From 176be369497f3020baa2d494e6d19ec3728a9d25 Mon Sep 17 00:00:00 2001 From: bramjanssen Date: Fri, 19 Dec 2025 14:52:07 +0100 Subject: [PATCH 1/3] worldcover-stats: added notebook example --- .../notebooks/worldcover_statistics.ipynb | 253 ++++++++++++++++++ .../records/worldcover_statistics.json | 6 + 2 files changed, 259 insertions(+) create mode 100644 algorithm_catalog/vito/worldcover_statistics/notebooks/worldcover_statistics.ipynb diff --git a/algorithm_catalog/vito/worldcover_statistics/notebooks/worldcover_statistics.ipynb b/algorithm_catalog/vito/worldcover_statistics/notebooks/worldcover_statistics.ipynb new file mode 100644 index 00000000..7eeef45f --- /dev/null +++ b/algorithm_catalog/vito/worldcover_statistics/notebooks/worldcover_statistics.ipynb @@ -0,0 +1,253 @@ +{ + "metadata": { + "kernelspec": { + "name": "python", + "display_name": "Python (Pyodide)", + "language": "python" + }, + "language_info": { + "codemirror_mode": { + "name": "python", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8" + } + }, + "nbformat_minor": 5, + "nbformat": 4, + "cells": [ + { + "id": "1c2d8d0e-dc8c-45dd-9b05-0e42f0dd083a", + "cell_type": "markdown", + "source": "# WorldCover Statistics – Example\n\nThis notebook demonstrates how to use the **WorldCover Statistics** service, which is hosted on the CDSE openEO backend and published in the [APEx Algorithm Catalogue](https://algorithm-catalogue.apex.esa.int/). \nIt provides a step-by-step walkthrough showing how to execute the service and retrieve land cover statistics for your area of interest.\n", + "metadata": {} + }, + { + "id": "9dc57d31-81a3-4177-bbc7-6da52a7ef91c", + "cell_type": "code", + "source": "%pip install esa-apex-algorithms openeo pandas matplotlib folium shapely geojson", + "metadata": { + "trusted": true + }, + "outputs": [], + "execution_count": 21 + }, + { + "id": "af828883-bbfd-442b-a273-00702bdc07c6", + "cell_type": "code", + "source": "import esa_apex_toolbox\nimport openeo\nimport pandas as pd\nimport folium\nfrom shapely.geometry import shape\nimport matplotlib.pyplot as plt", + "metadata": { + "trusted": true + }, + "outputs": [], + "execution_count": 38 + }, + { + "id": "23539872-1969-40d3-b0fd-6c3260cd640c", + "cell_type": "code", + "source": "from esa_apex_toolbox.algorithms import GithubAlgorithmRepository", + "metadata": { + "trusted": true + }, + "outputs": [], + "execution_count": 3 + }, + { + "id": "5d094253-7b45-4af6-9176-73ba131d0c17", + "cell_type": "markdown", + "source": "## Discover the WorldCover Statistics Algorithm\n\nWe use the Python API of the APEx algorithm catalogue to easily explore the available algorithms and their metadata. \nThis approach removes the need to manually copy and paste algorithm links, making the workflow more robust and reproducible.", + "metadata": {} + }, + { + "id": "bc95f3eb-687a-453b-aadf-5de652ae2133", + "cell_type": "code", + "source": "repo = GithubAlgorithmRepository(\n owner=\"ESA-APEx\",\n repo=\"apex_algorithms\",\n folder=\"algorithm_catalog\",\n )", + "metadata": { + "trusted": true + }, + "outputs": [], + "execution_count": 10 + }, + { + "id": "8fe91e33-2437-4edb-85ac-7d15ed0b3b74", + "cell_type": "code", + "source": "repo.list_algorithms()", + "metadata": { + "trusted": true + }, + "outputs": [ + { + "execution_count": 11, + "output_type": "execute_result", + "data": { + "text/plain": "['RAMONA-herbaceous_rangeland_biomass-country-mosaick',\n 'wind_turbine',\n 'sen2like',\n 'eurac_pv_farm_detection',\n 'gep_bas',\n 'gep_ost',\n 'sar_coin',\n 'snap_insar_sentinel1_iw_slc',\n 'bap_composite',\n 'biopar',\n 'max_ndvi',\n 'max_ndvi_composite',\n 'mogpr_s1s2',\n 'parcel_delineation',\n 'peakvalley',\n 'phenology',\n 'random_forest_firemapping',\n 'sentinel1_stats',\n 'variabilitymap',\n 'whittaker',\n 'worldcereal_crop_extent',\n 'worldcereal_crop_type',\n 'worldcover_statistics',\n 