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worldcover statistics algorithm #165

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a9f4bca
worldcover statistics algorithm
jdries May 1, 2025
8985daa
worldcover statistics algorithm
jdries May 3, 2025
0c7a6cf
worldcover statistics algorithm
jdries May 3, 2025
bf8bc11
worldcover statistics algorithm
jdries May 3, 2025
b3f5d05
use geojson parameter
jdries May 22, 2025
f6b2697
update record
jdries May 22, 2025
fe0b332
update description and reducer
jdries May 22, 2025
130186c
add scenario
jdries May 22, 2025
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44 changes: 44 additions & 0 deletions algorithm_catalog/vito/worldcover_statistics/benchmark_scenarios/worldcover_statistics.json
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[{
"id": "worldcover_statistics",
"type": "openeo",
"description": "World cover statistics.",
"backend": "openeofed.dataspace.copernicus.eu",
"process_graph": {
"worldcover_statistics": {
"arguments": {
"geometries": {
"coordinates": [
[
[
6.990046173926822,
50.92370033913926
],
[
6.990915651805,
50.8595444392102
],
[
7.239586324963913,
50.84527557041392
],
[
7.238716847085734,
50.92917965051333
],
[
6.990046173926822,
50.92370033913926
]
]
],
"type": "Polygon"
}
},
"description": "Land cover statistics for one or more areas based on ESA WorldCover 2021, provided by Terrascope.",
"namespace": "https://github.com/raw/ESA-APEx/apex_algorithms/refs/heads/worldcover_stats/algorithm_catalog/vito/worldcover_statistics/openeo_udp/worldcover_statistics.json",
"process_id": "worldcover_statistics",
"result": true
}
},
"reference_data": {}
}]
16 changes: 16 additions & 0 deletions algorithm_catalog/vito/worldcover_statistics/openeo_udp/README.md
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# 2021 landcover statistics

Computes landcover statistics over multiple user provided areas, based on the ESA WorldCover product.

As input, the service takes GeoJSON, which can also be a FeatureCollection defining multiple feature.

The output is a csv file containing the fraction of a specific landcover present in each area.

This service is maintained and provided by Terrascope.

![statistics](statistics.png)

## Methodology

The European Space Agency (ESA) WorldCover 10 m 2021 product provides a global land cover map for 2021 at 10 m resolution based on Sentinel-1 and Sentinel-2 data. The WorldCover product comes with 11 land cover classes, aligned with UN-FAO's Land Cover Classification System, and has been generated in the framework of the ESA WorldCover project.

