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Merge branch 'main' into add-earthdaily-methane-detection
2 parents 76127b9 + e0117c2 commit a177dc7

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‎.github/workflows/benchmarks.yaml‎

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with:
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name: report
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path: qa/benchmarks/report/
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- name: Merge Parquet Files and Upload to S3
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env:
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APEX_ALGORITHMS_S3_ACCESS_KEY_ID: ${{ secrets.APEX_ALGORITHMS_S3_ACCESS_KEY_ID }}
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APEX_ALGORITHMS_S3_SECRET_ACCESS_KEY: ${{ secrets.APEX_ALGORITHMS_S3_SECRET_ACCESS_KEY }}
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APEX_ALGORITHMS_S3_ENDPOINT_URL: "https://s3.waw3-1.cloudferro.com"
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APEX_ALGORITHMS_S3_DEFAULT_REGION: "waw3-1"
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run: |
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python qa/benchmarks/merge_parquet.py \
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--s3-client "${{ secrets.APEX_ALGORITHMS_S3_ACCESS_KEY_ID }}" \
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--s3-secret "${{ secrets.APEX_ALGORITHMS_S3_SECRET_ACCESS_KEY }}" \
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--s3-endpoint "${{ env.APEX_ALGORITHMS_S3_ENDPOINT_URL }}" \
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--s3-region "${{ env.APEX_ALGORITHMS_S3_DEFAULT_REGION }}" \
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--s3-bucket "apex-benchmarks" \
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--input-path "metrics/v1/metrics.parquet" \
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--output-path "metrics/v1/metrics-merged.parquet" \
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--s3-output

‎algorithm_catalog/developmentseed/atmospheric_resistant_vegetation_index/openeo_udp/atmospheric_resistant_vegetation_index.json‎

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{
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"process_graph": {
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"loadcollection1": {
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"process_id": "load_collection",
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"arguments": {
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"bands": [
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"B02",
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"B04",
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"B8A"
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],
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"id": "SENTINEL2_L2A",
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"spatial_extent": {
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"west": 14.09,
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"east": 14.27,
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"south": 45.174,
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"north": 45.25
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},
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"temporal_extent": [
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"2025-05-10",
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"2025-05-12"
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]
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}
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},
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"reducedimension1": {
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"process_id": "reduce_dimension",
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"arguments": {
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"data": {
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"from_node": "loadcollection1"
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},
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"dimension": "t",
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"reducer": {
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"process_graph": {
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"first1": {
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"process_id": "first",
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"arguments": {
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"data": {
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"from_parameter": "data"
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}
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},
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"result": true
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}
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}
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}
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}
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},
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"apply1": {
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"process_id": "apply",
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"arguments": {
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"data": {
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"from_node": "reducedimension1"
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},
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"process": {
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"process_graph": {
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"divide1": {
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"process_id": "divide",
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"arguments": {
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"x": {
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"from_parameter": "x"
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},
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"y": 10000.0
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},
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"result": true
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}
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}
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}
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}
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},
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"applydimension1": {
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"process_id": "apply_dimension",
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"arguments": {
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"data": {
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"from_node": "apply1"
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},
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"dimension": "bands",
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"process": {
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"process_graph": {
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"arrayelement1": {
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"process_id": "array_element",
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"arguments": {
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"data": {
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"from_parameter": "data"
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},
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"index": 2
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}
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},
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"arrayelement2": {
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"process_id": "array_element",
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"arguments": {
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"data": {
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"from_parameter": "data"
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},
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"index": 1
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}
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},
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"subtract1": {
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"process_id": "subtract",
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"arguments": {
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"x": {
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"from_node": "arrayelement1"
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},
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"y": {
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"from_node": "arrayelement2"
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}
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}
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},
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"arrayelement3": {
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"process_id": "array_element",
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"arguments": {
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"data": {
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"from_parameter": "data"
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},
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"index": 0
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}
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},
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"subtract2": {
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"process_id": "subtract",
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"arguments": {
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"x": {
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"from_node": "arrayelement2"
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},
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"y": {
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"from_node": "arrayelement3"
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}
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}
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},
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"multiply1": {
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"process_id": "multiply",
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"arguments": {
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"x": 0.106,
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"y": {
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"from_node": "subtract2"
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}
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}
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},
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"subtract3": {
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"process_id": "subtract",
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"arguments": {
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"x": {
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"from_node": "subtract1"
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},
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"y": {
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"from_node": "multiply1"
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}
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}
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},
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"add1": {
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"process_id": "add",
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"arguments": {
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"x": {
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"from_node": "arrayelement1"
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},
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"y": {
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"from_node": "arrayelement2"
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}
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}
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},
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"subtract4": {
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"process_id": "subtract",
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"arguments": {
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"x": {
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"from_node": "add1"
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},
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"y": {
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"from_node": "multiply1"
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}
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}
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},
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"divide2": {
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"process_id": "divide",
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"arguments": {
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"x": {
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"from_node": "subtract3"
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},
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"y": {
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"from_node": "subtract4"
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}
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},
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"result": true
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}
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}
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}
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}
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},
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"apply2": {
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"process_id": "apply",
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"arguments": {
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"data": {
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"from_node": "applydimension1"
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},
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"process": {
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"process_graph": {
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"linearscalerange1": {
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"process_id": "linear_scale_range",
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"arguments": {
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"inputMax": 1,
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"inputMin": 0,
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"outputMax": 255,
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"outputMin": 0,
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"x": {
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"from_parameter": "x"
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}
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},
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"result": true
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}
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}
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}
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},
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"result": true
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}
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},
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"parameters": [
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{
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"name": "time",
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"description": "Temporal range for data acquisition",
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"schema": {},
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"default": [
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"2025-05-10",
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"2025-05-12"
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],
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"optional": true
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},
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{
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"name": "bounding_box",
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"description": "Spatial extent for the \u010cepi\u0107 plain agricultural area",
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"schema": {},
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"default": {
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"west": 14.09,
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"east": 14.27,
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"south": 45.174,
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"north": 45.25
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},
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"optional": true
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}
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],
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"id": "atmospheric_resistant_vegetation_index",
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"summary": "Calculates the Atmospherically Resistant Vegetation Index (ARVI) from Sentinel-2 imagery using a blue-band self-correction to reduce atmospheric scattering effects compared to standard NDVI.",
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"description": "Calculates the Atmospherically Resistant Vegetation Index (ARVI) from Sentinel-2 L2A imagery. ARVI extends standard NDVI by incorporating the blue band as a self-correction term for atmospheric scattering from haze, smoke, and aerosols, using an empirically derived weighting factor (y = 0.106). Suitable for monitoring crop health, detecting early vegetation stress, and mapping vegetation density gradients across regions with variable atmospheric conditions such as tropical or fire-affected areas."
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}

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