'worldagrocommodities']" + }, + "metadata": {} + } + ], + "execution_count": 11 + }, + { + "id": "03fb8676-5ae4-4695-a05e-c57d2368d35d", + "cell_type": "code", + "source": "worldcover_stats = repo.get_algorithm('worldcover_statistics')\nworldcover_stats", + "metadata": { + "trusted": true + }, + "outputs": [ + { + "execution_count": 12, + "output_type": "execute_result", + "data": { + "text/plain": "Algorithm(id='worldcover_statistics', title='Land cover statistics based on ESA WorldCover data for 2021, provided by Terrascope.', description='For a given geometry or set of geometries, computes percentage landcover for 2021.', udp_link=UdpLink(href='https://raw.githubusercontent.com/ESA-APEx/apex_algorithms/refs/heads/main/algorithm_catalog/vito/worldcover_statistics/openeo_udp/worldcover_statistics.json', title='openEO Process Definition'), service_links=[ServiceLink(href='https://openeofed.dataspace.copernicus.eu', title='openEO platform')], license=None, organization='Terrascope')" + }, + "metadata": {} + } + ], + "execution_count": 12 + }, + { + "id": "edc0de06-9d3f-41ad-be47-611bcce4ddaf", + "cell_type": "markdown", + "source": "## Run the Algorithm in openEO\n\nTo execute the algorithm in openEO, first connect to the backend and authenticate using your user account. \nOnce authenticated, you can submit processing requests and run the service.", + "metadata": {} + }, + { + "id": "c53d3be3-11eb-4548-9a2b-3af7a6d5a27d", + "cell_type": "code", + "source": "connection = openeo.connect(worldcover_stats.service_links[0].href).authenticate_oidc()", + "metadata": { + "trusted": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": "Visit https://identity.dataspace.copernicus.eu/auth/realms/CDSE/device?user_code=ZWBP-HZZC to authenticate.\nAuthorized successfully \nAuthenticated using device code flow.\n" + } + ], + "execution_count": 14 + }, + { + "id": "6cb8baa1-12f2-43d1-b7a7-4490c831318a", + "cell_type": "markdown", + "source": "### Define the Area of Interest\n\nBefore running the land cover calculation, we first need to define the area of interest. \nThis spatial extent determines where the statistics will be computed.", + "metadata": {} + }, + { + "id": "cdcf6122-63c7-4b05-adfc-facfb4349881", + "cell_type": "code", + "source": "aoi = {\n \"coordinates\": [\n [\n [\n 6.328885950057327,\n 46.89383161990338\n ],\n [\n 6.328885950057327,\n 46.16912106552846\n ],\n [\n 7.549722603596138,\n 46.16912106552846\n ],\n [\n 7.549722603596138,\n 46.89383161990338\n ],\n [\n 6.328885950057327,\n 46.89383161990338\n ]\n ]\n ],\n \"type\": \"Polygon\"\n}", + "metadata": { + "trusted": true + }, + "outputs": [], + "execution_count": 15 + }, + { + "id": "d4405112-127f-40fd-8c11-47640b1f6404", + "cell_type": "code", + "source": "geometry = shape(aoi)\ncentroid = geometry.centroid\nm = folium.Map(\n location=[centroid.y, centroid.x],\n zoom_start=8\n)\nfolium.WmsTileLayer(\n url=\"https://services.terrascope.be/wms/v2\",\n layers=\"WORLDCOVER_2021_MAP\",\n name=\"WorldCover 2021\",\n srs=\"EPSG:3857\",\n fmt=\"image/png\",\n transparent=True,\n version=\"1.3.0\",\n attr=\"© Terrascope\"\n).add_to(m)\n\nfolium.GeoJson(\n aoi,\n name=\"Area of Interest\"\n).add_to(m)\nm", + "metadata": { + "trusted": true + }, + "outputs": [ + { + "execution_count": 33, + "output_type": "execute_result", + "data": { + "text/plain": "", + "text/html": "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + }, + "metadata": {} + } + ], + "execution_count": 33 + }, + { + "id": "b75e2310-1fd4-4ffd-805f-80a2b34cf9d7", + "cell_type": "markdown", + "source": "### Execute the Service through openEO\n\nNow that everything is set up, we can execute the service through openEO and visualise the results. \nThe following steps submit the processing request and retrieve the output for inspection.", + "metadata": {} + }, + { + "id": "35ea9ce7-869a-4fd8-9e57-84819ce5d7ac", + "cell_type": "code", + "source": "connection.datacube_from_process(\n