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{
"cells": [
{
"cell_type": "markdown",
"id": "5fbc1dee",
"metadata": {
"id": "5fbc1dee"
},
"source": [
"# July 2021 floods: starter notebook\n",
"\n",
"This notebook does two things:\n",
"1. **Connects** you to the challenge data in the cloud. Nothing is downloaded, and no login is needed: the data is public.\n",
"2. **Shows what data is available** (EMS flood maps, EFAS flood hazard, GHS-OBAT buildings, GHS-POP population for DE, BE, NL).\n",
"\n",
"Click **Run all**. Works in Colab and on a laptop.\n",
"\n",
"<details>\n",
"<summary><b>Not using Colab? (VS Code, Jupyter, any Python)</b></summary>\n",
"\n",
"1. `pip install gcsfs pyogrio rasterio geopandas shapely ipykernel`\n",
" (on Windows, if that fails: `conda install -c conda-forge geopandas rasterio pyogrio gcsfs`)\n",
"2. VS Code: install the Python and Jupyter extensions and pick your environment as kernel. If the packages are already installed there, you can skip the `pip install` cell below. After installing, restart the kernel.\n",
"\n",
"**Problems:** *connection or timeout errors* → check your internet connection and retry · *`No such file` on a path* → run the Explore cell to see the exact file names · *`ModuleNotFoundError`* → the kernel and your terminal use different Pythons: install with `%pip install gcsfs` inside the notebook, then restart the kernel.\n",
"\n",
"</details>"
]
},
{
"cell_type": "markdown",
"id": "1fa4f5c8",
"metadata": {
"id": "1fa4f5c8"
},
"source": [
"## 1. Setup"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "2f1a568b",
"metadata": {
"id": "2f1a568b"
},
"outputs": [],
"source": [
"%%capture\n",
"%pip install -q gcsfs pyogrio rasterio geopandas shapely"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "3a6951de",
"metadata": {
"id": "3a6951de"
},
"outputs": [],
"source": [
"import os, re, warnings\n",
"from pathlib import Path\n",
"import pandas as pd\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"BUCKET = \"datasets_challenge\"\n",
"FOLDER = \"working_data\""
]
},
{
"cell_type": "markdown",
"id": "7e4db53a",
"metadata": {
"id": "7e4db53a"
},
"source": [
"## 2. Connect"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "96bc9f92",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "96bc9f92",
"outputId": "54d6c138-933d-488e-9848-91d90dd95bbd"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Connected: ['cems-efas', 'ems_be', 'ems_de', 'ems_nl', 'ghs_pop', 'obat_be', 'obat_de', 'obat_nl']\n"
]
}
],
"source": [
"import gcsfs\n",
"\n",
"# The bucket is public: no login needed.\n",
"os.environ[\"GS_NO_SIGN_REQUEST\"] = \"YES\" # lets GDAL (rasterio, pyogrio) read it anonymously\n",
"GDAL_PREFIX = \"/vsigs/\"\n",
"ROOT = f\"{BUCKET}/{FOLDER}\"\n",
"fs = gcsfs.GCSFileSystem(token=\"anon\")\n",
"print(\"Connected:\", [Path(p).name for p in fs.ls(ROOT)])"
]
},
{
"cell_type": "markdown",
"id": "ce75ad49",
"metadata": {
"id": "ce75ad49"
},
"source": [
"## 3. Explore"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "3b82f013",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "3b82f013",
"outputId": "2bd3a22b-f62c-4c53-ed05-cc737b3377aa"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"cems-efas\n",
" Europe_RP100_filled_depth.tif 323 MB\n",
" Europe_RP10_filled_depth.tif 272 MB\n",
" Europe_RP500_filled_depth.tif 350 MB\n",
"\n",
"ghs_pop\n",
" GHS_POP_E2020_GLOBE_R2023A_4326_30ss_V1_0.tif 383 MB\n",
" GHS_POP_E2025_GLOBE_R2023A_4326_30ss_V1_0.tif 384 MB\n",
"\n",
"obat_be\n",
" GHS_OBAT_GPKG_BEL_E2020_R2024A_V1_0.gpkg 1,433 MB\n",
"\n",
"obat_de\n",
" GHS_OBAT_GPKG_DEU_E2020_R2024A_V1_0.gpkg 8,579 MB\n",
"\n",
"obat_nl\n",
" GHS_OBAT_GPKG_NLD_E2020_R2024A_V1_0.gpkg 2,229 MB\n",
"\n",
"ems_be\n",
" EMSR518_AOI01_DEL_MONIT01_r1_VECTORS_v1_vector 9 shp, 65.4 MB\n",
