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issue #884 Update for mask_scl_dilation function (#128)
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* issue #884 Update for mask_scl_dilation function

* issue #884 Update for mask_scl_dilation function

* issue #884 Update for mask_scl_dilation function

* issue #884 Update for mask_scl_dilation function

* issue #884 Update for mask_scl_dilation function

* issue #884 Update for mask_scl_dilation function

* issue #884 Update for mask_scl_dilation function
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@ElienVandermaesenVITO
ElienVandermaesenVITO authored Jan 15, 2025
1 parent f7ff0e3 commit 8bb8e37
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Showing 25 changed files with 388 additions and 88 deletions.
16 changes: 13 additions & 3 deletions notebooks/AI4FOOD_PeakValley_Detection_NDVI_S2/peakvalley_detection.ipynb
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Expand Up @@ -1333,12 +1333,22 @@
"outputs": [],
"source": [
"# download the data if it doesn't exist yet\n",
"\n",
"scl = connection.load_collection('SENTINEL2_L2A_SENTINELHUB',\n",
" spatial_extent=spat_ext,\n",
" temporal_extent=temp_ext,\n",
" bands=[\"SCL\"])\n",
"cloud_mask = scl.process(\n",
" \"to_scl_dilation_mask\",\n",
" data=scl,\n",
" kernel1_size=17, kernel2_size=77,\n",
" mask1_values=[2, 4, 5, 6, 7],\n",
" mask2_values=[3, 8, 9, 10, 11],\n",
" erosion_kernel_size=3)\n",
"s2 = connection.load_collection('SENTINEL2_L2A_SENTINELHUB',\n",
" spatial_extent=spat_ext,\n",
" temporal_extent=temp_ext,\n",
" bands=[\"B04\", \"B08\", \"SCL\"])\n",
"s2 = s2.process(\"mask_scl_dilation\", data=s2, scl_band_name=\"SCL\")\n",
" bands=[\"B04\", \"B08\"])\n",
"s2 = s2.mask(cloud_mask)\n",
"ndvi_cube = s2.ndvi(red=\"B04\", nir=\"B08\", target_band='NDVI')\n",
"\n",
"if not os.path.exists('./s2_meadow.nc'):\n",
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17 changes: 14 additions & 3 deletions notebooks/AI4FOOD_Whittaker_OpenEO_NDVI_S2.ipynb
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Expand Up @@ -94,11 +94,22 @@
"metadata": {},
"outputs": [],
"source": [
"scl = connection.load_collection('SENTINEL2_L2A_SENTINELHUB',\n",
" spatial_extent=spat_ext,\n",
" temporal_extent=temp_ext,\n",
" bands=[\"SCL\"])\n",
"cloud_mask = scl.process(\n",
" \"to_scl_dilation_mask\",\n",
" data=scl,\n",
" kernel1_size=17, kernel2_size=77,\n",
" mask1_values=[2, 4, 5, 6, 7],\n",
" mask2_values=[3, 8, 9, 10, 11],\n",
" erosion_kernel_size=3)\n",
"s2 = connection.load_collection('SENTINEL2_L2A_SENTINELHUB',\n",
" spatial_extent=spat_ext,\n",
" temporal_extent=temp_ext,\n",
" bands=[\"B04\",\"B08\",\"SCL\"])\n",
"s2_cloudmasked = s2.process(\"mask_scl_dilation\", data=s2, scl_band_name=\"SCL\")\n",
" bands=[\"B04\",\"B08\"])\n",
"s2_cloudmasked = s2.mask(cloud_mask)\n",
"ndvi_cube = s2.ndvi(red=\"B04\", nir=\"B08\")\n",
"ndvi_cube_cloudmasked = s2_cloudmasked.ndvi(red=\"B04\", nir=\"B08\")"
]
Expand Down Expand Up @@ -332,4 +343,4 @@
