From ee53f5dd47aa3be571108f2ec21529a6edd20d44 Mon Sep 17 00:00:00 2001 From: Kendall Smith Date: Thu, 12 May 2022 14:48:40 -0700 Subject: [PATCH 1/2] fixed fork divergence --- ...adiant-mlhub-on-demand-training-data.ipynb | 3532 +++++++++++++++++ 1 file changed, 3532 insertions(+) create mode 100644 tutorials/radiant-mlhub-on-demand-training-data.ipynb diff --git a/tutorials/radiant-mlhub-on-demand-training-data.ipynb b/tutorials/radiant-mlhub-on-demand-training-data.ipynb new file mode 100644 index 00000000..6a826c36 --- /dev/null +++ b/tutorials/radiant-mlhub-on-demand-training-data.ipynb @@ -0,0 +1,3532 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a7cf916c-0991-4af6-889f-a1ce86696d46", + "metadata": {}, + "source": [ + "## On Demand Training Data from Radiant MLHub and Planetary Computer\n", + "\n", + "Radiant MLHub Logo" + ] + }, + { + "cell_type": "markdown", + "id": "010b5b89-32c4-4f6b-81fb-f41d782d251f", + "metadata": {}, + "source": [ + "In this tutorial, we will walk through the process of requesting on-demand traning data from the [Planetary Computer Data Catalog](https://planetarycomputer.microsoft.com/catalog) to pair with the [BigEarthNet](https://mlhub.earth/data/bigearthnet_v1) dataset downloaded from Radiant MLHub. This is an important workflow for someone in the geospatial community who wants to train an ML model on a datasource outside of a prepackaged dataset, such as those found on MLHub. They can start with any dataset containing source image and label collections in STAC, obtain a random sample to work with, fetch source images from a different collection or satellite product, and then reproject and crop those images to match the spatial and temporal extent of the original dataset.\n", + "\n", + "**NOTE:** because the workflow documented below uses libraries like `pystac_client` and `stackstac`, the datasets queried need to be organized into STAC Collections." + ] + }, + { + "cell_type": "markdown", + "id": "f130b365-6fff-4d2a-86a9-39085ab13886", + "metadata": {}, + "source": [ + "Let's start by importing the Python libraries we'll use in this notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9fe7b447-cf8a-4cc7-aaed-4e8407d0f270", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install --upgrade wget # not installed on PC by default" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "7e144460-5549-4ab4-ba98-10a1a7ebd236", + "metadata": {}, + "outputs": [], + "source": [ + "import getpass\n", + "import tempfile\n", + "from pathlib import Path\n", + "import os\n", + "import json\n", + "from glob import glob\n", + "import requests\n", + "from typing import List, Tuple\n", + "from datetime import datetime as dt\n", + "from datetime import timedelta as td\n", + "\n", + "from radiant_mlhub import Collection\n", + "import planetary_computer\n", + "import pystac_client\n", + "from pystac import ItemCollection, Item, Asset\n", + "import dask\n", + "\n", + "import numpy as np\n", + "from stackstac import stack\n", + "from geopandas import GeoDataFrame\n", + "import rasterio as rio\n", + "import rioxarray\n", + "from xarray import DataArray\n", + "from shapely.geometry import shape\n", + "from shapely.geometry import Polygon\n", + "from pyproj import CRS" + ] + }, + { + "cell_type": "markdown", + "id": "3a5bb87b-9d9c-4140-bac8-3e95f146c029", + "metadata": {}, + "source": [ + "### Define global variables" + ] + }, + { + "cell_type": "markdown", + "id": "2de2f657-3229-4037-bf9c-541c503cc269", + "metadata": {}, + "source": [ + "In addition to the API key, we will also need to define some other initial global variables to get our workflow started. e.g. a temporary working directory to download and write data to, the STAC API endpoints, names of Collections, and other variables like the RGB bands for those collections. These are pretty flexible depending on your individual needs." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f3bf07a1-3a4a-4207-a449-7be766fa7e36", + "metadata": {}, + "outputs": [], + "source": [ + "# Temporary working directory on local machine or PC instance\n", + "TMP_DIR = tempfile.gettempdir()\n", + "\n", + "# API endpoints for MLHub and Planetary Computer catalogs\n", + "MLHUB_API_URL = \"https://api.radiant.earth/mlhub/v1\"\n", + "MSPC_API_URL = \"https://planetarycomputer.microsoft.com/api/stac/v1\"\n", + "\n", + "# Names of Collections that will be queried against using pystac_client\n", + "BIGEARTHNET_SOURCE_COLLECTION = \"bigearthnet_v1_source\" # sentinel-2 source imagery\n", + "BIGEARTHNET_LABEL_COLLECTION = \"bigearthnet_v1_labels\" # geojson classification labels\n", + "PLANETARY_COMPUTER_LANDSAT_8 = \"landsat-8-c2-l2\" # landsat 8 source imagery on PC\n", + "OUTPUT_DIR = \"landsat_8_source\"\n", + "\n", + "# Default variables that will be used in the API queries\n", + "BIGEARTHNET_TIME_RANGE = \"2017-06-01/2018-05-31\" # full date range for BigEarthNet\n", + "LABEL_CRS = CRS(\"EPSG:4326\")\n", + "DATE_BUFFER = 60\n", + "LANDSAT_8_RGB_BANDS = [\"SR_B4\", \"SR_B3\", \"SR_B2\"] # names of RGB bands from BigEarthNet\n", + "BIGEARTHNET_RGB_BANDS = [\"B04\", \"B03\", \"B02\"] # names of RGB bands from PC Landsat 8\n", + "\n", + "# Bounding box for demonstration fetching Items over Luxembourg\n", + "LUXEMBOURG_AOI = [6.06, 49.58, 6.21, 49.66] # aoi around Luxembourg" + ] + }, + { + "cell_type": "markdown", + "id": "5d5e31b3-5cde-4a7b-af43-dc194b06d0a0", + "metadata": {}, + "source": [ + "### Authentication with Radiant MLHub" + ] + }, + { + "cell_type": "markdown", + "id": "5f11e821-b98b-4df1-a26c-826c9bdbec50", + "metadata": {}, + "source": [ + "Programmatic access to the Radiant MLHub API using the `pystac_client` library requires both the API end-point and an API key. You can obtain an API key for free by registering an account on [mlhub.earth](https://mlhub.earth/). This can be found under `Settings & API Key` from the drop-down once logged in." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f4c9dd60-3abc-464d-af25-4b23c0d2783b", + "metadata": {}, + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "MLHub API Key: ································································\n" + ] + } + ], + "source": [ + "MLHUB_API_KEY = getpass.getpass(prompt=\"MLHub API Key: \")" + ] + }, + { + "cell_type": "markdown", + "id": "ccad2439-109f-4eef-ac3b-dac65f54e3aa", + "metadata": {}, + "source": [ + "Once you have your API key, you need to update the default profile file in your home directory. You can use the `mlhub configure` command line tool to do this:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cfd2681d-56ed-440c-a352-b4ecfd125c03", + "metadata": {}, + "outputs": [], + "source": [ + "!mlhub configure --api-key={MLHUB_API_KEY}" + ] + }, + { + "cell_type": "markdown", + "id": "5c77d029-191e-42a5-8250-bc451b80f247", + "metadata": {}, + "source": [ + "### Configure API connection to Radiant MLHub" + ] + }, + { + "cell_type": "markdown", + "id": "b0480635-eef5-4e3f-847a-5060c409ae4f", + "metadata": {}, + "source": [ + "This makes a connection to the Radiant MLHub Data Catalog using the API endpoint URL, and the API key from your account." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "bf79c301-76df-4158-bf97-3da53552e143", + "metadata": {}, + "outputs": [], + "source": [ + "mlhub_catalog = pystac_client.Client.open(\n", + " url=MLHUB_API_URL, parameters={\"key\": MLHUB_API_KEY}, ignore_conformance=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "637734bc-af77-4c81-ab26-98e808de6415", + "metadata": {}, + "source": [ + "### Fetch label items from BigEarthNet over Luxembourg" + ] + }, + { + "cell_type": "markdown", + "id": "e9f19ce4-57fd-43d0-9263-7a5edd106ee8", + "metadata": {}, + "source": [ + "We will now use the `search` function from the API client to get label Items over Luxembourg as a sample use-case." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "fc61a3d4-cfc0-4da5-9717-bf3c8e427100", + "metadata": {}, + "outputs": [], + "source": [ + "origin_label_items = mlhub_catalog.search(\n", + " collections=BIGEARTHNET_LABEL_COLLECTION,\n", + " bbox=LUXEMBOURG_AOI,\n", + " datetime=BIGEARTHNET_TIME_RANGE,\n", + ").get_all_items()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "917c392c-9ae5-43e2-b7e0-71dd9f749cc2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "178" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(origin_label_items)" + ] + }, + { + "cell_type": "markdown", + "id": "e9d121a9-e54b-4039-9cef-04277962c2ca", + "metadata": {}, + "source": [ + "This is another helper function that simply displays the geometry for labels from an ItemCollection overlayed on a map of the region." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "2f203e31-5b09-4f2e-bf27-9a3ef8e3fc4d", + "metadata": {}, + "outputs": [], + "source": [ + "def explore_search_extent(items: ItemCollection) -> None:\n", + " \"\"\"Extracts geometry from ItemCollection to display polygons on a map.\n", + "\n", + " Args:\n", + " items: ItemCollection of Items retrieved from pystac_client search\n", + "\n", + " Returns:\n", + " GeoDataFrame object with the .explore() method called\n", + " \"\"\"\n", + " item_feature_collection = items.to_dict()\n", + " geom_df = GeoDataFrame.from_features(item_feature_collection).set_crs(4326)\n", + " print(geom_df.bounds)\n", + " return geom_df[[\"geometry\", \"datetime\"]].explore(\n", + " column=\"datetime\", style_kwds={\"fillOpacity\": 0.2}, cmap=\"viridis\"\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "ab923855-3c20-4f10-af00-d4175a54fdd4", + "metadata": {}, + "source": [ + "Here are the BigEarthNet chips with their bounding boxes that matched the spatial parameters for the city of Luxembourg and surrounding areas." