{ "cells": [ { "cell_type": "markdown", "id": "326fe596", "metadata": {}, "source": [ "# tables, (x)arrays, and rasters\n", "\n", "Hi all,\n", "\n", "I've been playing around with some ideas for working with geospatial raster data. I'd be curious for any feedback you have.\n", "\n", "The core question: *what's the best data model for raster data in Python?* Unsurprisingly, I think the answer is \"it depends\". Let's use work through a concrete task and evaluate the various options. Suppose we wanted to compute NDVI for all the scenes captured by Landsat 8 over a couple of hours.\n", "\n", "We'll use the Planetary Computer's STAC API to find the scenes, and geopandas to plot the bounding boxes of each scene on a map." ] }, { "cell_type": "code", "execution_count": 1, "id": "31f58950", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Make this Notebook Trusted to load map: File -> Trust Notebook
" ], "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import warnings\n", "\n", "warnings.simplefilter(\"ignore\", FutureWarning)\n", "\n", "import pystac_client\n", "import geopandas\n", "import planetary_computer\n", "import pystac\n", "import pandas as pd\n", "\n", "catalog = pystac_client.Client.open(\n", " \"https://planetarycomputer.microsoft.com/api/stac/v1\"\n", ")\n", "\n", "items = catalog.search(\n", " collections=[\"landsat-8-c2-l2\"],\n", " datetime=\"2021-07-01T08:00:00Z/2021-07-01T10:00:00Z\"\n", ").get_all_items()\n", "\n", "\n", "items = [planetary_computer.sign(item) for item in items]\n", "items = pystac.ItemCollection(items, clone_items=False)\n", "df = geopandas.GeoDataFrame.from_features(items.to_dict(), crs=\"epsg:4326\")\n", "\n", "# https://github.com/geopandas/geopandas/issues/1208\n", "df[\"id\"] = [x.id for x in items]\n", "m = df[[\"geometry\", \"id\", \"datetime\"]].explore()\n", "m" ] }, { "cell_type": "markdown", "id": "a9d8f02c", "metadata": {}, "source": [ "This type of data *can* be represented as an xarray DataArray. But it's not the most efficient way to store the data:" ] }, { "cell_type": "code", "execution_count": 2, "id": "4d10a16a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ "\n", "dask.array\n", "Coordinates:\n", " * band (band) \n", "[,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ...\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ]\n", "Length: 117, dtype: stac" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import rasterpandas\n", "\n", "sa = rasterpandas.ItemArray(items)\n", "sa" ] }, { "cell_type": "markdown", "id": "e2dab726", "metadata": {}, "source": [ "That `ItemArray` can be put inside a pandas Series:" ] }, { "cell_type": "code", "execution_count": 6, "id": "9e626b19", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 \n", "1 \n", "2 \n", "3 \n", "4 \n", " ... \n", "112 \n", "113 \n", "114 \n", "115 \n", "116 \n", "Name: stac_items, Length: 117, dtype: stac" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "series = pd.Series(sa, name=\"stac_items\")\n", "series" ] }, { "cell_type": "markdown", "id": "9e72b9f1", "metadata": {}, "source": [ "Pandas lets you register accessors. For example, we could have a `stac` accessor that knows how to do stuff with STAC metadata, for example adding a column for each asset in the collection." ] }, { "cell_type": "code", "execution_count": 7, "id": "d616e747", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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