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

commit 7bd54c5475b1bba6cec1d13be6ed7b71870b7351

eodag/resources/ext_product_types.json
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<       "io-lulc-9-class": {
<         "productType": "io-lulc-9-class"
<       },
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>       "io-lulc-9-class": {
>         "productType": "io-lulc-9-class"
>       },
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>       },
>       "io-biodiversity": {
>         "productType": "io-biodiversity"
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<       "io-lulc-9-class": {
<         "abstract": "Time series of annual global maps of land use and land cover (LULC). It currently has data from 2017-2021. 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"
<       },
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>       "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"
>       },
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>       },
>       "io-biodiversity": {
>         "abstract": "Generated by [Impact Observatory](https://www.impactobservatory.com/), in collaboration with [Vizzuality](https://www.vizzuality.com/), these datasets estimate terrestrial Biodiversity Intactness as 100-meter gridded maps for the years 2017-2020.\n\nMaps depicting the intactness of global biodiversity have become a critical tool for spatial planning and management, monitoring the extent of biodiversity across Earth, and identifying critical remaining intact habitat. Yet, these maps are often years out of date by the time they are available to scientists and policy-makers. The datasets in this STAC Collection build on past studies that map Biodiversity Intactness using the [PREDICTS database](https://onlinelibrary.wiley.com/doi/full/10.1002/ece3.2579) of spatially referenced observations of biodiversity across 32,000 sites from over 750 studies. The approach differs from previous work by modeling the relationship between observed biodiversity metrics and contemporary, global, geospatial layers of human pressures, with the intention of providing a high resolution monitoring product into the future.\n\nBiodiversity intactness is estimated as a combination of two metrics: Abundance, the quantity of individuals, and Compositional Similarity, how similar the composition of species is to an intact baseline. Linear mixed effects models are fit to estimate the predictive capacity of spatial datasets of human pressures on each of these metrics and project results spatially across the globe. These methods, as well as comparisons to other leading datasets and guidance on interpreting results, are further explained in a methods [white paper](https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/io-biodiversity/Biodiversity_Intactness_whitepaper.pdf) entitled “Global 100m Projections of Biodiversity Intactness for the years 2017-2020.”\n\nAll years are available under a Creative Commons BY-4.0 license.\n",
>         "instrument": null,
>         "platform": null,
>         "platformSerialIdentifier": null,
>         "processingLevel": null,
>         "keywords": "biodiversity,global,io-biodiversity",
>         "license": "CC-BY-4.0",
>         "title": "Biodiversity Intactness",
>         "missionStartDate": "2017-01-01T00:00:00Z"