},
"nbformat": 4,
"nbformat_minor": 5
}
}
15 changes: 13 additions & 2 deletions notebooks/Local/FuseTS - MOGPR.ipynb
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Expand Up @@ -215,11 +215,22 @@
"VV = s1_asc.band(\"VV\")\n",
"rvi_asc_cube = (VH + VH) / (VV + VH)\n",
"\n",
"scl = connection.load_collection('SENTINEL2_L2A_SENTINELHUB',\n",
" spatial_extent=spat_ext,\n",
" temporal_extent=temp_ext,\n",
" bands=[\"SCL\"])\n",
"cloud_mask = scl.process(\n",
" \"to_scl_dilation_mask\",\n",
" data=scl,\n",
" kernel1_size=17, kernel2_size=77,\n",
" mask1_values=[2, 4, 5, 6, 7],\n",
" mask2_values=[3, 8, 9, 10, 11],\n",
" erosion_kernel_size=3)\n",
"s2 = connection.load_collection('SENTINEL2_L2A_SENTINELHUB',\n",
" spatial_extent=spat_ext,\n",
" temporal_extent=temp_ext,\n",
" bands=[\"B04\", \"B08\", \"SCL\"])\n",
"s2 = s2.process(\"mask_scl_dilation\", data=s2, scl_band_name=\"SCL\")\n",
" bands=[\"B04\", \"B08\"])\n",
"s2 = s2.mask(cloud_mask=cloud_mask)\n",
"ndvi_cube = s2.ndvi(red=\"B04\", nir=\"B08\")"
]
},
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56 changes: 41 additions & 15 deletions notebooks/Local/FuseTS - Peak Valley Detection.ipynb
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Expand Up @@ -57,7 +57,8 @@
"More information on the usage of openEO's Python client can be found on [GitHub](https://github.com/Open-EO/openeo-python-client).\n",
"\n",
"The first step is to connect to an openEO compatible backend.\n"
]
],
"id": "ccc826d7e3e794ad"
},
{
"cell_type": "code",
Expand All @@ -74,14 +75,16 @@
],
"source": [
"connection = openeo.connect(\"openeo.vito.be\").authenticate_oidc()"
]
],
"id": "e6ad5560fc9b30dc"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next we define the area of interest, in this case an extent, for which we would like to fetch time series data.\n"
]
],
"id": "efbacd1f48fedc4a"
},
{
"cell_type": "code",
Expand All @@ -98,14 +101,16 @@
")\n",
"spat_ext = dict(west=minx, east=maxx, north=maxy, south=miny, crs=4326)\n",
"temp_ext = [\"2021-01-01\", \"2021-12-31\"]"
]
],
"id": "10a0472a737b4773"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will create an openEO process to calculate the NDVI time series for our area of interest. We'll begin by using the SENTINEL2_L2A_SENTINELHUB collection, and apply a cloud masking algorithm to remove any interfering clouds before calculating the NDVI values.\n"
]
],
"id": "df3ee7d1a70f77ee"
},
{
"cell_type": "code",
Expand All @@ -114,22 +119,37 @@
"outputs": [],
"source": [
"# define openEO pipeline\n",
"scl = connection.load_collection(\n",
" \"SENTINEL2_L2A_SENTINELHUB\",\n",
" spatial_extent=spat_ext,\n",
" temporal_extent=temp_ext,\n",
" bands=[\"SCL\"],\n",
")\n",
"cloud_mask = scl.process(\n",
" \"to_scl_dilation_mask\",\n",
" data=scl,\n",
" kernel1_size=17, kernel2_size=77,\n",
" mask1_values=[2, 4, 5, 6, 7],\n",
" mask2_values=[3, 8, 9, 10, 11],\n",
" erosion_kernel_size=3)\n",
"s2 = connection.load_collection(\n",
" \"SENTINEL2_L2A_SENTINELHUB\",\n",
