Add draft re-write of intro docs

docs/intro.rst

@@ -1,16 +1,35 @@
 Overview
 ########
 
-This library combines geometry shape classes from shapely_ with CRS from pyproj_ to provide
-projection aware :py:class:`~odc.geo.geom.Geometry`. It exposes all the functionality provided by
-:py:mod:`shapely` modules, but will refuse operations between geometries defined in different
-projections. Geometries can be brought into a common projection with
-:py:meth:`~odc.geo.geom.Geometry.to_crs` method.
+The ``odc-geo`` library provides powerful tools for geospatial data manipulation, including working
+with coordinate reference systems, grid definitions, and spatial transformations.
 
-Based on that foundation a number of data types and utilities useful for working with geospatial
-metadata are implemented. Of particular importance is :py:class:`~odc.geo.geobox.GeoBox`. It is an
-abstraction for a geo-registered bounded pixel plane where a linear mapping from pixel coordinates
-to the real world is defined.
+The library integrates closely with the Python geospatial ecosystem (including shapely_ and pyproj_),
+providing a projection-aware :py:class:`~odc.geo.geom.Geometry` class that simplify complex geospatial
+operations. Geo-registered raster analysis is supported through an ``.odc.`` extension that is automatically
+added to :py:class:`xarray.Dataset` and :py:class:`xarray.DataArray` data loaded using the `Open Data Cube`_
+or rioxarray_ - exposing important geospatial metadata and a variety of useful geospatial analysis tools.
+
+Key features
+************
+
+* **Vector and raster reprojection:** Efficiently reproject data between CRSs using high-performance reprojection utilities powered by dask_
+* **Geospatial metadata handling:** Inspect and manipulate geo-registered raster metadata using a standardised :py:class:`~odc.geo.geobox.GeoBox` pixel grid model
+* **Spatial grid definition:** Generate precise :py:class:`~odc.geo.gridspec.GridSpec` tile grids for seamless large-scale analysis
+* **Cloud-optimised data:** Export geo-registered raster data into optimised cloud-optimised GeoTIFF files 
+* **Spatial analysis:** Tools for rasterising, masking, clipping vector and raster data 
+* **Interactive visualisation:** Plot and explore spatial vector and raster data on an interactive map
+
+.. This library combines geometry shape classes from shapely_ with CRS from pyproj_ to provide
+.. projection aware :py:class:`~odc.geo.geom.Geometry`. It exposes all the functionality provided by
+.. :py:mod:`shapely` modules, but will refuse operations between geometries defined in different
+.. projections. Geometries can be brought into a common projection with
+.. :py:meth:`~odc.geo.geom.Geometry.to_crs` method.
+
+.. Based on that foundation a number of data types and utilities useful for working with geospatial
+.. metadata are implemented. Of particular importance is :py:class:`~odc.geo.geobox.GeoBox`. It is an
+.. abstraction for a geo-registered bounded pixel plane where a linear mapping from pixel coordinates
+.. to the real world is defined.
 
 .. jupyter-execute::
       :stderr:
@@ -21,13 +40,13 @@ to the real world is defined.
          2_250_000, -1_000_000),
          "epsg:3577", resolution=1000)
 
-To make working with geo-registered raster data easier an integration with xarray_ is provided.
-Importing ``odc.geo.xr`` enables ``.odc.`` accessor on every :py:class:`xarray.Dataset` and
-:py:class:`xarray.DataArray` that exposes geospatial information of the raster loaded with `Open
-Datacube`_ or rioxarray_. Methods for attaching geospatial information to xarray objects in a robust
-way are also provided. Geospatial information attached in this way survives most operations you
-might do on the data: basic mathematical operations, type conversions, cropping, serialization to
-most formats like zarr, netcdf, GeoTIFF.
+.. To make working with geo-registered raster data easier an integration with xarray_ is provided.
+.. Importing ``odc.geo.xr`` enables ``.odc.`` accessor on every :py:class:`xarray.Dataset` and
+.. :py:class:`xarray.DataArray` that exposes geospatial information of the raster loaded with `Open
+.. Datacube`_ or rioxarray_. Methods for attaching geospatial information to xarray objects in a robust
+.. way are also provided. Geospatial information attached in this way survives most operations you
+.. might do on the data: basic mathematical operations, type conversions, cropping, serialization to
+.. most formats like zarr, netcdf, GeoTIFF.
 
 Installation
 ############
@@ -47,8 +66,9 @@ Using Conda
    conda install -c conda-forge odc-geo
 
 
+.. _dask: https://docs.dask.org/en/stable/
 .. _rioxarray: https://corteva.github.io/rioxarray/stable/
 .. _xarray: https://docs.xarray.dev/en/stable/
 .. _shapely: https://shapely.readthedocs.io/en/stable/manual.html
 .. _pyproj: https://pyproj4.github.io/pyproj/stable/
-.. _`Open Datacube`: https://github.com/opendatacube/datacube-core
+.. _`Open Data Cube`: https://github.com/opendatacube/datacube-core