apt-get update` and installing Unrar via `sudo apt-get install unrar`. The rendering approach relies on the plain-text output of the terminal commands, dynamically displaying the unrar operation results, including the target file `Nueva_carpeta__1_.rar` being processed from a path like `/home/work/software/`. The visualization appears to function by capturing and mapping the command execution flow, likely through a custom data processing system that parses each command and its output to illustrate the archive extraction process without invoking external visualization libraries.

MMistyBlunch

60% match