diff --git a/README.md b/README.md index d920559..785b196 100644 --- a/README.md +++ b/README.md @@ -34,6 +34,7 @@ fimeval/ ├── src/ │ └── fimeval/ │ ├──BuildingFootprint/ # Contains the evaluation of model predicted FIM with microsoft building footprint +│ │ └── microsoftBF.py │ │ └── evaluationwithBF.py │ └── ContingencyMap/ # Contains all the metrics calculation and contingency map generation │ │ ├── evaluationFIM.py # main evaluation moodule @@ -59,7 +60,8 @@ This framework is published as a python package in PyPI (https://pypi.org/projec ```bash #Install to use this framework -pip install fimeval +pip install uv #Makes the downloading much faster +uv pip install fimeval #Use this framework in your workflows using poetry poetry add fimeval @@ -132,7 +134,7 @@ Table 1: Modules in `fimeval` are in order of execution. | `EvaluateFIM` | It runs all the evaluation of FIM between B-FIM and M-FIMs. | `main_dir`: Main directory containing the case study folders,
`method_name`: How users wants to evaluate their FIM,
`outpur_dir`: Output directory where all the results and the intermidiate files will be saved for further calculation,
*`PWB_dir`*: The permanenet water bodies vectory file directory if user wants to user their own boundary,
*`target_crs`*: this fimeval framework needs the floodmaps to be in projected CRS so define the projected CRS in epsg code format,
*`target_resolution`*: sometime if the benchmark is very high resolution than candidate FIMs, it needs heavy computational time, so user can define the resolution if there FIMs are in different spatial resolution, else it will use the coarser resolution among all FIMS within that case. |The outputs includes generated files in TIFF, SHP, CSV, and PNG formats, all stored within the output folder. Users can visualize the TIFF files using any geospatial platform. The TIFF files consist of the binary Benchmark-FIM (Benchmark.tif), Model-FIM (Candidate.tif), and Agreement-FIM (Contingency.tif). The shp files contain the boundary of the generated flood extent.| | `PlotContingencyMap` | For better understanding, It will print the agreement maps derived in first step. | `main_dir`, `method_name`, `output_dir` : Based on the those arguments, once all the evaluation is done, it will dynamically get the corresponding contingency raster for printing.| This prints the contingency map showing different class of evaluation (TP, FP, no data, PWB etc). The outputs look like- Figure 4 first row.| | `PlotEvaluationMetrics` | For quick understanding of the evaluation metrics, to plot bar of evaluation scores. | `main_dir`, `method_name`, `output_dir` : Based on the those arguments, once all the evaluation is done, it will dynamically get the corresponding file for printing based on all those info.| This prints the bar plots which includes different performance metrics calculated by EvaluateFIM module. The outputs look like- Figure 4 second row.| -| `EvaluationWithBuildingFootprint` | For Building Footprint Analysis, user can specify shapefile of building footprints as .shp or .gpkg format. By default it consider global Microsoft building footprint dataset. Those data are hosted in Google Earth Engine (GEE) so, It pops up to authenticate the GEE account, please allow it and it will download the data based on evaluation boundary and evaluation is done. | `main_dir`, `method_name`, `output_dir`: Those arguments are as it is, same as all other modules.
*`building_footprint`*: If user wants to use their own building footprint file then pass the directory here, *`country`*: It is the 3 letter based country ISO code (eg. 'USA', NEP' etc), for the building data automation using GEE based on the evaluation extent, *`shapefile_dir`*: this is the directory of user defined AOI if user is working with their own boundary and automatic Building footprint download and evaluation. | It will calculate the different metrics (e.g. TP, FP, CSI, F1, Accuracy etc) based on hit and miss of building on different M-FIM and B-FIM. Those all metrics will be saved as CSV format in `output_dir` and finally using that info it prints the counts of building foorpint in each FIMs as well as scenario on the evaluation end via bar plot.| +| `EvaluationWithBuildingFootprint` | For Building Footprint Analysis, user can specify shapefile of building footprints as .shp or .gpkg format. By default it consider global Microsoft building footprint dataset. Those data are hosted in Google Earth Engine (GEE) so, It pops up to authenticate the GEE account, please allow it and it will download the data based on evaluation boundary and evaluation is done. | `main_dir`, `method_name`, `output_dir`: Those arguments are as it is, same as all other modules.