" spatial_extent=spat_ext,\n",
" temporal_extent=temp_ext,\n",
" bands=[\"B04\", \"B08\", \"SCL\"],\n",
" bands=[\"B04\", \"B08\"],\n",
")\n",
"s2 = s2.process(\"mask_scl_dilation\", data=s2, scl_band_name=\"SCL\")\n",
"s2 = s2.mask(cloud_mask)\n",
"ndvi_cube = s2.ndvi(red=\"B04\", nir=\"B08\", target_band=\"NDVI\")"
]
],
"id": "5d862b567ae6f299"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we have calculated the NDVI time series for our area of interest, we can request openEO to download the result to our local storage. This will allow us to access the file and use it for further analysis in this notebook. However, if we have already downloaded the file, we can use the existing time series to continue our analysis without the need for a new download.\n"
]
],
"id": "d7c22a63e09838e8"
},
{
"cell_type": "code",
Expand Down Expand Up @@ -174,7 +194,8 @@
"metadata": {},
"source": [
"### Plot Image Chips Across Time\n"
]
],
"id": "63390c062b62fadc"
},
{
"cell_type": "code",
Expand Down Expand Up @@ -209,14 +230,16 @@
" ax.set_title(img.t.dt.date.to_numpy())\n",
"\n",
"plt.tight_layout()"
]
],
"id": "efc69fe5bfb141e5"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot Time Series for Specific Polygon Area\n"
]
],
"id": "a7ed4de53954b38f"
},
{
"cell_type": "code",
Expand Down Expand Up @@ -258,7 +281,8 @@
"ax.plot(*np.array(geometry[\"coordinates\"][0]).T, \"C3\", lw=2, ls=\"dashed\")\n",
"ax.set_title(ndvi_filtered.isel(t=7).t.dt.date.to_numpy())\n",
"ax.set_aspect(\"auto\")"
]
],
"id": "b09851b79009b1f3"
},
{
"cell_type": "code",
Expand Down Expand Up @@ -299,7 +323,8 @@
"ax.imshow(ndvi_masked.isel(t=7), extent=[minx, maxx, miny, maxy])\n",
"ax.set_title(ndvi_masked.isel(t=7).t.dt.date.to_numpy())\n",
"ax.set_aspect(\"auto\")"
]
],
"id": "26f11754efa62e6b"
},
{
"cell_type": "code",
Expand Down Expand Up @@ -356,7 +381,8 @@
"ax.plot([], [], \"C0\", label=\"NDVI series of random pixels\")\n",
"ax.axvspan(None, None, color=\"C3\", alpha=0.5, label=\"Detected peak-valley events\")\n",
"ax.legend()"
]
],
"id": "1546c3e816acc487"
}
],
"metadata": {
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17 changes: 15 additions & 2 deletions notebooks/Local/FuseTS - Peak Valley Detection_holoviews.ipynb
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Expand Up @@ -1469,13 +1469,26 @@
"metadata": {},
"outputs": [],
"source": [
"scl = connection.load_collection(\n",
" \"SENTINEL2_L2A_SENTINELHUB\",\n",
" spatial_extent=spat_ext,\n",
" temporal_extent=temp_ext,\n",
" bands=[\"SCL\"],\n",
")\n",
"cloud_mask = scl.process(\n",
" \"to_scl_dilation_mask\",\n",
" data=scl,\n",
" kernel1_size=17, kernel2_size=77,\n",
" mask1_values=[2, 4, 5, 6, 7],\n",
" mask2_values=[3, 8, 9, 10, 11],\n",
" erosion_kernel_size=3)\n",
"s2 = connection.load_collection(\n",
" \"SENTINEL2_L2A_SENTINELHUB\",\n",
" spatial_extent=spat_ext,\n",
" temporal_extent=temp_ext,\n",
" bands=[\"B04\", \"B08\", \"SCL\"],\n",
" bands=[\"B04\", \"B08\"],\n",
")\n",
"s2 = s2.process(\"mask_scl_dilation\", data=s2, scl_band_name=\"SCL\")\n",
