Add io_lulc_v2 product to prod

.github/workflows/ows-config-test-build.yaml

@@ -28,7 +28,7 @@ jobs:
           python-version: 3.10.12
 
       - name: flake8 Lint
-        uses:  py-actions/flake8@v2
+        uses: py-actions/flake8@v2
         with:
           ignore: E501
           path: services/ows_refactored

docker-compose.ows.yaml

@@ -6,8 +6,6 @@ services:
     environment:
       POSTGRES_PASSWORD: opendatacubepassword
     restart: always
-    mem_limit: 2g
-    mem_reservation: 1g
 
   ows:
     image: opendatacube/ows:1.8.0-rc1
@@ -28,5 +26,3 @@ services:
       - ./services/ows_refactored:/env/config/ows_refactored/
       - ./services/inventory:/env/config/inventory/
       - ./output:/env/config/output/
-    mem_limit: 4g
-    mem_reservation: 2g

prod/products_prod.csv

@@ -17,6 +17,7 @@ dem_srtm,https://raw.githubusercontent.com/digitalearthafrica/config/master/prod
 dem_srtm_deriv,https://raw.githubusercontent.com/digitalearthafrica/config/master/products/dem_srtm_deriv.odc-product.yaml
 nasadem,https://raw.githubusercontent.com/opendatacube/datacube-dataset-config/main/products/nasadem.odc-product.yaml
 io_lulc,https://raw.githubusercontent.com/digitalearthafrica/config/master/products/io_lulc.odc-product.yaml
+io_lulc_v2,https://raw.githubusercontent.com/digitalearthafrica/config/master/products/io_lulc_v2.odc-product.yaml
 isda_soil_bedrock_depth,https://raw.githubusercontent.com/digitalearthafrica/config/master/products/isda_soil_bedrock_depth.odc-product.yaml
 isda_soil_bulk_density,https://raw.githubusercontent.com/digitalearthafrica/config/master/products/isda_soil_bulk_density.odc-product.yaml
 isda_soil_carbon_total,https://raw.githubusercontent.com/digitalearthafrica/config/master/products/isda_soil_carbon_total.odc-product.yaml

services/inventory/dev_af/inventory.json

@@ -605,9 +605,9 @@
           ]
       },
       {
-          "layer": "io_lulc",
+          "layer": "io_lulc_v2",
           "product": [
-              "io_lulc"
+              "io_lulc_v2"
           ],
           "styles_count": 1,
           "styles_list": [

services/inventory/prod_af/inventory.json

@@ -578,9 +578,9 @@
           ]
       },
       {
-          "layer": "io_lulc",
+          "layer": "io_lulc_v2",
           "product": [
-              "io_lulc"
+              "io_lulc_v2"
           ],
           "styles_count": 1,
           "styles_list": [

services/ows_refactored/dev_af_ows_root_cfg.py

@@ -420,7 +420,7 @@
                             ],
                         },
                         {
-                            "include": "ows_refactored.land_cover.ows_io_lulc_cfg.layer",
+                            "include": "ows_refactored.land_cover.ows_io_lulc_v2_cfg.layer",
                             "type": "python",
                         },
                     ],

services/ows_refactored/land_cover/ows_io_lulc_cfg.py → services/ows_refactored/land_cover/ows_io_lulc_v2_cfg.py

@@ -5,16 +5,16 @@
     "name": "style_colours",
     "title": "Coloured",
     "abstract": "Land cover colours",
-    "needed_bands": ["data"],
+    "needed_bands": ["supercell"],
     "index_function": {
         "function": "datacube_ows.band_utils.single_band",
         "mapped_bands": True,
         "kwargs": {
-            "band": "data",
+            "band": "supercell",
         },
     },
     "value_map": {
-        "data": [
+        "supercell": [
             {
                 "title": "",
                 "abstract": "",
@@ -83,18 +83,18 @@
 
 layer = {
     "title": "Impact Observatory Land Use/Land Cover",
-    "name": "io_lulc",
+    "name": "io_lulc_v2",
     "abstract": """
-Time series of annual global maps of land use and land cover (LULC), currently available from 2017 to 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.
+Time series of annual global maps of land use and land cover (LULC), currently available from 2017 to 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.
 
 This dataset was generated by Impact Observatory, 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%.
 
-For more information, see https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class.
+For more information, see https://planetarycomputer.microsoft.com/dataset/io-lulc-annual-v02.
 """,
-    "product_name": "io_lulc",
+    "product_name": "io_lulc_v2",
     "time_resolution": "summary",
     "bands": {
-        "data": [],
+        "supercell": [],
     },
     "resource_limits": reslim_land_cover,
     "image_processing": {

services/ows_refactored/prod_af_ows_root_cfg.py

@@ -377,7 +377,7 @@
                             ],
                         },
                         {
-                            "include": "ows_refactored.land_cover.ows_io_lulc_cfg.layer",
+                            "include": "ows_refactored.land_cover.ows_io_lulc_v2_cfg.layer",
                             "type": "python",
                         },
                     ],