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b86bf5d8-8dc8-491d-b3cd-1ea1263774ca", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " minx miny maxx maxy\n", + "0 6.197958 49.579464 6.215240 49.590700\n", + "1 6.198663 49.590240 6.215949 49.601477\n", + "2 6.199368 49.601017 6.216659 49.612254\n", + "3 6.180682 49.569146 6.197958 49.580381\n", + "4 6.181383 49.579923 6.198663 49.591158\n", + ".. ... ... ... ...\n", + "173 6.151709 49.634721 6.169003 49.645951\n", + "174 6.152406 49.645498 6.169703 49.656729\n", + "175 6.153102 49.656275 6.170404 49.667506\n", + "176 6.135808 49.645951 6.153102 49.657180\n", + "177 6.136501 49.656729 6.153800 49.667957\n", + "\n", + "[178 rows x 4 columns]\n" + ] + }, + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "explore_search_extent(origin_label_items)" + ] + }, + { + "cell_type": "markdown", + "id": "13c6e607-c79b-4678-a483-9bacb0b3b1df", + "metadata": {}, + "source": [ + "### Download the entire label collection for BigEarthNet from Radiant MLHub" + ] + }, + { + "cell_type": "markdown", + "id": "2e131160-50bb-487f-8fcb-96d37ce80167", + "metadata": {}, + "source": [ + "We could certainly use the method above to query label Items directly from our connection to the Radiant MLHub API endpoint. However, on very large collections, such as in the case with BigEarthNet, pagination becomes a bottleneck issue in obtaining and resolving STAC items, as it only returns 100 items at a time. Querying the entire Collection of nearly ~600,000 Items could take hours.\n", + "\n", + "Therefore, downloading the label Collection (which is only 160 MB) directly is preferrable to paginating over the entire Collection using the API." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "93f67824-bf8f-4cce-862e-856d9cfed26d", + "metadata": {}, + "outputs": [], + "source": [ + "label_collection_path = os.path.join(\n", + " TMP_DIR, BIGEARTHNET_LABEL_COLLECTION, \"collection.json\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "21bd7e52-6bc7-4a8d-bf56-060e80f4019b", + "metadata": {}, + "source": [ + "Check if collection folder already exists before downloading 173 mb dataset. Otherwise download and uncompress the `.tar.gz` file to extract the label collection files." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "81f5a8c4-c604-4f8a-8fb9-1d90607206ee", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Archive file already downloaded from Radiant MLHub, skipping...\n" + ] + } + ], + "source": [ + "if not os.path.exists(label_collection_path):\n", + " collection = Collection.fetch(BIGEARTHNET_LABEL_COLLECTION)\n", + " archive_path = collection.download(TMP_DIR)\n", + " !tar -xf {archive_path.as_posix()} -C {TMP_DIR}\n", + "else:\n", + " print(\"Archive file already downloaded from Radiant MLHub, skipping...\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "21e2683b-edde-43e0-8bf2-b791a8e04dc8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['bigearthnet_v1_labels_S2B_MSIL2A_20170914T93030_63_71',\n", + " 'bigearthnet_v1_labels_S2B_MSIL2A_20180506T105029_52_1',\n", + " 'bigearthnet_v1_labels_S2B_MSIL2A_20180509T092029_4_62',\n", + " 'bigearthnet_v1_labels_S2B_MSIL2A_20180525T94030_57_6',\n", + " 'bigearthnet_v1_labels_S2A_MSIL2A_20170717T113321_65_3']" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bigearthnet_dir = os.listdir(os.path.join(TMP_DIR, BIGEARTHNET_LABEL_COLLECTION))\n", + "bigearthnet_dir[0:5]" + ] + }, + { + "cell_type": "markdown", + "id": "e1c28041-79f1-4ebd-a09a-14d440ac2757", + "metadata": {}, + "source": [ + "This is the total count of label Item (chip) directories, plus one for the STAC Collection itself." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "1f270583-b7a5-4e37-8a50-2f9f4e49fdaf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "590327" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(bigearthnet_dir)" + ] + }, + { + "cell_type": "markdown", + "id": "954d358d-54ff-4300-89e5-43f16e452574", + "metadata": {}, + "source": [ + "### Obtain a random sample of label Items from BigEarthNet" + ] + }, + { + "cell_type": "markdown", + "id": "670ad5d5-458f-4f56-88d9-574e5c37ba26", + "metadata": {}, + "source": [ + "We don't want to work with the entire dataset of nearly 600,000 labels. This would take too long to download, and we likely won't have enough disk space or space in memory, so let's work with a random sample of the dataset that is 10% of the original size." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "ff4ad2d8-7cbd-4bc4-a1d2-f6bcd0c9fcf8", + "metadata": {}, + "outputs": [], + "source": [ + "assert os.path.exists(label_collection_path)\n", + "with open(label_collection_path, \"r\") as in_file:\n", + " collection_data = json.load(in_file)" + ] + }, + { + "cell_type": "markdown", + "id": "8c8b0c28-9dee-4f83-b3fe-a49079a530dd", + "metadata": {}, + "source": [ + "This confirms we have all of the label Items STAC objects and image data from the collection" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "9ffac3d1-4cf1-4bf8-b066-6de4fa1f4da1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "590326" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "label_item_links = [\n", + " link[\"href\"] for link in collection_data[\"links\"] if link[\"rel\"] == \"item\"\n", + "]\n", + "len(label_item_links)" + ] + }, + { + "cell_type": "markdown", + "id": "b9836f40-4228-4d72-8b84-d94cae1030c6", + "metadata": {}, + "source": [ + "Now we take a random sample that is 1/100th the original dataset size" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "97b8f303-08c8-4353-847e-fc4d712edac6", + "metadata": {}, + "outputs": [], + "source": [ + "label_item_sample = np.random.choice(\n", + " a=label_item_links, size=int(len(label_item_links) / 100), replace=False\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "a05b5914-ddf8-4332-87f9-4e631e71110a", + "metadata": {}, + "outputs": [], + "source": [ + "first_label_item = Item.from_file(\n", + " os.path.join(\n", + " TMP_DIR,\n", + " BIGEARTHNET_LABEL_COLLECTION,\n", + " label_item_sample[np.random.randint(len(label_item_sample))],\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "65959f74-4d1d-4ce4-8583-bc51d6047ce1", + "metadata": {}, + "source": [ + "Chip ID for the sample label Item pulled:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "4420d40b-2cd3-4f74-b271-a137ca71e360", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'bigearthnet_v1_labels_S2A_MSIL2A_20180413T95032_86_10'" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "first_label_item.id" + ] + }, + { + "cell_type": "markdown", + "id": "c0b0734b-ce42-48de-8bea-f4c4f5e9b37f", + "metadata": {}, + "source": [ + "Links for the sample label Item, take special note of the `rel=source` Link listed:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "42d94501-6305-4329-aa4f-ef69802951b8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "first_label_item.links" + ] + }, + { + "cell_type": "markdown", + "id": "5d23fb83-400e-487b-940f-fc158b5ae41b", + "metadata": { + "tags": [] + }, + "source": [ + "### Fetch source items for random sample from BigEarthNet" + ] + }, + { + "cell_type": "markdown", + "id": "e419d96f-f8b9-4911-b5d1-4a4077767062", + "metadata": {}, + "source": [ + "If we had the source collection archive downloaded and uncompressed in the same parent directory as the labels collection, we could reference the source Items and images directly. However the BigEarthNet source collection is over 60GB when compressed. Therefore to work around the disk size limitations of a Planetary Computer instance, we can query the same source items from the MLHub API endpoint, the same way we got the labels, but filter to the exact source item using IDs." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "fa334c57-9dbe-495e-b040-980adf1192c4", + "metadata": {}, + "outputs": [], + "source": [ + "def get_source_item_ids(label_item: Item) -> List[str]:\n", + " return [\n", + " link.href.split(\"/\")[-2] for link in label_item.links if link.rel == \"source\"\n", + " ]" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "f4fa6f4b-8463-40a4-8aec-527d868be5e2", + "metadata": {}, + "outputs": [], + "source": [ + "origin_source_items = mlhub_catalog.search(\n", + " collections=[BIGEARTHNET_SOURCE_COLLECTION],\n", + " ids=get_source_item_ids(first_label_item),\n", + ").get_all_items()" + ] + }, + { + "cell_type": "markdown", + "id": "03e775fa-dd60-4623-8d44-3d83beafcb2c", + "metadata": {}, + "source": [ + "This is the number of source items that match the query parameters we sent to the MLHub API using the first label's bounding box and datetime properties." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "2c98a5d6-1a55-466a-b768-4433cea148ca", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(origin_source_items)" + ] + }, + { + "cell_type": "markdown", + "id": "c0ba4eaf-03d8-4e56-92c8-4e98b26ba983", + "metadata": {}, + "source": [ + "Taking a look at some of the properties of the first source Item found:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "854abe53-e1b7-452a-8aa2-c139aa220348", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bigearthnet_v1_source_S2A_MSIL2A_20180413T95032_86_10\n", + "2018-04-13 09:50:32+00:00\n", + "[25.247600449332744, 60.30639355471977, 25.269882490837322, 60.317447602103464]\n", + "{'gsd': 30, 'datetime': '2018-04-13T09:50:32Z', 'eo:bands': [{'name': 'B01', 'common_name': 'Coastal Aerosol', 'description': 'Coastal Aerosol'}, {'name': 'B02', 'common_name': 'Blue', 'description': 'Blue'}, {'name': 'B03', 'common_name': 'Green', 'description': 'Green'}, {'name': 'B04', 'common_name': 'Red', 'description': 'Red'}, {'name': 'B05', 'common_name': 'Vegetation Red Edge', 'description': 'Vegetation Red Edge (704.1nm)'}, {'name': 'B06', 'common_name': 'Vegetation Red Edge', 'description': 'Vegetation Red Edge (740.1nm)'}, {'name': 'B07', 'common_name': 'Vegetation Red Edge', 'description': 'Vegetation Red Edge (782.8nm)'}, {'name': 'B08', 'common_name': 'NIR', 'description': 'NIR'}, {'name': 'B8A', 'common_name': 'Narrow NIR', 'description': 'Narrow NIR'}, {'name': 'B09', 'common_name': 'Water Vapour', 'description': 'Water Vapour'}, {'name': 'B11', 'common_name': 'SWIR', 'description': 'SWIR (1613.7nm)'}, {'name': 'B12', 'common_name': 'SWIR', 'description': 'SWIR (2202.4nm)'}], 'platform': 'Sentinel-2', 'instruments': ['MSI'], 'constellation': 'Sentinel-2'}\n" + ] + } + ], + "source": [ + "for source_item in origin_source_items:\n", + " print(source_item.id)\n", + " print(source_item.datetime)\n", + " print(source_item.bbox)\n", + " print(source_item.properties)\n", + " break" + ] + }, + { + "cell_type": "markdown", + "id": "5303475f-d29b-4e84-82ef-d355ef0519de", + "metadata": {}, + "source": [ + "With the properties from this sample source Item, we can observe where the chip is located, the relevant Sentinel-2 bands (assets) and datetime the image was captured." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "01d88ffe-f530-46d6-87d9-4337c1ec0202", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " minx miny maxx maxy\n", + "0 25.2476 60.306394 25.269882 60.317448\n" + ] + }, + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "explore_search_extent(origin_source_items)" + ] + }, + { + "cell_type": "markdown", + "id": "77109ba6-f0a1-426e-8884-39a5fac2795f", + "metadata": {}, + "source": [ + "This is the location of the source items fetched from the label Items sample." + ] + }, + { + "cell_type": "markdown", + "id": "a5e0cbba-32b8-47ad-9913-e7ba2a939922", + "metadata": { + "tags": [] + }, + "source": [ + "### Fetch Landsat 8 scenes based on source Item bbox and datetime" + ] + }, + { + "cell_type": "markdown", + "id": "4392b23e-eebb-4a53-88d8-f49fbfacfaf1", + "metadata": {}, + "source": [ + "Configure API connection for the microsoft planetary computer stac endpoint" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "1eddda32-d50d-413d-add8-dedaa2f9a067", + "metadata": {}, + "outputs": [], + "source": [ + "def temporal_buffer(item_datetime: str, date_delta: int) -> str:\n", + " \"\"\"Takes a datetime string and returns a buffer around that date\n", + "\n", + " Args:\n", + " item_datetime: string of the datetime property from an Item\n", + " date_delta: integer for days to add before and after a date\n", + "\n", + " Returns:\n", + " a string range representing the full date buffer\n", + " \"\"\"\n", + " delta = td(days=date_delta)\n", + " item_dt = dt.strptime(item_datetime, \"%Y-%m-%dT%H:%M:%SZ\")\n", + "\n", + " dt_start = item_dt - delta\n", + " dt_start_str = dt_start.strftime(\"%Y-%m-%d\")\n", + "\n", + " dt_end = item_dt + delta\n", + " dt_end_str = dt_end.strftime(\"%Y-%m-%d\")\n", + "\n", + " return f\"{dt_start_str}/{dt_end_str}\"" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "f77e4ec0-a1b8-490c-b732-4c10d89b06ea", + "metadata": {}, + "outputs": [], + "source": [ + "def min_cloud_cover_scene(label_geom: Polygon, search_items: ItemCollection) -> Item:\n", + " \"\"\"Finds the Item with minimal cloud cover from an ItemCollection\n", + "\n", + " Args:\n", + " label_geom: Polygon geometry to ensure label completely within scene\n", + " search_items: ItemCollection of the Items found from pystac_client search\n", + "\n", + " Returns:\n", + " Item where label completely contained within, and minimal cloud cover\n", + " \"\"\"\n", + " min_cc = np.inf\n", + " min_cc_item = None\n", + " for item in search_items:\n", + " item_geom = shape(item.geometry)\n", + " item_cc = item.properties[\"eo:cloud_cover\"]\n", + " if item_cc < min_cc and label_geom.within(item_geom):\n", + " min_cc = item_cc\n", + " min_cc_item = item\n", + " return min_cc_item" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "c3b23fa5-d588-42d4-b913-fab7819e7ea3", + "metadata": {}, + "outputs": [], + "source": [ + "def get_landsat_8_match(label_item: Item) -> Tuple[Item, Item]:\n", + " \"\"\"Finds the best Landsat 8 match using source Item datetime and bounding box.\n", + "\n", + " Args:\n", + " label_item: the STAC label Item object\n", + "\n", + " Returns:\n", + " Tuple of the BigEarthNet source Item and the Landsat 8 match Item\n", + " \"\"\"\n", + " # get the matching source Item properties\n", + " source_items = mlhub_catalog.search(\n", + " collections=[BIGEARTHNET_SOURCE_COLLECTION],\n", + " ids=get_source_item_ids(label_item),\n", + " ).get_all_items()\n", + "\n", + " if source_items:\n", + " source_item = source_items[0]\n", + " source_bbox = source_item.bbox\n", + " source_datetime = source_item.properties[\"datetime\"]\n", + "\n", + " # search PC Catalog for L8 Items\n", + " l8_items = mspc_catalog.search(\n", + " collections=PLANETARY_COMPUTER_LANDSAT_8,\n", + " bbox=source_bbox,\n", + " datetime=temporal_buffer(source_datetime, DATE_BUFFER),\n", + " ).get_all_items()\n", + "\n", + " # filter to best L8 Item match\n", + " signed_l8_items = planetary_computer.sign(l8_items)\n", + " best_l8_match = min_cloud_cover_scene(\n", + " shape(source_item.geometry), signed_l8_items\n", + " )\n", + "\n", + " if not best_l8_match:\n", + " print(\n", + " \"No Landsat 8 Item was found on the Planetary \"\n", + " \"Computer matching the query parameters:\"\n", + " )\n", + " print(\n", + " f\"Source Item ID: {source_item.id} \"\n", + " f\"Bbox: {source_bbox}, \"\n", + " f\"Datetime: {source_datetime}\"\n", + " )\n", + " best_l8_match = None\n", + " else:\n", + " print(\n", + " \"No Sentinel-2 source Item was found in the \"\n", + " \"BigEarthNet dataset matching that label item!\"\n", + " )\n", + " source_item = None\n", + " return source_item, best_l8_match" + ] + }, + { + "cell_type": "markdown", + "id": "69ad7ef1-143e-47f0-b6b9-26c73e5cc65d", + "metadata": {}, + "source": [ + "Since it is known that the BigEarthNet dataset from MLHub has a 1-to-1 pairing of source and labels, we can safely assume the first source item is the appropriate match for our label." + ] + }, + { + "cell_type": "markdown", + "id": "ab77ef46-b54a-42e7-a780-bc8e8094ec6f", + "metadata": {}, + "source": [ + "This makes a connection to the Planetary Computer Data Catalog using the API endpoint URL." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "38280716-e130-4de4-9699-2c0bcbfc056d", + "metadata": {}, + "outputs": [], + "source": [ + "mspc_catalog = pystac_client.Client.open(MSPC_API_URL)" + ] + }, + { + "cell_type": "markdown", + "id": "39f35d02-d2de-4be5-9317-c80214502c88", + "metadata": {}, + "source": [ + "We will now use the API client with the helper function above to fetch the best Landsat 8 match for the sampled label Item. This will find only the scenes where the label is completely within the scene, and there is minimal cloud cover." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "53d3ab14-86c7-42a8-be96-8156f5e74d64", + "metadata": {}, + "outputs": [], + "source": [ + "source_item, best_l8_match = get_landsat_8_match(first_label_item)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "53556cf0-375b-4044-9262-d16de707a13d", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LC08_L2SP_187018_20180510_02_T1\n", + "[24.323999281168234, 58.95366546927769, 28.882648861314085, 61.187374530722316]\n", + "{'datetime': '2018-05-10T09:22:33.464049Z', 'platform': 'landsat-8', 'proj:bbox': [356085.0, 6537585.0, 601215.0, 6785115.0], 'proj:epsg': 32635, 'description': 'Landsat Collection 2 Level-2 Surface Reflectance Product', 'instruments': ['oli', 'tirs'], 'eo:cloud_cover': 0.01, 'view:off_nadir': 0, 'landsat:wrs_row': '018', 'landsat:scene_id': 'LC81870182018130LGN00', 'landsat:wrs_path': '187', 'landsat:wrs_type': '2', 'view:sun_azimuth': 163.43293558, 'view:sun_elevation': 46.70358845, 'landsat:cloud_cover_land': 0.01, 'landsat:processing_level': 'L2SP', 'landsat:collection_number': '02', 'landsat:collection_category': 'T1'}\n" + ] + } + ], + "source": [ + "if best_l8_match:\n", + " print(best_l8_match.id)\n", + " print(best_l8_match.bbox)\n", + " print(best_l8_match.properties)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "3f3b5d88-7f3c-4e74-8e68-2408ba762b0f", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " minx miny maxx maxy\n", + "0 24.32561 58.956597 28.877931 61.185413\n" + ] + }, + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "explore_search_extent(ItemCollection([best_l8_match]))" + ] + }, + { + "cell_type": "markdown", + "id": "66c13807-50a3-4a8c-8a89-33002eafabe6", + "metadata": {}, + "source": [ + "If everything worked correctly, the geographic scope of the Landsat 8 scene should encompass a much larger surface area than the Sentinel-2 source and label chips. From here we need to crop the image down and make sure the chips from both products match." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "258a1d9e-55aa-4781-a0f5-e77ace273240", + "metadata": {}, + "outputs": [], + "source": [ + "def get_redirect_url(asset: Asset) -> str:\n", + " \"\"\"Returns the direct URL to an asset.\n", + "\n", + " Args:\n", + " asset: Asset object from an Item\n", + "\n", + " Returns:\n", + " string response URL direct to Asset\n", + " \"\"\"\n", + " response = requests.get(asset.href, allow_redirects=True)\n", + " if response.status_code == 200:\n", + " return response.url\n", + " return None" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "1e4e977d-eb14-4aa9-85df-adbe84b1732d", + "metadata": {}, + "outputs": [], + "source": [ + "s2_stack = stack(\n", + " items=ItemCollection([source_item]),\n", + " assets=BIGEARTHNET_RGB_BANDS,\n", + " epsg=rio.open(get_redirect_url(source_item.assets[\"B02\"])).crs.to_epsg(),\n", + " resolution=10,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "287f9b13-82a3-4a5d-82f2-9ab5066c6be1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", 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+       "  * time                         (time) datetime64[ns] 2018-05-10T09:22:33.46...\n",
+       "    id                           (time) <U31 'LC08_L2SP_187018_20180510_02_T1'\n",
+       "  * band                         (band) <U5 'SR_B4' 'SR_B3' 'SR_B2'\n",
+       "  * x                            (x) float64 3.561e+05 3.561e+05 ... 6.012e+05\n",
+       "  * y                            (y) float64 6.785e+06 6.785e+06 ... 6.538e+06\n",
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+       "    description                  (band) <U56 'Collection 2 Level-2 Red Band (...\n",
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+       "    proj:transform               object {0.0, -30.0, 356085.0, 6785115.0, 30.0}\n",
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+       "    title                        (band) <U15 'Red Band (B4)' ... 'Blue Band (...\n",
+       "    common_name                  (band) <U5 'red' 'green' 'blue'\n",
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+       "    landsat:collection_category  <U2 'T1'\n",
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+       "    proj:transform               object {0.0, -30.0, 356085.0, 6785115.0, 30.0}\n",
+       "    proj:shape                   object {8251, 8171}\n",
+       "    title                        (band) <U15 'Red Band (B4)' ... 'Blue Band (...\n",
+       "    common_name                  (band) <U5 'red' 'green' 'blue'\n",
+       "    center_wavelength            (band) float64 0.65 0.56 0.48\n",
+       "    full_width_half_max          (band) float64 0.04 0.06 0.06\n",
+       "    epsg                         int64 32635\n",
+       "Attributes:\n",
+       "    spec:        RasterSpec(epsg=32635, bounds=(403160, 6686780, 404440, 6688...\n",
+       "    crs:         epsg:32635\n",
+       "    transform:   | 10.00, 0.00, 403160.00|\\n| 0.00,-10.00, 6688060.00|\\n| 0.0...\n",
+       "    resolution:  10
" + ], + "text/plain": [ + "\n", + "dask.array\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 2018-05-10T09:22:33.46...\n", + " id (time) " + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Connection method: Cluster objectCluster type: distributed.LocalCluster
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Status: runningUsing processes: True
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" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "client = dask.distributed.Client() # you can configure Dask client parameters here\n", + "client" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "55d9dc11-d3b8-4edc-a5fd-acb2faba1c18", + "metadata": {}, + "outputs": [], + "source": [ + "# client.close()" + ] + }, + { + "cell_type": "markdown", + "id": "f3b09697-b6ab-4026-bfcb-2f5214b03f5c", + "metadata": {}, + "source": [ + "### Scale the workflow using Dask Delayed" + ] + }, + { + "cell_type": "markdown", + "id": "5368c39f-94a1-41ba-acc0-fd18c8dc1c18", + "metadata": {}, + "source": [ + "These are two helper functions that we will use to encapsulate the process of creating the cropped Landsat 8 chips and write them to disk in parallel using the Dask Delayed decorator." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "c924acb1-092e-4f86-b73f-5b56ccdebe27", + "metadata": {}, + "outputs": [], + "source": [ + "def create_landsat_8_dataarray(item_path: str) -> DataArray:\n", + " \"\"\"Creates a Landsat 8 chip from BigEarthNet label chip.\n", + "\n", + " Args:\n", + " item_path: string path to the label item on disk\n", + "\n", + " Returns:\n", + " Landsat 8 DataArray that has been cropped to label bbox\n", + " \"\"\"\n", + " # read label Item object\n", + " label_item = Item.from_file(\n", + " os.path.join(TMP_DIR, BIGEARTHNET_LABEL_COLLECTION, item_path)\n", + " )\n", + "\n", + " # fetch the Landsat 8 scene that best matches the label\n", + " s2_source, l8_match = get_landsat_8_match(label_item)\n", + "\n", + " if l8_match:\n", + " # crop L8 match to S2 dims and read image data\n", + " l8_stack = stack(\n", + " items=ItemCollection([l8_match]),\n", + " assets=LANDSAT_8_RGB_BANDS,\n", + " bounds_latlon=s2_source.bbox,\n", + " resolution=10,\n", + " )\n", + "\n", + " return l8_stack\n", + " return None" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "02b974fe-0c6f-40a3-ad63-2f4753e0236b", + "metadata": {}, + "outputs": [], + "source": [ + "def write_tifs_bands(l8_array: DataArray, l8_item_id: str) -> None:\n", + " \"\"\"Writes to a GeoTiff for each band in Landsat 8 DataArray\n", + "\n", + " Args:\n", + " l8_array: the DataArray object created from the BigEarthNet label item\n", + " \"\"\"\n", + " # write cropped L8 DataArray to a tiff file for each band\n", + " for _band in LANDSAT_8_RGB_BANDS:\n", + " l8_band_img = l8_array.sel(band=_band)\n", + " l8_band_filename = os.path.join(\n", + " TMP_DIR, OUTPUT_DIR, l8_item_id, f\"{l8_item_id}_{_band}.tiff\"\n", + " )\n", + " Path(os.path.split(l8_band_filename)[0]).mkdir(parents=True, exist_ok=True)\n", + " l8_band_img[0].rio.to_raster(l8_band_filename)" + ] + }, + { + "cell_type": "markdown", + "id": "d5a97950-5b08-4e79-ab15-3736697d0584", + "metadata": {}, + "source": [ + "This sets the stage for the Dask Task Scheduler by mapping all label Items to the `create_landsat_8_dataarray` function. Nothing in the task graph will actually be executed until the `.compute()` command is ran." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "6bba4435-3029-4e0f-b1c8-6e37f225184f", + "metadata": {}, + "outputs": [], + "source": [ + "item_bag = dask.bag.from_sequence(label_item_sample).map(create_landsat_8_dataarray)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "4420a2eb-8e3f-479b-a055-b5394b25f837", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dask.bag" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "item_bag" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "604aeb5d-f0d0-4045-963b-0c603e336c24", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "%%time\n", + "computed_result = item_bag.compute()" + ] + }, + { + "cell_type": "markdown", + "id": "6f95d76c-dc3e-4591-a596-a95e27a3dbde", + "metadata": {}, + "source": [ + "Lastly, we want to write a GeoTIFF to disk for each band of each Landsat 8 DataArray we created." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e1436d4d-10ac-4b39-88fb-5150fe9df12b", + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "for l8_array in computed_result:\n", + " if isinstance(l8_array, DataArray):\n", + " write_tifs_bands(l8_array, l8_array.id.values[0])" + ] + }, + { + "cell_type": "markdown", + "id": "2bde6ce7-9d7a-4114-88ba-56e4a4bea247", + "metadata": {}, + "source": [ + "This confirms that folders with images were written to disk. If there is a discrepancy between the sample size and the output, it's likely that there wasn't always a matching Landsat 8 scene given the geometry and datetime parameters for a particular Sentinel-2 source Item." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ec64a009-9a87-4fb2-bf5b-fbc41a3f8021", + "metadata": {}, + "outputs": [], + "source": [ + "landsat_chip_dir = os.path.join(TMP_DIR, OUTPUT_DIR)\n", + "len(os.listdir(landsat_chip_dir))" + ] + }, + { + "cell_type": "markdown", + "id": "e0884796-bfab-4905-aa75-2f8dd43a5a13", + "metadata": {}, + "source": [ + "Open one of the new Landsat 8 chips to inspect what it looks like." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "beb3d5a8-2677-43dc-867a-153eb1e087f9", + "metadata": {}, + "outputs": [], + "source": [ + "landsat_images = glob(f\"{landsat_chip_dir}/**/*.tiff\", recursive=True)\n", + "first_l8_img = rioxarray.open_rasterio(landsat_images[0])\n", + "first_l8_img.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "37581811-0f21-4838-9541-db84688032f6", + "metadata": {}, + "source": [ + "Shutdown the Dask client to cleanup cluster resources." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1d643476-917b-484b-a041-8f3c94d12c06", + "metadata": {}, + "outputs": [], + "source": [ + "client.shutdown()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From fad5a9e7e7fcf8adf3acfc7832d84a0531915907 Mon Sep 17 00:00:00 2001 From: Kendall Smith Date: Mon, 16 May 2022 15:23:00 -0700 Subject: [PATCH 2/2] optimized dask operations, tested on PC --- ...adiant-mlhub-on-demand-training-data.ipynb | 3039 ++--------------- 1 file changed, 227 insertions(+), 2812 deletions(-) diff --git a/tutorials/radiant-mlhub-on-demand-training-data.ipynb b/tutorials/radiant-mlhub-on-demand-training-data.ipynb index 6a826c36..13ab8a02 100644 --- a/tutorials/radiant-mlhub-on-demand-training-data.ipynb +++ b/tutorials/radiant-mlhub-on-demand-training-data.ipynb @@ -31,16 +31,6 @@ { "cell_type": "code", "execution_count": null, - "id": "9fe7b447-cf8a-4cc7-aaed-4e8407d0f270", - "metadata": {}, - "outputs": [], - "source": [ - "!pip install --upgrade wget # not installed on PC by default" - ] - }, - { - "cell_type": "code", - "execution_count": 53, "id": "7e144460-5549-4ab4-ba98-10a1a7ebd236", "metadata": {}, "outputs": [], @@ -52,20 +42,22 @@ "import json\n", "from glob import glob\n", "import requests\n", - "from typing import List, Tuple\n", + "from typing import List, Tuple, Dict, Any\n", "from datetime import datetime as dt\n", "from datetime import timedelta as td\n", "\n", - "from radiant_mlhub import Collection\n", "import planetary_computer\n", "import pystac_client\n", "from pystac import ItemCollection, Item, Asset\n", "import dask\n", "\n", "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", "from stackstac import stack\n", "from geopandas import GeoDataFrame\n", "import rasterio as rio\n", + "from rasterio.plot import show\n", "import rioxarray\n", "from xarray import DataArray\n", "from shapely.geometry import shape\n", @@ -86,12 +78,12 @@ "id": "2de2f657-3229-4037-bf9c-541c503cc269", "metadata": {}, "source": [ - "In addition to the API key, we will also need to define some other initial global variables to get our workflow started. e.g. a temporary working directory to download and write data to, the STAC API endpoints, names of Collections, and other variables like the RGB bands for those collections. These are pretty flexible depending on your individual needs." + "We will also need to define other initial global variables to get our workflow started, e.g. a temporary working directory to download and write data to, the STAC API endpoints, names of Collections, and other variables like the RGB bands for those collections. These are pretty flexible depending on your individual needs." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "f3bf07a1-3a4a-4207-a449-7be766fa7e36", "metadata": {}, "outputs": [], @@ -117,7 +109,8 @@ "BIGEARTHNET_RGB_BANDS = [\"B04\", \"B03\", \"B02\"] # names of RGB bands from PC Landsat 8\n", "\n", "# Bounding box for demonstration fetching Items over Luxembourg\n", - "LUXEMBOURG_AOI = [6.06, 49.58, 6.21, 49.66] # aoi around Luxembourg" + "LUXEMBOURG_AOI = [6.06, 49.58, 6.21, 49.66] # aoi around Luxembourg\n", + "SPAIN_AOI = [-9.73, 35.84, 3.43, 43.87]" ] }, { @@ -136,40 +129,14 @@ "Programmatic access to the Radiant MLHub API using the `pystac_client` library requires both the API end-point and an API key. You can obtain an API key for free by registering an account on [mlhub.earth](https://mlhub.earth/). This can be found under `Settings & API Key` from the drop-down once logged in." ] }, - { - "cell_type": "code", - "execution_count": 5, - "id": "f4c9dd60-3abc-464d-af25-4b23c0d2783b", - "metadata": {}, - "outputs": [ - { - "name": "stdin", - "output_type": "stream", - "text": [ - "MLHub API Key: ································································\n" - ] - } - ], - "source": [ - "MLHUB_API_KEY = getpass.getpass(prompt=\"MLHub API Key: \")" - ] - }, - { - "cell_type": "markdown", - "id": "ccad2439-109f-4eef-ac3b-dac65f54e3aa", - "metadata": {}, - "source": [ - "Once you have your API key, you need to update the default profile file in your home directory. You can use the `mlhub configure` command line tool to do this:" - ] - }, { "cell_type": "code", "execution_count": null, - "id": "cfd2681d-56ed-440c-a352-b4ecfd125c03", + "id": "f4c9dd60-3abc-464d-af25-4b23c0d2783b", "metadata": {}, "outputs": [], "source": [ - "!mlhub configure --api-key={MLHUB_API_KEY}" + "MLHUB_API_KEY = getpass.getpass(prompt=\"MLHub API Key: \")" ] }, { @@ -190,7 +157,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "bf79c301-76df-4158-bf97-3da53552e143", "metadata": {}, "outputs": [], @@ -213,13 +180,13 @@ "id": "e9f19ce4-57fd-43d0-9263-7a5edd106ee8", "metadata": {}, "source": [ - "We will now use the `search` function from the API client to get label Items over Luxembourg as a sample use-case." + "We will now use the `search` function from the API client to get label Items over Luxembourg as a simple use-case." ] }, { "cell_type": "code", - "execution_count": 7, - "id": "fc61a3d4-cfc0-4da5-9717-bf3c8e427100", + "execution_count": null, + "id": "cc6e40fb-9e96-4041-bb21-119539875caa", "metadata": {}, "outputs": [], "source": [ @@ -227,41 +194,21 @@ " collections=BIGEARTHNET_LABEL_COLLECTION,\n", " bbox=LUXEMBOURG_AOI,\n", " datetime=BIGEARTHNET_TIME_RANGE,\n", + " max_items=100\n", ").get_all_items()" ] }, - { - "cell_type": "code", - "execution_count": 8, - "id": "917c392c-9ae5-43e2-b7e0-71dd9f749cc2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "178" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(origin_label_items)" - ] - }, { "cell_type": "markdown", "id": "e9d121a9-e54b-4039-9cef-04277962c2ca", "metadata": {}, "source": [ - "This is another helper function that simply displays the geometry for labels from an ItemCollection overlayed on a map of the region." + "This is a helper function that simply displays the geometry for labels from an ItemCollection overlayed on a map of the region." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "2f203e31-5b09-4f2e-bf27-9a3ef8e3fc4d", "metadata": {}, "outputs": [], @@ -293,46 +240,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "b86bf5d8-8dc8-491d-b3cd-1ea1263774ca", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " minx miny maxx maxy\n", - "0 6.197958 49.579464 6.215240 49.590700\n", - "1 6.198663 49.590240 6.215949 49.601477\n", - "2 6.199368 49.601017 6.216659 49.612254\n", - "3 6.180682 49.569146 6.197958 49.580381\n", - "4 6.181383 49.579923 6.198663 49.591158\n", - ".. ... ... ... ...\n", - "173 6.151709 49.634721 6.169003 49.645951\n", - "174 6.152406 49.645498 6.169703 49.656729\n", - "175 6.153102 49.656275 6.170404 49.667506\n", - "176 6.135808 49.645951 6.153102 49.657180\n", - "177 6.136501 49.656729 6.153800 49.667957\n", - "\n", - "[178 rows x 4 columns]\n" - ] - }, - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "explore_search_extent(origin_label_items)" ] @@ -342,7 +255,7 @@ "id": "13c6e607-c79b-4678-a483-9bacb0b3b1df", "metadata": {}, "source": [ - "### Download the entire label collection for BigEarthNet from Radiant MLHub" + "### Download BigEarthNet Source Items from Radiant MLHub" ] }, { @@ -350,446 +263,124 @@ "id": "2e131160-50bb-487f-8fcb-96d37ce80167", "metadata": {}, "source": [ - "We could certainly use the method above to query label Items directly from our connection to the Radiant MLHub API endpoint. However, on very large collections, such as in the case with BigEarthNet, pagination becomes a bottleneck issue in obtaining and resolving STAC items, as it only returns 100 items at a time. Querying the entire Collection of nearly ~600,000 Items could take hours.\n", + "We could certainly use the method above to query all label and source Items directly from our connection to the Radiant MLHub API endpoint. However, on very large collections, such as in the case with BigEarthNet, pagination becomes a bottleneck issue in obtaining and resolving STAC item. \n", + "\n", + "Querying the entire Collection of nearly ~600,000 Items from a single collection alone would take almost an hour depending on your connection speed. This means it could possibly take a few hours to download all Items in the Catalog. \n", "\n", - "Therefore, downloading the label Collection (which is only 160 MB) directly is preferrable to paginating over the entire Collection using the API." + "One alternative is to download the `.tar.gz` of the collections directly from the Radiant MLHub dataset detail page. The filesize for the labels archive is not large, only 165 MB. However because there are over half a million objects, it takes a long time to decompress the entire download.\n", + "\n", + "Therefore, we can showcase this workflow by paginating over the source Item Collection to fetch the first 5,000 Items available (which only represents 1% of the entire collection)." ] }, { "cell_type": "code", - "execution_count": 11, - "id": "93f67824-bf8f-4cce-862e-856d9cfed26d", + "execution_count": null, + "id": "b257cc4f-d77c-422b-b5ca-e81f768e32d5", "metadata": {}, "outputs": [], "source": [ - "label_collection_path = os.path.join(\n", - " TMP_DIR, BIGEARTHNET_LABEL_COLLECTION, \"collection.json\"\n", + "bigearthnet_source_search = mlhub_catalog.search(\n", + " collections=BIGEARTHNET_SOURCE_COLLECTION,\n", + " bbox=SPAIN_AOI,\n", + " # limit=100, # limit of items per page\n", + " max_items=5000 # total Item recall\n", ")" ] }, { "cell_type": "markdown", - "id": "21bd7e52-6bc7-4a8d-bf56-060e80f4019b", - "metadata": {}, - "source": [ - "Check if collection folder already exists before downloading 173 mb dataset. Otherwise download and uncompress the `.tar.gz` file to extract the label collection files." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "81f5a8c4-c604-4f8a-8fb9-1d90607206ee", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Archive file already downloaded from Radiant MLHub, skipping...\n" - ] - } - ], - "source": [ - "if not os.path.exists(label_collection_path):\n", - " collection = Collection.fetch(BIGEARTHNET_LABEL_COLLECTION)\n", - " archive_path = collection.download(TMP_DIR)\n", - " !tar -xf {archive_path.as_posix()} -C {TMP_DIR}\n", - "else:\n", - " print(\"Archive file already downloaded from Radiant MLHub, skipping...\")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "21e2683b-edde-43e0-8bf2-b791a8e04dc8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['bigearthnet_v1_labels_S2B_MSIL2A_20170914T93030_63_71',\n", - " 'bigearthnet_v1_labels_S2B_MSIL2A_20180506T105029_52_1',\n", - " 'bigearthnet_v1_labels_S2B_MSIL2A_20180509T092029_4_62',\n", - " 'bigearthnet_v1_labels_S2B_MSIL2A_20180525T94030_57_6',\n", - " 'bigearthnet_v1_labels_S2A_MSIL2A_20170717T113321_65_3']" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bigearthnet_dir = os.listdir(os.path.join(TMP_DIR, BIGEARTHNET_LABEL_COLLECTION))\n", - "bigearthnet_dir[0:5]" - ] - }, - { - "cell_type": "markdown", - "id": "e1c28041-79f1-4ebd-a09a-14d440ac2757", - "metadata": {}, - "source": [ - "This is the total count of label Item (chip) directories, plus one for the STAC Collection itself." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "1f270583-b7a5-4e37-8a50-2f9f4e49fdaf", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "590327" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(bigearthnet_dir)" - ] - }, - { - "cell_type": "markdown", - "id": "954d358d-54ff-4300-89e5-43f16e452574", - "metadata": {}, - "source": [ - "### Obtain a random sample of label Items from BigEarthNet" - ] - }, - { - "cell_type": "markdown", - "id": "670ad5d5-458f-4f56-88d9-574e5c37ba26", - "metadata": {}, - "source": [ - "We don't want to work with the entire dataset of nearly 600,000 labels. This would take too long to download, and we likely won't have enough disk space or space in memory, so let's work with a random sample of the dataset that is 10% of the original size." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "ff4ad2d8-7cbd-4bc4-a1d2-f6bcd0c9fcf8", - "metadata": {}, - "outputs": [], - "source": [ - "assert os.path.exists(label_collection_path)\n", - "with open(label_collection_path, \"r\") as in_file:\n", - " collection_data = json.load(in_file)" - ] - }, - { - "cell_type": "markdown", - "id": "8c8b0c28-9dee-4f83-b3fe-a49079a530dd", + "id": "f729e7ea-689f-40ee-9048-4c37f3f2e859", "metadata": {}, "source": [ - "This confirms we have all of the label Items STAC objects and image data from the collection" + "It should take less than a minute to fetch all the STAC Items for the 5000 sample we've queried." ] }, { "cell_type": "code", - "execution_count": 16, - "id": "9ffac3d1-4cf1-4bf8-b066-6de4fa1f4da1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "590326" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "label_item_links = [\n", - " link[\"href\"] for link in collection_data[\"links\"] if link[\"rel\"] == \"item\"\n", - "]\n", - "len(label_item_links)" - ] - }, - { - "cell_type": "markdown", - "id": "b9836f40-4228-4d72-8b84-d94cae1030c6", - "metadata": {}, - "source": [ - "Now we take a random sample that is 1/100th the original dataset size" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "97b8f303-08c8-4353-847e-fc4d712edac6", - "metadata": {}, - "outputs": [], - "source": [ - "label_item_sample = np.random.choice(\n", - " a=label_item_links, size=int(len(label_item_links) / 100), replace=False\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "a05b5914-ddf8-4332-87f9-4e631e71110a", + "execution_count": null, + "id": "6d026603-2fc8-434b-9701-72aab86a6cd6", "metadata": {}, "outputs": [], "source": [ - "first_label_item = Item.from_file(\n", - " os.path.join(\n", - " TMP_DIR,\n", - " BIGEARTHNET_LABEL_COLLECTION,\n", - " label_item_sample[np.random.randint(len(label_item_sample))],\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "65959f74-4d1d-4ce4-8583-bc51d6047ce1", - "metadata": {}, - "source": [ - "Chip ID for the sample label Item pulled:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "4420d40b-2cd3-4f74-b271-a137ca71e360", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'bigearthnet_v1_labels_S2A_MSIL2A_20180413T95032_86_10'" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "first_label_item.id" - ] - }, - { - "cell_type": "markdown", - "id": "c0b0734b-ce42-48de-8bea-f4c4f5e9b37f", - "metadata": {}, - "source": [ - "Links for the sample label Item, take special note of the `rel=source` Link listed:" + "%%time\n", + "bigearthnet_source_items = bigearthnet_source_search.get_all_items()" ] }, { "cell_type": "code", - "execution_count": 20, - "id": "42d94501-6305-4329-aa4f-ef69802951b8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[,\n", - " ,\n", - " ,\n", - " ]" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "first_label_item.links" - ] - }, - { - "cell_type": "markdown", - "id": "5d23fb83-400e-487b-940f-fc158b5ae41b", + "execution_count": null, + "id": "01d88ffe-f530-46d6-87d9-4337c1ec0202", "metadata": { "tags": [] }, + "outputs": [], "source": [ - "### Fetch source items for random sample from BigEarthNet" + "explore_search_extent(bigearthnet_source_items)" ] }, { "cell_type": "markdown", - "id": "e419d96f-f8b9-4911-b5d1-4a4077767062", - "metadata": {}, - "source": [ - "If we had the source collection archive downloaded and uncompressed in the same parent directory as the labels collection, we could reference the source Items and images directly. However the BigEarthNet source collection is over 60GB when compressed. Therefore to work around the disk size limitations of a Planetary Computer instance, we can query the same source items from the MLHub API endpoint, the same way we got the labels, but filter to the exact source item using IDs." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "fa334c57-9dbe-495e-b040-980adf1192c4", + "id": "77109ba6-f0a1-426e-8884-39a5fac2795f", "metadata": {}, - "outputs": [], "source": [ - "def get_source_item_ids(label_item: Item) -> List[str]:\n", - " return [\n", - " link.href.split(\"/\")[-2] for link in label_item.links if link.rel == \"source\"\n", - " ]" + "We can see from this map that the location of the source items fetched are concentrated in Portugal. This is merely a consequence of the fact we fetched the first 5,000 source Items out of the Catalog API with a bounding box search criteria over Spain. Had we downloaded the entire Catalog locally, or ran an unfiltered search, we could fetch a random sample that is more representative of the entire dataset." ] }, { - "cell_type": "code", - "execution_count": 22, - "id": "f4fa6f4b-8463-40a4-8aec-527d868be5e2", + "cell_type": "markdown", + "id": "3f09ed9c-66c5-4f4e-8230-1c62e71e2db3", "metadata": {}, - "outputs": [], "source": [ - "origin_source_items = mlhub_catalog.search(\n", - " collections=[BIGEARTHNET_SOURCE_COLLECTION],\n", - " ids=get_source_item_ids(first_label_item),\n", - ").get_all_items()" + "### Configure API connection to Planetary Computer" ] }, { "cell_type": "markdown", - "id": "03e775fa-dd60-4623-8d44-3d83beafcb2c", + "id": "9a320f3a-f6dd-487d-8cef-34b47f327f1d", "metadata": {}, "source": [ - "This is the number of source items that match the query parameters we sent to the MLHub API using the first label's bounding box and datetime properties." + "This makes a connection to the Planetary Computer Data Catalog using the API endpoint URL." ] }, { "cell_type": "code", - "execution_count": 23, - "id": "2c98a5d6-1a55-466a-b768-4433cea148ca", + "execution_count": null, + "id": "38280716-e130-4de4-9699-2c0bcbfc056d", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "len(origin_source_items)" + "mspc_catalog = pystac_client.Client.open(MSPC_API_URL)" ] }, { "cell_type": "markdown", - "id": "c0ba4eaf-03d8-4e56-92c8-4e98b26ba983", - "metadata": {}, - "source": [ - "Taking a look at some of the properties of the first source Item found:" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "854abe53-e1b7-452a-8aa2-c139aa220348", + "id": "a5e0cbba-32b8-47ad-9913-e7ba2a939922", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "bigearthnet_v1_source_S2A_MSIL2A_20180413T95032_86_10\n", - "2018-04-13 09:50:32+00:00\n", - "[25.247600449332744, 60.30639355471977, 25.269882490837322, 60.317447602103464]\n", - "{'gsd': 30, 'datetime': '2018-04-13T09:50:32Z', 'eo:bands': [{'name': 'B01', 'common_name': 'Coastal Aerosol', 'description': 'Coastal Aerosol'}, {'name': 'B02', 'common_name': 'Blue', 'description': 'Blue'}, {'name': 'B03', 'common_name': 'Green', 'description': 'Green'}, {'name': 'B04', 'common_name': 'Red', 'description': 'Red'}, {'name': 'B05', 'common_name': 'Vegetation Red Edge', 'description': 'Vegetation Red Edge (704.1nm)'}, {'name': 'B06', 'common_name': 'Vegetation Red Edge', 'description': 'Vegetation Red Edge (740.1nm)'}, {'name': 'B07', 'common_name': 'Vegetation Red Edge', 'description': 'Vegetation Red Edge (782.8nm)'}, {'name': 'B08', 'common_name': 'NIR', 'description': 'NIR'}, {'name': 'B8A', 'common_name': 'Narrow NIR', 'description': 'Narrow NIR'}, {'name': 'B09', 'common_name': 'Water Vapour', 'description': 'Water Vapour'}, {'name': 'B11', 'common_name': 'SWIR', 'description': 'SWIR (1613.7nm)'}, {'name': 'B12', 'common_name': 'SWIR', 'description': 'SWIR (2202.4nm)'}], 'platform': 'Sentinel-2', 'instruments': ['MSI'], 'constellation': 'Sentinel-2'}\n" - ] - } - ], - "source": [ - "for source_item in origin_source_items:\n", - " print(source_item.id)\n", - " print(source_item.datetime)\n", - " print(source_item.bbox)\n", - " print(source_item.properties)\n", - " break" - ] - }, - { - "cell_type": "markdown", - "id": "5303475f-d29b-4e84-82ef-d355ef0519de", - "metadata": {}, "source": [ - "With the properties from this sample source Item, we can observe where the chip is located, the relevant Sentinel-2 bands (assets) and datetime the image was captured." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "01d88ffe-f530-46d6-87d9-4337c1ec0202", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " minx miny maxx maxy\n", - "0 25.2476 60.306394 25.269882 60.317448\n" - ] - }, - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "explore_search_extent(origin_source_items)" + "### Fetch Landsat 8 scenes based on source Item bbox and datetime" ] }, { "cell_type": "markdown", - "id": "77109ba6-f0a1-426e-8884-39a5fac2795f", + "id": "69ad7ef1-143e-47f0-b6b9-26c73e5cc65d", "metadata": {}, "source": [ - "This is the location of the source items fetched from the label Items sample." - ] - }, - { - "cell_type": "markdown", - "id": "a5e0cbba-32b8-47ad-9913-e7ba2a939922", - "metadata": { - "tags": [] - }, - "source": [ - "### Fetch Landsat 8 scenes based on source Item bbox and datetime" + "Since it is known that the BigEarthNet dataset from MLHub has a 1-to-1 pairing of source and labels, we can safely assume the first source item is the appropriate match for our label." ] }, { "cell_type": "markdown", - "id": "4392b23e-eebb-4a53-88d8-f49fbfacfaf1", + "id": "39f35d02-d2de-4be5-9317-c80214502c88", "metadata": {}, "source": [ - "Configure API connection for the microsoft planetary computer stac endpoint" + "We will now use the API client with the helper function above to fetch the best Landsat 8 match for the sampled label Item. This will find only the scenes where the label is completely within the scene, and there is minimal cloud cover." ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "1eddda32-d50d-413d-add8-dedaa2f9a067", "metadata": {}, "outputs": [], @@ -818,7 +409,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "id": "f77e4ec0-a1b8-490c-b732-4c10d89b06ea", "metadata": {}, "outputs": [], @@ -846,163 +437,87 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "c3b23fa5-d588-42d4-b913-fab7819e7ea3", "metadata": {}, "outputs": [], "source": [ - "def get_landsat_8_match(label_item: Item) -> Tuple[Item, Item]:\n", + "def get_landsat_8_match(bbox: List[float], geometry: Dict[str, Any], datetime: str) -> Item:\n", " \"\"\"Finds the best Landsat 8 match using source Item datetime and bounding box.\n", "\n", " Args:\n", - " label_item: the STAC label Item object\n", + " bbox: bounding box of the STAC source Item\n", + " datetime: datetime of the STAC source Item\n", "\n", " Returns:\n", - " Tuple of the BigEarthNet source Item and the Landsat 8 match Item\n", + " best_l8_match: matching Landsat 8 source Item\n", " \"\"\"\n", - " # get the matching source Item properties\n", - " source_items = mlhub_catalog.search(\n", - " collections=[BIGEARTHNET_SOURCE_COLLECTION],\n", - " ids=get_source_item_ids(label_item),\n", - " ).get_all_items()\n", - "\n", - " if source_items:\n", - " source_item = source_items[0]\n", - " source_bbox = source_item.bbox\n", - " source_datetime = source_item.properties[\"datetime\"]\n", "\n", - " # search PC Catalog for L8 Items\n", - " l8_items = mspc_catalog.search(\n", - " collections=PLANETARY_COMPUTER_LANDSAT_8,\n", - " bbox=source_bbox,\n", - " datetime=temporal_buffer(source_datetime, DATE_BUFFER),\n", - " ).get_all_items()\n", + " # search PC Catalog for L8 Items\n", + " l8_items = mspc_catalog.search(\n", + " collections=PLANETARY_COMPUTER_LANDSAT_8,\n", + " bbox=bbox,\n", + " datetime=temporal_buffer(datetime, DATE_BUFFER),\n", + " ).get_all_items()\n", "\n", - " # filter to best L8 Item match\n", - " signed_l8_items = planetary_computer.sign(l8_items)\n", - " best_l8_match = min_cloud_cover_scene(\n", - " shape(source_item.geometry), signed_l8_items\n", - " )\n", + " # filter to best L8 Item match\n", + " signed_l8_items = planetary_computer.sign(l8_items)\n", + " best_l8_match = min_cloud_cover_scene(\n", + " shape(geometry), \n", + " signed_l8_items\n", + " )\n", "\n", - " if not best_l8_match:\n", - " print(\n", - " \"No Landsat 8 Item was found on the Planetary \"\n", - " \"Computer matching the query parameters:\"\n", - " )\n", - " print(\n", - " f\"Source Item ID: {source_item.id} \"\n", - " f\"Bbox: {source_bbox}, \"\n", - " f\"Datetime: {source_datetime}\"\n", - " )\n", - " best_l8_match = None\n", - " else:\n", - " print(\n", - " \"No Sentinel-2 source Item was found in the \"\n", - " \"BigEarthNet dataset matching that label item!\"\n", - " )\n", - " source_item = None\n", - " return source_item, best_l8_match" - ] - }, - { - "cell_type": "markdown", - "id": "69ad7ef1-143e-47f0-b6b9-26c73e5cc65d", - "metadata": {}, - "source": [ - "Since it is known that the BigEarthNet dataset from MLHub has a 1-to-1 pairing of source and labels, we can safely assume the first source item is the appropriate match for our label." - ] - }, - { - "cell_type": "markdown", - "id": "ab77ef46-b54a-42e7-a780-bc8e8094ec6f", - "metadata": {}, - "source": [ - "This makes a connection to the Planetary Computer Data Catalog using the API endpoint URL." + " return best_l8_match" ] }, { "cell_type": "code", - "execution_count": 29, - "id": "38280716-e130-4de4-9699-2c0bcbfc056d", + "execution_count": null, + "id": "784d731a-4e04-41ba-99e1-476d828aa65e", "metadata": {}, "outputs": [], "source": [ - "mspc_catalog = pystac_client.Client.open(MSPC_API_URL)" - ] - }, - { - "cell_type": "markdown", - "id": "39f35d02-d2de-4be5-9317-c80214502c88", - "metadata": {}, - "source": [ - "We will now use the API client with the helper function above to fetch the best Landsat 8 match for the sampled label Item. This will find only the scenes where the label is completely within the scene, and there is minimal cloud cover." + "sample_source_item = bigearthnet_source_items[np.random.randint(0, len(bigearthnet_source_items))]" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "53d3ab14-86c7-42a8-be96-8156f5e74d64", "metadata": {}, "outputs": [], "source": [ - "source_item, best_l8_match = get_landsat_8_match(first_label_item)" + "best_l8_match = get_landsat_8_match(\n", + " sample_source_item.bbox,\n", + " sample_source_item.geometry,\n", + " sample_source_item.properties['datetime']\n", + ")" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "id": "53556cf0-375b-4044-9262-d16de707a13d", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "LC08_L2SP_187018_20180510_02_T1\n", - "[24.323999281168234, 58.95366546927769, 28.882648861314085, 61.187374530722316]\n", - "{'datetime': '2018-05-10T09:22:33.464049Z', 'platform': 'landsat-8', 'proj:bbox': [356085.0, 6537585.0, 601215.0, 6785115.0], 'proj:epsg': 32635, 'description': 'Landsat Collection 2 Level-2 Surface Reflectance Product', 'instruments': ['oli', 'tirs'], 'eo:cloud_cover': 0.01, 'view:off_nadir': 0, 'landsat:wrs_row': '018', 'landsat:scene_id': 'LC81870182018130LGN00', 'landsat:wrs_path': '187', 'landsat:wrs_type': '2', 'view:sun_azimuth': 163.43293558, 'view:sun_elevation': 46.70358845, 'landsat:cloud_cover_land': 0.01, 'landsat:processing_level': 'L2SP', 'landsat:collection_number': '02', 'landsat:collection_category': 'T1'}\n" - ] - } - ], + "outputs": [], "source": [ "if best_l8_match:\n", " print(best_l8_match.id)\n", " print(best_l8_match.bbox)\n", + " print(best_l8_match.geometry)\n", " print(best_l8_match.properties)" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "id": "3f3b5d88-7f3c-4e74-8e68-2408ba762b0f", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " minx miny maxx maxy\n", - "0 24.32561 58.956597 28.877931 61.185413\n" - ] - }, - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "explore_search_extent(ItemCollection([best_l8_match]))" ] @@ -1017,7 +532,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "id": "258a1d9e-55aa-4781-a0f5-e77ace273240", "metadata": {}, "outputs": [], @@ -1039,610 +554,89 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, + "id": "40a5cf10-0c5b-4e42-856a-51c0987f5523", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_rgb_chip(rgb_stack: DataArray, norm: int) -> None:\n", + " img_arr = rgb_stack[0].to_numpy().squeeze()\n", + " fig, ax = plt.subplots(figsize=(7,7))\n", + " show(img_arr/norm, ax=ax)" + ] + }, + { + "cell_type": "code", + "execution_count": null, "id": "1e4e977d-eb14-4aa9-85df-adbe84b1732d", "metadata": {}, "outputs": [], "source": [ "s2_stack = stack(\n", - " items=ItemCollection([source_item]),\n", + " items=ItemCollection([sample_source_item]),\n", " assets=BIGEARTHNET_RGB_BANDS,\n", - " epsg=rio.open(get_redirect_url(source_item.assets[\"B02\"])).crs.to_epsg(),\n", + " epsg=rio.open(get_redirect_url(sample_source_item.assets[\"B02\"])).crs.to_epsg(),\n", " resolution=10,\n", ")" ] }, + { + "cell_type": "markdown", + "id": "fc36fee6-59ea-4554-82e0-20291d6d3004", + "metadata": {}, + "source": [ + "The `stackstac.stack` method returns a DataArray object with width and height for longitude and latitude, and a third dimension for the RGB bands." + ] + }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "id": "287f9b13-82a3-4a5d-82f2-9ab5066c6be1", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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-       "    title                        (band) <U15 'Red Band (B4)' ... 'Blue Band (...\n",