" EMSR518_AOI01_DEL_PRODUCT_r1_VECTORS_v1_vector 10 shp, 76.3 MB\n",
" EMSR518_AOI02_DEL_MONIT01_r1_VECTORS_v1_vector 12 shp, 110.3 MB\n",
" EMSR518_AOI02_DEL_PRODUCT_r1_VECTORS_v1_vector 10 shp, 138.3 MB\n",
"\n",
"ems_de\n",
" EMSR517_AOI01_DEL_MONIT01_r1_RTP01_v1_vector 11 shp, 21.3 MB\n",
" EMSR517_AOI01_DEL_PRODUCT_r1_RTP01_v1_vector 10 shp, 21.3 MB\n",
" EMSR517_AOI03_DEL_MONIT01_r1_RTP01_v1_vector 10 shp, 6.9 MB\n",
" EMSR517_AOI03_DEL_PRODUCT_r1_RTP01_v1_vector 10 shp, 5.2 MB\n",
" EMSR517_AOI04_DEL_MONIT01_r1_RTP01_v1_vector 11 shp, 22.4 MB\n",
" EMSR517_AOI04_DEL_PRODUCT_r1_RTP01_v1_vector 10 shp, 26.6 MB\n",
" EMSR517_AOI05_DEL_MONIT01_r1_RTP01_v1_vector 10 shp, 102.6 MB\n",
" EMSR517_AOI05_DEL_PRODUCT_r1_RTP01_v1_vector 10 shp, 69.2 MB\n",
" EMSR517_AOI06_DEL_MONIT01_r1_RTP01_v1_vector 10 shp, 78.7 MB\n",
" EMSR517_AOI06_DEL_PRODUCT_r1_RTP01_v1_vector 10 shp, 77.2 MB\n",
" EMSR517_AOI07_GRA_PRODUCT_r1_RTP01_v1_vector 9 shp, 6.6 MB\n",
" EMSR517_AOI08_GRA_PRODUCT_r1_RTP01_v1_vector 9 shp, 5.5 MB\n",
" EMSR517_AOI09_GRA_PRODUCT_r1_RTP01_v1_vector 10 shp, 11.8 MB\n",
" EMSR517_AOI10_GRA_PRODUCT_r1_RTP01_v1_vector 10 shp, 4.3 MB\n",
" EMSR517_AOI11_GRA_PRODUCT_r1_RTP01_v1_vector 10 shp, 7.6 MB\n",
" EMSR517_AOI14_GRA_PRODUCT_r1_RTP01_v1_vector 9 shp, 13.4 MB\n",
" EMSR517_AOI15_GRA_MONIT01_r1_RTP01_v3_vector 9 shp, 25.6 MB\n",
" EMSR517_AOI15_GRA_PRODUCT_r1_RTP01_v1_vector 8 shp, 12.9 MB\n",
" EMSR517_AOI16_GRA_PRODUCT_r1_RTP01_v1_vector 9 shp, 4.3 MB\n",
" EMSR517_AOI17_GRA_PRODUCT_r1_RTP01_v2_vector 9 shp, 7.3 MB\n",
" EMSR517_AOI18_GRA_PRODUCT_r1_RTP01_v1_vector 7 shp, 2.9 MB\n",
" EMSR517_AOI20_GRA_PRODUCT_r1_RTP01_v1_vector 8 shp, 2.1 MB\n",
"\n",
"ems_nl\n",
" EMSR520_AOI01_DEL_PRODUCT_r1_RTP01_v1_vector 10 shp, 59.8 MB\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
" layer\n",
"cc aoi ptype ver \n",
"be AOI01 DEL MONIT01 areaOfInterestA, builtUpA, facilitiesL, hydrog...\n",
" PRODUCT areaOfInterestA, builtUpA, facilitiesL, hydrog...\n",
" AOI02 DEL MONIT01 areaOfInterestA, builtUpA, facilitiesA, facili...\n",
" PRODUCT areaOfInterestA, facilitiesL, hydrographyA, hy...\n",
"de AOI01 DEL MONIT01 areaOfInterestA, builtUpA, facilitiesA, facili...\n",
" PRODUCT areaOfInterestA, builtUpA, facilitiesL, hydrog...\n",
" AOI03 DEL MONIT01 areaOfInterestA, builtUpA, facilitiesA, facili...\n",
" PRODUCT areaOfInterestA, builtUpA, facilitiesA, facili...\n",
" AOI04 DEL MONIT01 areaOfInterestA, builtUpA, facilitiesA, facili...\n",
" PRODUCT areaOfInterestA, builtUpA, facilitiesL, hydrog...\n",
" AOI05 DEL MONIT01 areaOfInterestA, builtUpA, facilitiesL, hydrog...\n",
" PRODUCT areaOfInterestA, builtUpA, facilitiesL, hydrog...\n",
" AOI06 DEL MONIT01 areaOfInterestA, builtUpA, facilitiesL, hydrog...\n",
" PRODUCT areaOfInterestA, builtUpA, facilitiesL, hydrog...\n",
" AOI07 GRA PRODUCT areaOfInterestA, builtUpP, facilitiesA, hydrog...\n",
" AOI08 GRA PRODUCT areaOfInterestA, builtUpP, facilitiesA, hydrog...\n",
" AOI09 GRA PRODUCT areaOfInterestA, builtUpP, facilitiesA, hydrog...\n",
" AOI10 GRA PRODUCT areaOfInterestA, builtUpP, facilitiesA, facili...\n",
" AOI11 GRA PRODUCT areaOfInterestA, builtUpP, facilitiesA, hydrog...\n",
" AOI14 GRA PRODUCT areaOfInterestA, builtUpP, hydrographyA, hydro...\n",
" AOI15 GRA MONIT01 areaOfInterestA, builtUpP, facilitiesA, hydrog...\n",
" PRODUCT areaOfInterestA, builtUpP, facilitiesA, hydrog...\n",
" AOI16 GRA PRODUCT areaOfInterestA, builtUpP, facilitiesA, hydrog...\n",
" AOI17 GRA PRODUCT areaOfInterestA, builtUpP, facilitiesA, hydrog...\n",
" AOI18 GRA PRODUCT areaOfInterestA, builtUpP, facilitiesA, hydrog...\n",
" AOI20 GRA PRODUCT areaOfInterestA, facilitiesA, hydrographyA, hy...\n",
"nl AOI01 DEL PRODUCT areaOfInterestA, builtUpA, facilitiesA, hydrog..."