"s2 = s2.mask(cloud_mask)\n",
"ndvi_cube = s2.ndvi(red=\"B04\", nir=\"B08\", target_band=\"NDVI\")"
]
},
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15 changes: 13 additions & 2 deletions notebooks/Local/FuseTS - Phenology.ipynb
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Expand Up @@ -159,11 +159,22 @@
},
"outputs": [],
"source": [
"scl = connection.load_collection('SENTINEL2_L2A_SENTINELHUB',\n",
" spatial_extent=spat_ext,\n",
" temporal_extent=temp_ext,\n",
" bands=[\"SCL\"])\n",
"cloud_mask = scl.process(\n",
" \"to_scl_dilation_mask\",\n",
" data=scl,\n",
" kernel1_size=17, kernel2_size=77,\n",
" mask1_values=[2, 4, 5, 6, 7],\n",
" mask2_values=[3, 8, 9, 10, 11],\n",
" erosion_kernel_size=3)\n",
"s2 = connection.load_collection('SENTINEL2_L2A_SENTINELHUB',\n",
" spatial_extent=spat_ext,\n",
" temporal_extent=temp_ext,\n",
" bands=[\"B04\",\"B08\",\"SCL\"])\n",
"s2 = s2.process(\"mask_scl_dilation\", data=s2, scl_band_name=\"SCL\")\n",
" bands=[\"B04\",\"B08\"])\n",
"s2 = s2.mask(cloud_mask)\n",
"s2 = s2.mask_polygon(spat_ext)\n",
"ndvi_cube = s2.ndvi(red=\"B04\", nir=\"B08\", target_band='NDVI')"
]
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15 changes: 13 additions & 2 deletions notebooks/Local/FuseTS - Whittaker Smoothing.ipynb
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Expand Up @@ -857,11 +857,22 @@
},
"outputs": [],
"source": [
"scl = connection.load_collection('SENTINEL2_L2A_SENTINELHUB',\n",
" spatial_extent=spat_ext,\n",
" temporal_extent=temp_ext,\n",
" bands=[\"SCL\"])\n",
"cloud_mask = scl.process(\n",
" \"to_scl_dilation_mask\",\n",
" data=scl,\n",
" kernel1_size=17, kernel2_size=77,\n",
" mask1_values=[2, 4, 5, 6, 7],\n",
" mask2_values=[3, 8, 9, 10, 11],\n",
" erosion_kernel_size=3)\n",
"s2 = connection.load_collection('SENTINEL2_L2A_SENTINELHUB',\n",
" spatial_extent=spat_ext,\n",
" temporal_extent=temp_ext,\n",
" bands=[\"B02\", \"B03\", \"B04\",\"SCL\"])\n",
"s2 = s2.process(\"mask_scl_dilation\", data=s2, scl_band_name=\"SCL\")\n",
" bands=[\"B02\", \"B03\", \"B04\"])\n",
"s2 = s2.mask(cloud_mask)\n",
"\n",
"B02 = s2.band('B02')\n",
"B03 = s2.band('B03')\n",
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15 changes: 13 additions & 2 deletions notebooks/MOGPR Process Chain Demo.ipynb
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Expand Up @@ -89,11 +89,22 @@
"VV = s1_asc.band(\"VV\")\n",
"rvi_asc_cube = (VH + VH) / (VV + VH)\n",
"\n",
"scl = connection.load_collection('SENTINEL2_L2A_SENTINELHUB',\n",
" spatial_extent=spat_ext,\n",
" temporal_extent=temp_ext,\n",
" bands=[\"SCL\"])\n",
"cloud_mask = scl.process(\n",
" \"to_scl_dilation_mask\",\n",
" data=scl,\n",
" kernel1_size=17, kernel2_size=77,\n",
" mask1_values=[2, 4, 5, 6, 7],\n",
" mask2_values=[3, 8, 9, 10, 11],\n",
" erosion_kernel_size=3)\n",
"s2 = connection.load_collection('SENTINEL2_L2A_SENTINELHUB',\n",
" spatial_extent=spat_ext,\n",
" temporal_extent=temp_ext,\n",
" bands=[\"B04\",\"B08\",\"SCL\"])\n",