-       "    common_name                  (band) <U5 'red' 'green' 'blue'\n",
-       "    center_wavelength            (band) float64 0.65 0.56 0.48\n",
-       "    full_width_half_max          (band) float64 0.04 0.06 0.06\n",
-       "    epsg                         int64 32635\n",
-       "Attributes:\n",
-       "    spec:        RasterSpec(epsg=32635, bounds=(403160, 6686780, 404440, 6688...\n",
-       "    crs:         epsg:32635\n",
-       "    transform:   | 10.00, 0.00, 403160.00|\\n| 0.00,-10.00, 6688060.00|\\n| 0.0...\n",
-       "    resolution:  10
" - ], - "text/plain": [ - "\n", - "dask.array\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 2018-05-10T09:22:33.46...\n", - " id (time) " - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "client = dask.distributed.Client() # you can configure Dask client parameters here\n", - "client" + "Now we have a cropped Landsat 8 chip that spatially and temporally matches our Sentinel-2 source imagery and label sample from the BigEarthNet dataset. The first observation is that the Landsat 8 image appears blurry compared to Sentinel-2. This is because Sentinel-2 RGB bands have a 10m resolution, while the same bands for Landsat 8 have a 30m resolution." ] }, { - "cell_type": "code", - "execution_count": 2, - "id": "55d9dc11-d3b8-4edc-a5fd-acb2faba1c18", + "cell_type": "markdown", + "id": "f3b09697-b6ab-4026-bfcb-2f5214b03f5c", "metadata": {}, - "outputs": [], "source": [ - "# client.close()" + "### Scale the workflow using Dask Delayed" ] }, { "cell_type": "markdown", - "id": "f3b09697-b6ab-4026-bfcb-2f5214b03f5c", + "id": "cb650d80-bf1a-4aad-8f8b-08a612e28aae", "metadata": {}, "source": [ - "### Scale the workflow using Dask Delayed" + "We will now use Dask to optimize processing the Landsat-8 scenes by parallelizing the workflow with a delayed computation graph. The Dask Client schedules, runs the delayed computations, and gathers the results. With parallel processing, we can speed up the runtime of our image processing workflow by 10-20x." ] }, { @@ -3314,54 +740,52 @@ "id": "5368c39f-94a1-41ba-acc0-fd18c8dc1c18", "metadata": {}, "source": [ - "These are two helper functions that we will use to encapsulate the process of creating the cropped Landsat 8 chips and write them to disk in parallel using the Dask Delayed decorator." + "These are some helper functions that we will use to encapsulate the process of creating the cropped Landsat 8 chips and write them to disk in parallel using the Dask Delayed decorator." ] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "id": "c924acb1-092e-4f86-b73f-5b56ccdebe27", "metadata": {}, "outputs": [], "source": [ - "def create_landsat_8_dataarray(item_path: str) -> DataArray:\n", + "def create_landsat_8_chip(source_item: Dict[str, any]) -> DataArray:\n", " \"\"\"Creates a Landsat 8 chip from BigEarthNet label chip.\n", "\n", " Args:\n", - " item_path: string path to the label item on disk\n", + " source_item: JSON/dictionary representation of source Item\n", "\n", " Returns:\n", - " Landsat 8 DataArray that has been cropped to label bbox\n", + " Landsat 8 DataArray that has been cropped to sentinel-2 bbox\n", " \"\"\"\n", - " # read label Item object\n", - " label_item = Item.from_file(\n", - " os.path.join(TMP_DIR, BIGEARTHNET_LABEL_COLLECTION, item_path)\n", - " )\n", "\n", " # fetch the Landsat 8 scene that best matches the label\n", - " s2_source, l8_match = get_landsat_8_match(label_item)\n", + " l8_match = get_landsat_8_match(\n", + " source_item['bbox'],\n", + " source_item['geometry'],\n", + " source_item['properties']['datetime']\n", + " )\n", "\n", - " if l8_match:\n", - " # crop L8 match to S2 dims and read image data\n", - " l8_stack = stack(\n", - " items=ItemCollection([l8_match]),\n", - " assets=LANDSAT_8_RGB_BANDS,\n", - " bounds_latlon=s2_source.bbox,\n", - " resolution=10,\n", - " )\n", + " # crop L8 match to S2 dims and read image data\n", + " l8_stack = stack(\n", + " items=ItemCollection([l8_match]),\n", + " assets=LANDSAT_8_RGB_BANDS,\n", + " bounds_latlon=source_item['bbox'],\n", + " resolution=10,\n", + " )\n", "\n", - " return l8_stack\n", - " return None" + " return l8_stack" ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "id": "02b974fe-0c6f-40a3-ad63-2f4753e0236b", "metadata": {}, "outputs": [], "source": [ - "def write_tifs_bands(l8_array: DataArray, l8_item_id: str) -> None:\n", + "def write_tif_bands(l8_array: DataArray, l8_item_id: str) -> None:\n", " \"\"\"Writes to a GeoTiff for each band in Landsat 8 DataArray\n", "\n", " Args:\n", @@ -3387,67 +811,56 @@ }, { "cell_type": "code", - "execution_count": 58, - "id": "6bba4435-3029-4e0f-b1c8-6e37f225184f", + "execution_count": null, + "id": "29531759-6d19-4010-8401-eb947a32c515", "metadata": {}, "outputs": [], "source": [ - "item_bag = dask.bag.from_sequence(label_item_sample).map(create_landsat_8_dataarray)" + "client = dask.distributed.Client() # you can configure Dask client parameters here\n", + "client" ] }, { - "cell_type": "code", - "execution_count": 59, - "id": "4420a2eb-8e3f-479b-a055-b5394b25f837", + "cell_type": "markdown", + "id": "6a30a2f9-a176-436b-a132-01903ab72fd3", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dask.bag" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "item_bag" + "One quirky nature of combining DataArray objects returned from `stackstac.stack()` (leveraging the `rioxarray` library under the hood) is that the kernel will throw an error that the DataArrays don't have the method `rio.to_raster()`. Normally we could solve this problem by explicitly importing the `rioxarray` library, but we also need to import the module onto each worker in the client cluster. " ] }, { "cell_type": "code", - "execution_count": 60, - "id": "604aeb5d-f0d0-4045-963b-0c603e336c24", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "%%time\n", - "computed_result = item_bag.compute()" - ] - }, - { - "cell_type": "markdown", - "id": "6f95d76c-dc3e-4591-a596-a95e27a3dbde", + "execution_count": null, + "id": "16e0b91b-0d73-4335-8ba9-00f75850d409", "metadata": {}, + "outputs": [], "source": [ - "Lastly, we want to write a GeoTIFF to disk for each band of each Landsat 8 DataArray we created." + "import importlib\n", + "client.run(lambda: importlib.import_module(\"rioxarray\"))" ] }, { "cell_type": "code", "execution_count": null, - "id": "e1436d4d-10ac-4b39-88fb-5150fe9df12b", - "metadata": {}, + "id": "f3c67a19-442d-484c-8643-93c4d00c60a0", + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "%%time\n", - "for l8_array in computed_result:\n", - " if isinstance(l8_array, DataArray):\n", - " write_tifs_bands(l8_array, l8_array.id.values[0])" + "chunk_size = 125\n", + "\n", + "for i in range(0, len(bigearthnet_source_items[0:500]), chunk_size):\n", + " future_pool = []\n", + " item_chunk=bigearthnet_source_items[i:i+chunk_size]\n", + " for source_item in item_chunk:\n", + " item_dict = dask.delayed(Item.to_dict)(source_item)\n", + " l8_xarray = dask.delayed(create_landsat_8_chip)(item_dict)\n", + " image_writer = dask.delayed(write_tif_bands)(l8_xarray, item_dict['id'])\n", + " future_pool.append(image_writer)\n", + " future_pool = dask.persist(*future_pool)\n", + " dask.compute(*future_pool)" ] }, { @@ -3455,7 +868,7 @@ "id": "2bde6ce7-9d7a-4114-88ba-56e4a4bea247", "metadata": {}, "source": [ - "This confirms that folders with images were written to disk. If there is a discrepancy between the sample size and the output, it's likely that there wasn't always a matching Landsat 8 scene given the geometry and datetime parameters for a particular Sentinel-2 source Item." + "Now that our parallelized workflow has completed, let's confirm that folders with images were written to disk." ] }, { @@ -3474,7 +887,7 @@ "id": "e0884796-bfab-4905-aa75-2f8dd43a5a13", "metadata": {}, "source": [ - "Open one of the new Landsat 8 chips to inspect what it looks like." + "We can also open one of the new Landsat 8 chips to inspect what it looks like." ] }, { @@ -3494,14 +907,16 @@ "id": "37581811-0f21-4838-9541-db84688032f6", "metadata": {}, "source": [ - "Shutdown the Dask client to cleanup cluster resources." + "Lastly, we will shutdown the Dask client to cleanup cluster resources." ] }, { "cell_type": "code", "execution_count": null, "id": "1d643476-917b-484b-a041-8f3c94d12c06", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "client.shutdown()"