*`building_footprint`*: If user wants to use their own building footprint file then pass the directory here, *`country`*: It is the 3 letter based country ISO code (eg. 'USA', NEP' etc), for the building data automation using GEE based on the evaluation extent, *`shapefile_dir`*: this is the directory of user defined AOI if user is working with their own boundary and automatic Building footprint download and evaluation, *`geeprojectID`*: this is the google earth engine google cloud project ID, which helps to access the GEE data and resources to work with building footprint download and process. | It will calculate the different metrics (e.g. TP, FP, CSI, F1, Accuracy etc) based on hit and miss of building on different M-FIM and B-FIM. Those all metrics will be saved as CSV format in `output_dir` and finally using that info it prints the counts of building foorpint in each FIMs as well as scenario on the evaluation end via bar plot.|

@@ -140,9 +142,9 @@ Table 1: Modules in `fimeval` are in order of execution. Figure 4: Combined raw output from framework for different two method. First row (subplot a and b) and second row (subplot c and d) is contingency maps and evaluation metrics of FIM derived using `PrintContingencyMaP` and `PlotEvaluationMetrics` module. Third row (subplot e and f) is the output after processing and calculating of evaluation with BF by unsing `EvaluateWithBuildingFoorprint` module. -## 🔧 Installation Instructions +## Installation Instructions -### 1. ✅ Prerequisites +### 1. Prerequisites Before installing `fimeval`, ensure the following software are installed: @@ -162,28 +164,24 @@ If Anaconda is not installed, download and install it from the [official website --- -### 3. 🌐 Set Up Virtual Environment +### 3. Set Up Virtual Environment -#### 💻 For Mac Users +#### For Mac Users Open **Terminal** and run: ```bash - # Create a new environment named 'fimeval' conda create --name fimeval python=3.10 # Activate the environment conda activate fimeval -# Install Jupyter Notebook -pip install notebook - # Install fimeval package -pip install fimeval - +pip install uv +uv pip install fimeval ``` -### ☁️ Google Colab Version +### Google Colab Version To use fimeval in Google Colab, follow the steps below: @@ -196,7 +194,6 @@ In a new Colab notebook, mount the Google Drive ```bash pip install fimeval ``` - ### **Acknowledgements** | | | | --- | --- | diff --git a/dist/fimeval-0.1.43-py3-none-any.whl b/dist/fimeval-0.1.43-py3-none-any.whl deleted file mode 100644 index 93ca0c8..0000000 Binary files a/dist/fimeval-0.1.43-py3-none-any.whl and /dev/null differ diff --git a/dist/fimeval-0.1.43.tar.gz b/dist/fimeval-0.1.43.tar.gz deleted file mode 100644 index 97e1cc4..0000000 Binary files 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index ff001d9..0014fb4 100644 --- a/docs/fimeval_usage.ipynb +++ b/docs/fimeval_usage.ipynb @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -206,7 +206,10 @@ } ], "source": [ - "%pip install fimpef" + "#If not installed within terminal\n", + "%pip install uv\n", + "#then\n", + "%uv pip install fimeval" ] }, { @@ -252,12 +255,21 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/supath/Downloads/MSResearch/FIMpef/fimpef/.venv/lib/python3.11/site-packages/geemap/conversion.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " import pkg_resources\n" + ] + } + ], "source": [ "#Import the package\n", - "import fimpef as fp\n", + "import fimeval as fp\n", "from pathlib import Path #To handle file path effectively" ] }, @@ -419,7 +431,7 @@ "fp.EvaluationWithBuildingFootprint(main_dir, method_name, output_dir, building_footprint = building_footprint)\n", "\n", "#Sometimes user is working with their own AOI, then they need to pass that shapefile as well\n", - "fp.EvaluationWithBuildingFootprint(main_dir, method_name, output_dir, building_footprint = building_footprint, shapefile_dir= AOI)" + "# fp.EvaluationWithBuildingFootprint(main_dir, method_name, output_dir, building_footprint = building_footprint, shapefile_dir= AOI)" ] }, { @@ -431,8 +443,10 @@ "# FIM Evaluation with Building Footprint (by default, it uses the Microsoft Building Footprint dataset)\n", "#3 letter country ISO code\n", "countryISO = \"USA\" #For instance\n", + "geeprojectID = \"your_gee_project_id\" # Replace with your actual GEE cloud project