High-level STAC API

[kylebarron]

Users should be able to pass in a STAC item and:

• see what algorithms are available for that image
• the STACLayer should automatically handle fetching the requisite bands and rendering an underlying COGLayer

[jo-chemla]

An automated way to detect COG type and pre-select which algorithms to let the user choose from would indeed be great. A while back I did a quick gathering of existing webmaps which let the user select algorithms to apply to a COG here TerriaJS/terriajs#7665. Pasting it below in case it's useful

Edit: just discovered a great resource some might probably know, awesome-spectral-indices, a well structured JSON structure with python, jl etc bindings, and with a live espectro app (see doc and paper) where awesome-ASI, which includes indices for Vegetation, Burn, Water, Snow, Soil, Urban, Kernel, RADAR. ASls refers to apps lexcube and openeo-web-editor

Similar alternatives

Existing tooling with similar feature-set

• geomatico are behind the maplibre-cog-protocol, with examples advanced-viewer and custom-color but allows visualizations of vegetation indices or other thematic indices, as well as gain/gamma correction, RGB contrast stretching,
• The best existing web UI - but only applicable to Sentinel Data - is probably that of Copernicus Browser - a bit hidden, accessible by selecting a theme like Vegetation or Agriculture, selecting a Pin, then in Layers panel, there are simple and advanced modes, nice indices computation based on raster bands, as well as composites or index A/B or (A-B)/(A+B). Some code might be on this repo and VisualizationPanel/VisualizationLayer/CustomVisualizationLayer
• The great TiTiler might soon get expression support. It is based on numexpr, supported-operators interprets expressions like (B1 - B2) / (B1 + B2)
• ESRI layers-imagerytilelayer-cog. Useful bandarithmetic and rasterFunctionTemplate
• OpenLayers cog-stretch example and editable CSB
• geotiff-js cog-explorer can ingest any COG but has support only for pipeline-steps sigmoid and gamma, no more granular control.
• kylebarron/deck.gl-raster and keplergl raster-tile-layer docs

[gadomski]

Some considerations for general STAC support:

• Sometimes a STAC item will have a single multi-band raster asset, sometimes there will be several single-band assets. A generic STAC viewer would handle both cases.
• There some differences between STAC v1.1 and v1.0:
  • v1.1:
    • nodata, data_type, and friends are now "common metadata" and don't require an extension prefix
    • Common metadata and extension values can exist on an Asset, and Item, or on a Bands structure
  • v1.0: noata, data_type, etc comes from the raster extension v1.1.0 or v1.0.0, and there is no bands structure
• If present, the render extension has information about resampling, rescaling, and other values important for visualization (confusingly, it also has nodata 🙈)

Without thinking too deeply about it, a layer interface might look like:

new StacLayer({
  id,
  item,
  assetKeys: ["B01", "B02", "B03"],  // Required unless either (a) there's one asset with three or four bands, or (b) there's three or four single-band assets
  noData: 0,  // If not provided, will be derived from the value on the asset, `bands`, or from the item
  ...  // Similar stuff for the other attributes, e.g. data_type, resampling, etc...
});

You could also punt on all the auto-detection stuff, and only expose the attributes you need ... we could build the auto-detection (aka getting stuff from assets or items or bands) somewhere else.

For a single STAC item, I don't think you have to think about the multi-resolution or multi-CRS problem. For a mosaic of STAC items, you do, but maybe that's a problem that's more general than just STAC support?

[kylebarron]

• Sometimes a STAC item will have a single multi-band raster asset, sometimes there will be several single-band assets. A generic STAC viewer would handle both cases.

💯

Without thinking too deeply about it, a layer interface might look like:

new StacLayer({
id,
item,
assetKeys: ["B01", "B02", "B03"], // Required unless either (a) there's one asset with three or four bands, or (b) there's three or four single-band assets
});

I feel like it would be nice to support arbitrary band math here, where the layer then figures out what to load. Like

STACLayer({
  id,
  item,
  render: b2 - b1 / b2 + b1
})

For a single STAC item, I don't think you have to think about the multi-resolution or multi-CRS problem.

Different assets within a STAC can have different resolutions, right? It looks like this Sentinel item does.

