Data formats tutorial

data_type/readme.md

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+# Tutorial: Geospatial Data Formats
+
+Geospatial data allows the communication of information in a wide range of fields. Over time,
+many data formats have emerged to support this diversity. This tutorial introduces these various
+formats, their evolution, their advantages and disadvantages, and provides guidance on how and in
+which contexts to use them most effectively. It will be divided into four jupyter notebooks:
+
+1) [Raster formats](./raster_formats.ipynb)
+2) [Vector formats](./vector_data_formats.ipynb)
+3) [Data cube formats](./datacube_formats.ipynb)
+4) [Point clouds](./point_clouds.ipynb)
+5) [Data optimization](./data_optimization.ipynb)
+
+For each tutorial, sample data will be provided in the `sample_data` directory or automatically
+downloaded (in the `sample_data` directory by default)
+
+
+To run a notebook, first create a virtual environment using pip. Then, install the required 
+packages (if not already done), using the `requirements.txt` file.
+
+```bash
+python3.11 -m venv venv
+pip install -r requirements.txt
+```
+
+Then you can simply use `jupyter notebook xxx.ipynb` to run the corresponding notebook.

data_type/requirements.txt

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+Cartopy==0.24.1
+dask==2024.11.2
+fsspec==2024.10.0
+geopandas==1.0.1
+gcsfs==2024.10.0
+geoviews==1.13.0
+h5py==3.12.1
+holoviews==1.20.0
+hvplot==0.11.1
+jupyter==1.1.1
+laspy[lazrs,laszip]==2.5.4
+matplotlib==3.9.2
+netCDF4==1.7.2
+numpy==2.1.3
+pandas==2.2.3
+panel==1.5.4
+plotly==5.24.1
+pyarrow==18.0.0
+rasterio==1.4.2
+Requests==2.32.3
+rio_cogeo==5.3.6
+rioxarray==0.18.0
+scipy==1.14.1
+xarray==2024.10.0
+zarr==2.18.3