],
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" <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
" </svg>\n",
" </button>\n",
"\n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" .colab-df-buttons div {\n",
" margin-bottom: 4px;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-e5288fce-be8a-4a96-90cb-77edf772a66f button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-e5288fce-be8a-4a96-90cb-77edf772a66f');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
"\n",
"\n",
" </div>\n",
" </div>\n"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"summary": "{\n \"name\": \"display(pd\",\n \"rows\": 27,\n \"fields\": [\n {\n \"column\": \"layer\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 17,\n \"samples\": [\n \"areaOfInterestA, builtUpA, facilitiesL, hydrographyA, hydrographyL, imageFootprintA, naturalLandUseA, observedEventA, transportationL\",\n \"areaOfInterestA, builtUpA, facilitiesL, hydrographyA, hydrographyL, imageFootprintA, naturalLandUseA, observedEventA, physiographyL, transportationL\",\n \"areaOfInterestA, builtUpA, facilitiesL, hydrographyA, hydrographyL, imageFootprintA, naturalLandUseA, observedEventA, transportationL, transportationP\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {}
}
],
"source": [
"for folder in [\"cems-efas\", \"ghs_pop\", \"obat_be\", \"obat_de\", \"obat_nl\", \"ems_be\", \"ems_de\", \"ems_nl\"]:\n",
" print(f\"\\n{folder}\")\n",
" files = fs.find(f\"{ROOT}/{folder}\", detail=True)\n",
" if folder.startswith(\"ems_\"):\n",
" # vector products: one line per product directory\n",
" dirs = {}\n",
" for p, info in sorted(files.items()):\n",
" d = dirs.setdefault(Path(p).parent.name, [0, 0])\n",
" d[0] += p.lower().endswith(\".shp\"); d[1] += int(info[\"size\"])\n",
" for name, (n_shp, size) in dirs.items(): print(\" \", name, f\"{n_shp} shp, {size/1e6:,.1f} MB\")\n",
" else:\n",
" for p, info in sorted(files.items()):\n",
" if p.lower().endswith((\".tif\", \".tiff\", \".gpkg\")): print(\" \", Path(p).name, f\"{int(info['size'])/1e6:,.0f} MB\")\n",
"\n",
"# EMS: one row per area (aoi), type (DEL/GRA) and version, with its layers\n",
"EMS_RE = re.compile(r\"(EMSR\\d+)_(AOI\\d+)_([A-Z]+)_(PRODUCT|MONIT\\d*)_(\\w+?)_r\\d+_v\\d+\", re.I)\n",
"rows = []\n",
"for cc in (\"de\", \"be\", \"nl\"):\n",
" for p in fs.find(f\"{ROOT}/ems_{cc}\"):\n",
" m = EMS_RE.search(Path(p).name) if p.lower().endswith(\".shp\") else None\n",
" if m: rows.append(dict(cc=cc, aoi=m[2], ptype=m[3].upper(), ver=m[4].upper(), layer=m[5]))\n",
"display(pd.DataFrame(rows).groupby([\"cc\", \"aoi\", \"ptype\", \"ver\"]).layer.apply(lambda s: \", \".join(sorted(s))).to_frame())"
]
},
{
"cell_type": "markdown",
"id": "c726ab00",
"metadata": {
"id": "c726ab00"
},
"source": [
"## 4. Your analysis\n",
"Read data straight from the bucket, no login needed: list files with `fs` (gcsfs), or open them with GDAL-based libraries using the `GDAL_PREFIX` path prefix, for example `rasterio.open(f\"{GDAL_PREFIX}{path}\")`. The bucket is read-only, so save results to the runtime disk and download them from the file browser."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "d08bb833",
"metadata": {
"id": "d08bb833"
},
"outputs": [],
"source": [
"# your code"
]
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|
