fix: update external product types reference

[github-actions]

Update external product types reference from daily fetch. See Python API User Guide / Product types discovery

Changed files

May be truncated.
commit c22701a47278db230655f5a830a0dc6cb5f81175

eodag/resources/ext_product_types.json
514a515,517
>       "io-lulc-annual-v02": {
>         "productType": "io-lulc-annual-v02"
>       },
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<       "naip": {
<         "productType": "naip"
<       },
793a794,796
>       "naip": {
>         "productType": "naip"
>       },
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>       "io-lulc-annual-v02": {
>         "abstract": "Time series of annual global maps of land use and land cover (LULC). It currently has data from 2017-2023. The maps are derived from ESA Sentinel-2 imagery at 10m resolution. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year.\n\nThis dataset, produced by [Impact Observatory](http://impactobservatory.com/), Microsoft, and Esri, displays a global map of land use and land cover (LULC) derived from ESA Sentinel-2 imagery at 10 meter resolution for the years 2017 - 2023. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year. This dataset was generated by Impact Observatory, which used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. Each global map was produced by applying this model to the Sentinel-2 annual scene collections from the Mircosoft Planetary Computer. Each of the maps has an assessed average accuracy of over 75%.\n\nThese maps have been improved from Impact Observatory’s [previous release](https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class) and provide a relative reduction in the amount of anomalous change between classes, particularly between “Bare” and any of the vegetative classes “Trees,” “Crops,” “Flooded Vegetation,” and “Rangeland”. This updated time series of annual global maps is also re-aligned to match the ESA UTM tiling grid for Sentinel-2 imagery.\n\nAll years are available under a Creative Commons BY-4.0.",
>         "instrument": null,
>         "platform": null,
>         "platformSerialIdentifier": null,
>         "processingLevel": null,
>         "keywords": "global,io-lulc-annual-v02,land-cover,land-use,sentinel",
>         "license": "CC-BY-4.0",
>         "title": "10m Annual Land Use Land Cover (9-class) V2",
>         "missionStartDate": "2017-01-01T00:00:00Z"
>       },
1925a1940,1950
>       "io-lulc-9-class": {
>         "abstract": "__Note__: _A new version of this item is available for your use. This mature version of the map remains available for use in existing applications. This item will be retired in December 2024. There is 2023 data available in the newer [9-class v2 dataset](https://planetarycomputer.microsoft.com/dataset/io-lulc-annual-v02)._\n\nTime series of annual global maps of land use and land cover (LULC). It currently has data from 2017-2022. The maps are derived from ESA Sentinel-2 imagery at 10m resolution. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year.\n\nThis dataset was generated by [Impact Observatory](http://impactobservatory.com/), who used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. The global map was produced by applying this model to the Sentinel-2 annual scene collections on the Planetary Computer. Each of the maps has an assessed average accuracy of over 75%.\n\nThis map uses an updated model from the [10-class model](https://planetarycomputer.microsoft.com/dataset/io-lulc) and combines Grass(formerly class 3) and Scrub (formerly class 6) into a single Rangeland class (class 11). The original Esri 2020 Land Cover collection uses 10 classes (Grass and Scrub separate) and an older version of the underlying deep learning model.  The Esri 2020 Land Cover map was also produced by Impact Observatory.  The map remains available for use in existing applications. New applications should use the updated version of 2020 once it is available in this collection, especially when using data from multiple years of this time series, to ensure consistent classification.\n\nAll years are available under a Creative Commons BY-4.0.",
>         "instrument": null,
>         "platform": null,
>         "platformSerialIdentifier": null,
>         "processingLevel": null,
>         "keywords": "global,io-lulc-9-class,land-cover,land-use,sentinel",
>         "license": "CC-BY-4.0",
>         "title": "10m Annual Land Use Land Cover (9-class)",
>         "missionStartDate": "2017-01-01T00:00:00Z"
>       },
1936,1946d1960
<       },
<       "io-lulc-9-class": {
<         "abstract": "Time series of annual global maps of land use and land cover (LULC). It currently has data from 2017-2022. The maps are derived from ESA Sentinel-2 imagery at 10m resolution. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year.\n\nThis dataset was generated by [Impact Observatory](http://impactobservatory.com/), who used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. The global map was produced by applying this model to the Sentinel-2 annual scene collections on the Planetary Computer. Each of the maps has an assessed average accuracy of over 75%.\n\nThis map uses an updated model from the [10-class model](https://planetarycomputer.microsoft.com/dataset/io-lulc) and combines Grass(formerly class 3) and Scrub (formerly class 6) into a single Rangeland class (class 11). The original Esri 2020 Land Cover collection uses 10 classes (Grass and Scrub separate) and an older version of the underlying deep learning model.  The Esri 2020 Land Cover map was also produced by Impact Observatory.  The map remains available for use in existing applications. New applications should use the updated version of 2020 once it is available in this collection, especially when using data from multiple years of this time series, to ensure consistent classification.\n\nAll years are available under a Creative Commons BY-4.0.",
<         "instrument": null,
<         "platform": null,
<         "platformSerialIdentifier": null,
<         "processingLevel": null,
<         "keywords": "global,io-lulc-9-class,land-cover,land-use,sentinel",
<         "license": "CC-BY-4.0",
<         "title": "10m Annual Land Use Land Cover (9-class)",
<         "missionStartDate": "2017-01-01T00:00:00Z"