namespace = worldcover_stats.udp_link.href,\n process_id = worldcover_stats.id,\n geometries = aoi\n).save_result(format=\"CSV\").execute_batch(\"worldcover_stats.csv\")", + "metadata": { + "trusted": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": "0:00:00 Job 'cdse-j-25121913270447a19fd0b6af4a50abf3': send 'start'\n0:00:15 Job 'cdse-j-25121913270447a19fd0b6af4a50abf3': queued (progress 0%)\n0:00:21 Job 'cdse-j-25121913270447a19fd0b6af4a50abf3': queued (progress 0%)\n0:00:27 Job 'cdse-j-25121913270447a19fd0b6af4a50abf3': queued (progress 0%)\n0:00:36 Job 'cdse-j-25121913270447a19fd0b6af4a50abf3': queued (progress 0%)\n0:00:46 Job 'cdse-j-25121913270447a19fd0b6af4a50abf3': queued (progress 0%)\n0:00:58 Job 'cdse-j-25121913270447a19fd0b6af4a50abf3': queued (progress 0%)\n0:01:14 Job 'cdse-j-25121913270447a19fd0b6af4a50abf3': running (progress N/A)\n0:01:34 Job 'cdse-j-25121913270447a19fd0b6af4a50abf3': running (progress N/A)\n0:01:59 Job 'cdse-j-25121913270447a19fd0b6af4a50abf3': running (progress N/A)\n0:02:30 Job 'cdse-j-25121913270447a19fd0b6af4a50abf3': running (progress N/A)\n0:03:08 Job 'cdse-j-25121913270447a19fd0b6af4a50abf3': running (progress N/A)\n0:03:56 Job 'cdse-j-25121913270447a19fd0b6af4a50abf3': running (progress N/A)\n0:04:56 Job 'cdse-j-25121913270447a19fd0b6af4a50abf3': running (progress N/A)\n0:05:56 Job 'cdse-j-25121913270447a19fd0b6af4a50abf3': finished (progress 100%)\n" + }, + { + "execution_count": 29, + "output_type": "execute_result", + "data": { + "text/plain": "", + "text/html": "\n \n \n \n \n " + }, + "metadata": {} + } + ], + "execution_count": 29 + }, + { + "id": "6f160ae8-01a0-4204-ae10-15addc18b05a", + "cell_type": "code", + "source": "df = pd.read_csv(\"worldcover_stats.csv\")\ndf.date = pd.to_datetime(df.date)\ndf.set_index([\"date\",\"feature_index\"],inplace=True)\ndf", + "metadata": { + "trusted": true + }, + "outputs": [ + { + "execution_count": 30, + "output_type": "execute_result", + "data": { + "text/plain": " treecover shrubland grassland \\\ndate feature_index \n2021-01-01 00:00:00+00:00 0 0.404512 0.000033 0.345542 \n\n cropland builtup \\\ndate feature_index \n2021-01-01 00:00:00+00:00 0 0.103134 0.038537 \n\n bare/sparse vegetation snow and ice \\\ndate feature_index \n2021-01-01 00:00:00+00:00 0 0.020193 0.00581 \n\n water herbaceous wetland \\\ndate feature_index \n2021-01-01 00:00:00+00:00 0 0.079298 0.000028 \n\n mangroves moss and lichen \ndate feature_index \n2021-01-01 00:00:00+00:00 0 0.0 0.002913 ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
treecovershrublandgrasslandcroplandbuiltupbare/sparse vegetationsnow and icewaterherbaceous wetlandmangrovesmoss and lichen
datefeature_index
2021-01-01 00:00:00+00:0000.4045120.0000330.3455420.1031340.0385370.0201930.005810.0792980.0000280.00.002913
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" + }, + "metadata": {} + } + ], + "execution_count": 30 + }, + { + "id": "755e8b5b-1842-4ed5-a443-155cb79753f5", + "cell_type": "code", + "source": "df_T = df.T\nnum_charts = len(df_T.columns)\n\nfig, axes = plt.subplots(1, num_charts, figsize=(7*num_charts, 7))\n\nif num_charts == 1:\n axes = [axes]\n\nfor ax, col in zip(axes, df_T.columns):\n # Sort values largest to smallest\n data = df_T[col].sort_values(ascending=False)\n \n # Pie chart without labels or percentages\n wedges, texts = ax.pie(\n data,\n labels=None,\n startangle=90,\n counterclock=False\n )\n \n ax.set_ylabel(\"\")\n ax.set_title(\"WoldCover Statistics - 2021\")\n\n # Legend with values and percentages\n legend_labels = [f\"{idx}: {val/data.sum()*100:.1f}%\" for idx, val in data.items()]\n \n ax.legend(\n wedges,\n legend_labels,\n loc='center left',\n bbox_to_anchor=(1, 0.5)\n )\n\nplt.tight_layout()\nplt.show()", + "metadata": { + "trusted": true + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {} + } + ], + "execution_count": 84 + }, + { + "id": "21bdc31d-70d5-4c8a-bde1-f2ecca9a920d", + "cell_type": "code", + "source": "", + "metadata": { + "trusted": true + }, + "outputs": [], + "execution_count": null + } + ] +} \ No newline at end of file diff --git a/algorithm_catalog/vito/worldcover_statistics/records/worldcover_statistics.json b/algorithm_catalog/vito/worldcover_statistics/records/worldcover_statistics.json index 7a9b6fe9..d63b942b 100644 --- a/algorithm_catalog/vito/worldcover_statistics/records/worldcover_statistics.json +++ b/algorithm_catalog/vito/worldcover_statistics/records/worldcover_statistics.json @@ -111,6 +111,12 @@ "type": "text/html", "title": "WorldCover 2021 DOI", "href": "https://doi.org/10.5281/zenodo.7254221" + }, + { + "rel": "notebook", + "type": "text/html", + "title": "Example Notebook", + "href": "https://raw.githubusercontent.com/ESA-APEx/apex_algorithms/refs/heads/main/algorithm_catalog/vito/worldcover_statistics/notebooks/worldcover_statistics.ipynb" } ] } \ No newline at end of file From 6722483cb25abc4cb72560b01eed229c3282679d Mon Sep 17 00:00:00 2001 From: bramjanssen Date: Fri, 19 Dec 2025 15:27:40 +0100 Subject: [PATCH 2/3] worldcover-stats: rename of notebook --- ...ver_statistics.ipynb => worldcover_statistics_example.ipynb} | 0 .../worldcover_statistics/records/worldcover_statistics.json | 2 +- 2 files changed, 1 insertion(+), 1 deletion(-) rename algorithm_catalog/vito/worldcover_statistics/notebooks/{worldcover_statistics.ipynb => worldcover_statistics_example.ipynb} (100%) diff --git a/algorithm_catalog/vito/worldcover_statistics/notebooks/worldcover_statistics.ipynb b/algorithm_catalog/vito/worldcover_statistics/notebooks/worldcover_statistics_example.ipynb similarity index 100% rename from algorithm_catalog/vito/worldcover_statistics/notebooks/worldcover_statistics.ipynb rename to algorithm_catalog/vito/worldcover_statistics/notebooks/worldcover_statistics_example.ipynb diff --git a/algorithm_catalog/vito/worldcover_statistics/records/worldcover_statistics.json b/algorithm_catalog/vito/worldcover_statistics/records/worldcover_statistics.json index d63b942b..d374fdaf 100644 --- a/algorithm_catalog/vito/worldcover_statistics/records/worldcover_statistics.json +++ b/algorithm_catalog/vito/worldcover_statistics/records/worldcover_statistics.json @@ -116,7 +116,7 @@ "rel": "notebook", "type": "text/html", "title": "Example Notebook", - "href": "https://raw.githubusercontent.com/ESA-APEx/apex_algorithms/refs/heads/main/algorithm_catalog/vito/worldcover_statistics/notebooks/worldcover_statistics.ipynb" + "href": "https://raw.githubusercontent.com/ESA-APEx/apex_algorithms/refs/heads/main/algorithm_catalog/vito/worldcover_statistics/notebooks/worldcover_statistics_example.ipynb" } ] } \ No newline at end of file From 87c37a6ddf8b810bf685c6b17d83a7d0a35a023e Mon Sep 17 00:00:00 2001 From: bramjanssen Date: Fri, 19 Dec 2025 15:41:16 +0100 Subject: [PATCH 3/3] worldcover-stats: updated url to notebook --- .../worldcover_statistics/records/worldcover_statistics.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/algorithm_catalog/vito/worldcover_statistics/records/worldcover_statistics.json b/algorithm_catalog/vito/worldcover_statistics/records/worldcover_statistics.json index d374fdaf..e7d22dfe 100644 --- a/algorithm_catalog/vito/worldcover_statistics/records/worldcover_statistics.json +++ b/algorithm_catalog/vito/worldcover_statistics/records/worldcover_statistics.json @@ -116,7 +116,7 @@ "rel": "notebook", "type": "text/html", "title": "Example Notebook", - "href": "https://raw.githubusercontent.com/ESA-APEx/apex_algorithms/refs/heads/main/algorithm_catalog/vito/worldcover_statistics/notebooks/worldcover_statistics_example.ipynb" + "href": "https://raw.githubusercontent.com/ESA-APEx/apex_algorithms/6722483cb25abc4cb72560b01eed229c3282679d/algorithm_catalog/vito/worldcover_statistics/notebooks/worldcover_statistics_example.ipynb" } ] } \ No newline at end of file