"s2 = s2.process(\"mask_scl_dilation\", data=s2, scl_band_name=\"SCL\")\n",
" bands=[\"B04\",\"B08\"])\n",
"s2 = s2.mask(cloud_mask)\n",
"ndvi_cube = s2.ndvi(red=\"B04\", nir=\"B08\")"
]
},
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19 changes: 15 additions & 4 deletions notebooks/OpenEO/FuseTS - MOGPR Multi Source Fusion.ipynb
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Expand Up @@ -67,8 +67,8 @@
"Requirement already satisfied: packaging>=21.3 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from xarray>=0.12.3->openeo) (23.1)\n",
"Requirement already satisfied: six>=1.5 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from python-dateutil>=2.8.2->pandas>0.20.0->openeo) (1.16.0)\n",
"\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip available: \u001B[0m\u001B[31;49m22.3.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.0\u001B[0m\n",
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\n"
]
}
],
Expand Down Expand Up @@ -279,11 +279,22 @@
"metadata": {},
"outputs": [],
"source": [
"scl = connection.load_collection('SENTINEL2_L2A',\n",
" spatial_extent=spat_ext,\n",
" temporal_extent=temp_ext,\n",
" bands=[\"SCL\"])\n",
"cloud_mask = scl.process(\n",
" \"to_scl_dilation_mask\",\n",
" data=scl,\n",
" kernel1_size=17, kernel2_size=77,\n",
" mask1_values=[2, 4, 5, 6, 7],\n",
" mask2_values=[3, 8, 9, 10, 11],\n",
" erosion_kernel_size=3)\n",
"base_s2 = connection.load_collection('SENTINEL2_L2A',\n",
" spatial_extent=spat_ext,\n",
" temporal_extent=temp_ext,\n",
" bands=[\"B04\", \"B08\", \"SCL\"])\n",
"base_s2 = base_s2.process(\"mask_scl_dilation\", data=base_s2, scl_band_name=\"SCL\")\n",
" bands=[\"B04\", \"B08\"])\n",
"base_s2 = base_s2.mask(cloud_mask)\n",
"base_s2 = base_s2.ndvi(red=\"B04\", nir=\"B08\", target_band='NDVI')\n",
"base_s2 = base_s2.filter_bands(bands=['NDVI'])\n",
"base_s2 = base_s2.mask_polygon(spat_ext)"
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15 changes: 13 additions & 2 deletions notebooks/OpenEO/FuseTS - MOGPR.ipynb
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Expand Up @@ -160,11 +160,22 @@
"metadata": {},
"outputs": [],
"source": [
"scl = connection.load_collection('SENTINEL2_L2A_SENTINELHUB',\n",
" spatial_extent=spat_ext,\n",
" temporal_extent=temp_ext,\n",
" bands=[\"SCL\"])\n",
"cloud_mask = scl.process(\n",
" \"to_scl_dilation_mask\",\n",
" data=scl,\n",
" kernel1_size=17, kernel2_size=77,\n",
" mask1_values=[2, 4, 5, 6, 7],\n",
" mask2_values=[3, 8, 9, 10, 11],\n",
" erosion_kernel_size=3)\n",
"base = connection.load_collection('SENTINEL2_L2A_SENTINELHUB',\n",
" spatial_extent=spat_ext,\n",
" temporal_extent=temp_ext,\n",
" bands=[\"B04\", \"B08\", \"SCL\"])\n",
"base_cloudmasked = base.process(\"mask_scl_dilation\", data=base, scl_band_name=\"SCL\")\n",
" bands=[\"B04\", \"B08\"])\n",
"base_cloudmasked = base.mask(cloud_mask)\n",
"base_ndvi = base_cloudmasked.ndvi(red=\"B04\", nir=\"B08\")"
]
},
Expand Down
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