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259 changes: 259 additions & 0 deletions algorithm_catalog/vito/worldcover_statistics/openeo_udp/worldcover_statistics.json
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{
"id": "worldcover_statistics",
"summary": "Land cover statistics for one or more areas based on ESA WorldCover 2021, provided by Terrascope.",
"description": "# 2021 landcover statistics\n\nComputes landcover statistics over multiple areas, based on the ESA WorldCover product.\n\nAs input, the service takes GeoJSON, which can also be a FeatureCollection defining multiple feature.\n\nThe output is a csv file containing the fraction of a specific landcover present in each area.\n\nThis service is maintained and provided by Terrascope.\n\n![statistics](statistics.png)\n\n## Methodology\n\nThe European Space Agency (ESA) WorldCover 10 m 2021 product provides a global land cover map for 2021 at 10 m resolution based on Sentinel-1 and Sentinel-2 data. The WorldCover product comes with 11 land cover classes, aligned with UN-FAO's Land Cover Classification System, and has been generated in the framework of the ESA WorldCover project.\n\n",
"process_graph": {
"load4": {
"process_id": "load_collection",
"arguments": {
"spatial_extent": null,
"temporal_extent": null,
"bands": [
"MAP"
],
"id": "ESA_WORLDCOVER_10M_2021_V2"
}
},
"apply6": {
"process_id": "apply_dimension",
"arguments": {
"data": {
"from_node": "load4"
},
"dimension": "bands",
"process": {
"process_graph": {
"1": {
"arguments": {
"data": [
{
"process_graph": {
"eq1": {
"arguments": {
"x": {
"from_parameter": "data"
},
"y": 10
},
"process_id": "eq",
"result": true
}
}
},
{
"process_graph": {
"eq1": {
"arguments": {
"x": {
"from_parameter": "data"
},
"y": 20
},
"process_id": "eq",
"result": true
}
}
},
{
"process_graph": {
"eq1": {
"arguments": {
"x": {
"from_parameter": "data"
},
"y": 30
},
"process_id": "eq",
"result": true
}
}
},
{
"process_graph": {
"eq1": {
"arguments": {
"x": {
"from_parameter": "data"
},
"y": 40
},
"process_id": "eq",
"result": true
}
}
},
{
"process_graph": {
"eq1": {
"arguments": {
"x": {
"from_parameter": "data"
},
"y": 50
},
"process_id": "eq",
"result": true
}
}
},
{
"process_graph": {
"eq1": {
"arguments": {
"x": {
"from_parameter": "data"
},
"y": 60
},
"process_id": "eq",
"result": true
}
}
},
{
"process_graph": {
"eq1": {
"arguments": {
"x": {
"from_parameter": "data"
},
"y": 70
},
"process_id": "eq",
"result": true
}
}
},
{
"process_graph": {
"eq1": {
"arguments": {
"x": {
"from_parameter": "data"
},
"y": 80
},
"process_id": "eq",
"result": true
}
}
},
{
"process_graph": {
"eq1": {
"arguments": {
"x": {
"from_parameter": "data"
},
"y": 90
},
"process_id": "eq",
"result": true
}
}
},
{
"process_graph": {
"eq1": {
"arguments": {
"x": {
"from_parameter": "data"
},
"y": 95
},
"process_id": "eq",
"result": true
}
}
},
{
"process_graph": {
"eq1": {
"arguments": {
"x": {
"from_parameter": "data"
},
"y": 100
},
"process_id": "eq",
"result": true
}
}
}
]
},
"process_id": "array_create",
"result": true
}
}
}
}
},
"rename7": {
"process_id": "rename_labels",
"arguments": {
"data": {
"from_node": "apply6"
},
"dimension": "bands",
"target": [
"treecover",
"shrubland",
"grassland",
"cropland",
"builtup",
"bare/sparse vegetation",
"snow and ice",
"water",
"herbaceous wetland",
"mangroves",
"moss and lichen"
]
}
},
"aggregate5": {
"process_id": "aggregate_spatial",
"arguments": {
"data": {
"from_node": "rename7"
},
"geometries": {
"from_parameter": "geometries"
},
"reducer": {
"process_graph": {
"count1": {
"arguments": {
"data": {
"from_parameter": "data"
}
},
"process_id": "mean",
"result": true
}
}
}
}
},
"save3": {
"process_id": "save_result",
"arguments": {
"data": {
"from_node": "aggregate5"
},
"format": "CSV"
},
"result": true
}
},
"parameters": [
{
"name": "geometries",
"description": "Geometries for which the statistics will be computed, as a GeoJSON FeatureCollection. Feature properties are preserved for vector data cubes and all GeoJSON Features.\n\nOne value will be computed per label in the dimension of type `geometries`, GeoJSON `Feature` or `Geometry`. For a `FeatureCollection` multiple values will be computed, one value per contained `Feature`. No values will be computed for empty geometries. For example, a single value will be computed for a `MultiPolygon`, but two values will be computed for a `FeatureCollection` containing two polygons.\n\n- For **polygons**, the process considers all pixels for which the point at the pixel center intersects with the corresponding polygon (as defined in the Simple Features standard by the OGC).\n- For **points**, the process considers the closest pixel center.\n- For **lines** (line strings), the process considers all the pixels whose centers are closest to at least one point on the line.\n\nThus, pixels may be part of multiple geometries and be part of multiple aggregations. No operation is applied to geometries that are outside of the bounds of the data.",
"schema": {
"type": "object",
"subtype": "geojson"
}
}
]
}
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