{
  "type": "Feature",
  "stac_version": "1.0.0",
  "stac_extensions": [
    "https://stac-extensions.github.io/mgrs/v1.0.0/schema.json",
    "https://stac-extensions.github.io/view/v1.0.0/schema.json",
    "https://stac-extensions.github.io/processing/v1.1.0/schema.json",
    "https://stac-extensions.github.io/eo/v1.1.0/schema.json",
    "https://stac-extensions.github.io/sentinel-2/v1.0.0/schema.json",
    "https://stac-extensions.github.io/grid/v1.1.0/schema.json",
    "https://stac-extensions.github.io/projection/v1.1.0/schema.json",
    "https://stac-extensions.github.io/raster/v1.1.0/schema.json"
  ],
  "id": "S2B_31SCV_20260108_0_L2A",
  "geometry": {
    "type": "Polygon",
    "coordinates": [
      [
        [
          1.880778210540626,
          36.13965961835193
        ],
        [
          1.6068282972708232,
          35.14688902617884
        ],
        [
          2.009709677884217,
          35.15085199698541
        ],
        [
          1.9974176909883568,
          36.14069288925992
        ],
        [
          1.880778210540626,
          36.13965961835193
        ]
      ]
    ]
  },
  "bbox": [
    1.606828,
    35.146889,
    2.00971,
    36.140693
  ],
  "properties": {
    "created": "2026-01-08T15:24:35.985373+00:00",
    "platform": "sentinel-2b",
    "constellation": "sentinel-2",
    "instruments": [
      "msi"
    ],
    "eo:cloud_cover": 62.19793,
    "proj:epsg": 32631,
    "proj:centroid": {
      "lat": 35.5535,
      "lon": 1.86167
    },
    "mgrs:utm_zone": 31,
    "mgrs:latitude_band": "S",
    "mgrs:grid_square": "CV",
    "grid:code": "MGRS-31SCV",
    "view:azimuth": 107.4988804041063,
    "view:incidence_angle": 10.779854439756077,
    "view:sun_azimuth": 158.777934238896,
    "view:sun_elevation": 29.1397345026408,
    "s2:tile_id": "S2B_OPER_MSI_L2A_TL_2BPS_20260108T143058_A046180_T31SCV_N05.11",
    "s2:degraded_msi_data_percentage": 0.0,
    "s2:nodata_pixel_percentage": 78.456962,
    "s2:saturated_defective_pixel_percentage": 0.0,
    "s2:cloud_shadow_percentage": 19.355164,
    "s2:vegetation_percentage": 0.164298,
    "s2:not_vegetated_percentage": 15.811461,
    "s2:water_percentage": 0.070352,
    "s2:unclassified_percentage": 1.472965,
    "s2:medium_proba_clouds_percentage": 20.545043,
    "s2:high_proba_clouds_percentage": 41.652808,
    "s2:thin_cirrus_percentage": 0.000077,
    "s2:snow_ice_percentage": 0.793797,
    "s2:product_type": "S2MSI2A",
    "s2:processing_baseline": "05.11",
    "s2:product_uri": "S2B_MSIL2A_20260108T103319_N0511_R108_T31SCV_20260108T143058.SAFE",
    "s2:generation_time": "2026-01-08T14:30:58.000000Z",
    "s2:datatake_id": "GS2B_20260108T103319_046180_N05.11",
    "s2:datatake_type": "INS-NOBS",
    "s2:datastrip_id": "S2B_OPER_MSI_L2A_DS_2BPS_20260108T143058_S20260108T103821_N05.11",
    "s2:reflectance_conversion_factor": 1.03431330721951,
    "datetime": "2026-01-08T10:41:12.878000Z",
    "s2:sequence": "0",
    "earthsearch:s3_path": "s3://sentinel-cogs/sentinel-s2-l2a-cogs/31/S/CV/2026/1/S2B_31SCV_20260108_0_L2A",
    "earthsearch:payload_id": "roda-sentinel2/workflow-sentinel2-to-stac/a66399729102e1376f5ab704424357d6",
    "earthsearch:boa_offset_applied": true,
    "processing:software": {
      "sentinel2-to-stac": "2025.03.06"
    },
    "updated": "2026-01-08T15:24:35.985373+00:00"
  },
  "links": [
    {
      "rel": "self",
      "href": "s3://sentinel-cogs/sentinel-s2-l2a-cogs/31/S/CV/2026/1/S2B_31SCV_20260108_0_L2A/S2B_31SCV_20260108_0_L2A.json",
      "type": "application/json"
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    {
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      "type": "application/json"
    },
    {
      "rel": "license",
      "href": "https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice"
    },
    {
      "rel": "derived_from",
      "href": "https://earth-search.aws.element84.com/v1/collections/sentinel-2-l1c/items/S2B_31SCV_20260108_0_L1C",
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    }
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    "cloud": {
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      "type": "image/jp2",
      "title": "Cloud Probabilities",
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      "roles": [
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    "coastal": {
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      "eo:bands": [
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      "type": "image/tiff; application=geotiff; profile=cloud-optimized",
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      "eo:bands": [
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      "eo:bands": [
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}

[gadomski]

I feel like it would be nice to support arbitrary band math here, where the layer then figures out what to load

A pleasant API would be one that supports "band math" and "asset math", e.g. for that sentinel example you could math between data from different source files. I don't have a sense of what that looks like, though.

Different assets within a STAC can have different resolutions, right? It looks like this Sentinel item does.

Yes but that's relatively uncommon, and often those assets aren't "meant" to be combined (not always). Multi-resolution would be in the nice-to-have category, at least to me.

[kylebarron]

Aren't there a lot of sentinel/landsat band combinations that would need to work across bands with different spatial resolutions?

[gadomski]

Aren't there a lot of sentinel/landsat band combinations that would need to work across bands with different spatial resolutions?

I guess so ... I might be locked into the "traditional" way of thinking where you only combine bands w/ the same resolution for viz.

[kylebarron]

Edit: just discovered a great resource some might probably know, awesome-spectral-indices, a well structured JSON structure with python, jl etc bindings, and with a live espectro app (see doc and paper) where awesome-ASI, which includes indices for Vegetation, Burn, Water, Snow, Soil, Urban, Kernel, RADAR. ASls refers to apps lexcube and openeo-web-editor

That repo is nice in theory but storing these formulas as strings makes them very hard to parse. It looks like the source library uses py_expression_eval.... which is explicitly archived.

So we might have to... fork? awesome-spectral-indices at some point. Or, once we're closer to supporting custom high-level rendering then perhaps we can contact the author.

Related to #417 , #215