tackled comments + limited postprocessing options in notebooks

notebooks/worldcereal_v1_demo_custom_cropland.ipynb

@@ -15,7 +15,7 @@
     "\n",
     "This notebook guides you through the process of training a custom cropland classification model using publicly available and harmonized in-situ reference data for your area of interest. Afterwards, the model can be applied to your area and season of interest to generate a cropland extent map.\n",
     "\n",
-    "Please note that for the purpose of this demo, the processing area is currently limited to 250 km² per model run."
+    "Please note that for the purpose of this demo, the processing area is currently limited to 250 km² per model run. On average, one such run consumes xxx credits on the Copernicus Data Space Ecosystem."
    ]
   },
   {
@@ -53,7 +53,12 @@
     "Click the Rectangle button on the left hand side of the map to start drawing your region of interest.\n",
     "\n",
     "Currently, there is a maximum size of 250 km² for your area within this demo. Upon exceeding this limit, an error will be shown.\n",
-    "The widget will automatically store the coordinates of the last rectangle you drew on the map.\n"
+    "You can bypass this limit by altering the code below to:<br>\n",
+    "*map = ui_map(area_limit=750)*<br>\n",
+    "\n",
+    "Processing areas beyond 750 km² are currently not supported.\n",
+    "\n",
+    "The widget will automatically store the coordinates of the last rectangle you drew on the map."
    ]
   },
   {
@@ -212,8 +217,9 @@
    "source": [
     "from worldcereal.utils.upload import deploy_model\n",
     "from openeo_gfmap.backend import cdse_connection\n",
+    "from utils import get_input\n",
     "\n",
-    "modelname = <your_model_name>\n",
+    "modelname = get_input(\"model\")\n",
     "model_url = deploy_model(cdse_connection(), custom_model, pattern=modelname)"
    ]
   },
@@ -276,25 +282,14 @@
     "import os\n",
     "from pathlib import Path\n",
     "\n",
-    "# Choose whether or not you want to spatially clean the classification results (default = True)\n",
+    "# Choose whether or not you want to spatially clean the classification results\n",
     "postprocess_result = True\n",
-    "# Choose the postprocessing method you want to use [\"smooth_probabilities\", \"majority_vote\"] (default = \"smooth_probabilities\")\n",
-    "postprocess_method = \"majority_vote\"\n",
-    "# Additiona parameters for the majority vote method:\n",
-    "kernel_size = 3 # default = 5\n",
-    "# Do you want to save the intermediate results (before applying the postprocessing)\n",
-    "save_intermediate = True #default is False\n",
-    "# Do you want to save all class probabilities in the final product? (default is False)\n",
-    "keep_class_probs = True\n",
-    "\n",
-    "postprocess_parameters = PostprocessParameters(enable=postprocess_result,\n",
-    "                                               method=postprocess_method,\n",
-    "                                               kernel_size=kernel_size,\n",
-    "                                               save_intermediate=save_intermediate,\n",
-    "                                               keep_class_probs=keep_class_probs)\n",
+    "\n",
+    "postprocess_parameters = PostprocessParameters(enable=postprocess_result)\n",
     "\n",
     "# Specify the local directory where the resulting maps should be downloaded to.\n",
-    "output_dir = Path(os.getcwd()) / 'CROPLAND_custom_test'\n",
+    "run = get_input(\"model run\")\n",
+    "output_dir = Path(os.getcwd()) / f'CROPLAND_{modelname}_{run}'\n",
     "print(f\"Output directory: {output_dir}\")"
    ]
   },
@@ -366,31 +361,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The resulting raster files can be visualized in QGIS.\n",
-    "\n",
-    "In case you are running this script on your local environment, you can alternatively use the following cells to visualize the outputs directly in this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from utils import visualize_products\n",
-    "\n",
-    "visualize_products(rasters, port=8887)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from utils import show_color_legend\n",
-    "\n",
-    "show_color_legend(rasters, \"cropland\")"
+    "The resulting raster files can be visualized in QGIS."
    ]
   }
  ],