ID\n", + "\n", "fp.EvaluationWithBuildingFootprint(\n", - 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"sha256:a3629b04e3edb893212df862038c7232f62973373869db5084aed739b437b5af"}, -] - -[[package]] -name = "xyzservices" -version = "2025.4.0" -description = "Source of XYZ tiles providers" -optional = false -python-versions = ">=3.8" -files = [ - {file = "xyzservices-2025.4.0-py3-none-any.whl", hash = "sha256:8d4db9a59213ccb4ce1cf70210584f30b10795bff47627cdfb862b39ff6e10c9"}, - {file = "xyzservices-2025.4.0.tar.gz", hash = "sha256:6fe764713648fac53450fbc61a3c366cb6ae5335a1b2ae0c3796b495de3709d8"}, -] - -[metadata] -lock-version = "2.0" -python-versions = "^3.10" -content-hash = "1a87b9093835897bee7d8a2b876b71ad809f93e8c450fdcdcfa40674ef39d092" diff --git a/pyproject.toml b/pyproject.toml index c5a796a..fb37d8c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,37 +1,49 @@ -[tool.poetry] +[project] name = "fimeval" -version = "0.1.52" +version = "0.1.53" description = "A Framework for Automatic Evaluation of Flood Inundation Mapping Predictions Evaluation" +readme = "README.md" +requires-python = ">=3.10" +license = { file = "LICENSE" } + authors = [ - "Surface Dynamics Modeling Lab", - "Supath Dhital ", - "Dipshika Devi " + { name = "Surface Dynamics Modeling Lab" }, + { name = "Supath Dhital", email = "sdhital@crimson.ua.edu" }, + { name = "Dipshika Devi", email = "ddevi@ua.edu" } ] + maintainers = [ - "Supath Dhital ", - "Dipshika Devi " + { name = "Supath Dhital", email = "sdhital@crimson.ua.edu" }, + { name = "Dipshika Devi", email = "ddevi@ua.edu" } +] + +dependencies = [ + "rasterio>=1.4.2,<2.0.0", + "numpy>=2", + "geopandas>=1.0.1,<2.0.0", + "shapely>=2.0.6,<3.0.0", + "matplotlib>=3.9.2,<4.0.0", + "plotly>=5.24.1,<6.0.0", + "kaleido==0.2.1", + "nbformat>=5.10.4,<6.0.0", + "pyproj>=3.7.0,<4.0.0", + "notebook>=7.3.2,<8.0.0", + "boto3>=1.36.16,<2.0.0", + "geemap" +] + +[project.optional-dependencies] +dev = [ + "pytest", + "black" ] -license = "GPLv3" -readme = "README.md" +[tool.setuptools] +package-dir = { "" = "src" } -[tool.poetry.dependencies] -python = "^3.10" -pytest = "^8.3.3" -rasterio = "^1.4.2" -numpy = "<2" -geopandas = "^1.0.1" -shapely = "^2.0.6" -pathlib = "^1.0.1" -matplotlib = "^3.9.2" -plotly = "^5.24.1" -kaleido = "0.2.1" -nbformat = "^5.10.4" -pyproj = "^3.7.0" -notebook = "^7.3.2" -msfootprint = "^0.1.27" -boto3 = "^1.36.16" +[tool.setuptools.packages.find] +where = ["src"] [build-system] -requires = ["poetry-core"] -build-backend = "poetry.core.masonry.api" +requires = ["setuptools>=61.0"] +build-backend = "setuptools.build_meta" diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..d447b3e --- /dev/null +++ b/requirements.txt @@ -0,0 +1,524 @@ +# This file was autogenerated by uv via the following command: +# uv pip compile pyproject.toml -o requirements.txt +affine==2.4.0 + # via rasterio +anyio==4.10.0 + # via + # httpx + # jupyter-server +anywidget==0.9.18 + # via geemap +appnope==0.1.4 + # via ipykernel +argon2-cffi==25.1.0 + # via jupyter-server +argon2-cffi-bindings==25.1.0 + # via argon2-cffi +arrow==1.3.0 + # via isoduration +asttokens==3.0.0 + # via stack-data +async-lru==2.0.5 + # via jupyterlab +attrs==25.3.0 + # via + # jsonschema + # rasterio + # referencing +babel==2.17.0 + # via jupyterlab-server +beautifulsoup4==4.13.4 + # via nbconvert +bleach==6.2.0 + # via nbconvert +boto3==1.40.11 + # via fimeval (pyproject.toml) +botocore==1.40.11 + # via + # boto3 + # s3transfer +bqplot==0.12.45 + # via geemap +branca==0.8.1 + # via + # folium + # ipyleaflet +cachetools==5.5.2 + # via google-auth +certifi==2025.8.3 + # via + # httpcore + # httpx + # pyogrio + # pyproj + # rasterio + # requests +cffi==1.17.1 + # via argon2-cffi-bindings +charset-normalizer==3.4.3 + # via requests +click==8.2.1 + # via + # click-plugins + # cligj + # geocoder + # rasterio +click-plugins==1.1.1.2 + # via rasterio +cligj==0.7.2 + # via rasterio +comm==0.2.3 + # via + # ipykernel + # ipywidgets +contourpy==1.3.3 + # via matplotlib +cycler==0.12.1 + # via matplotlib +debugpy==1.8.16 + # via ipykernel +decorator==5.2.1 + # via + # ipython + # ratelim +defusedxml==0.7.1 + # via nbconvert +earthengine-api==1.6.3 + # via + # eerepr + # geemap +eerepr==0.1.2 + # via geemap +executing==2.2.0 + # via stack-data +fastjsonschema==2.21.2 + # via nbformat +folium==0.20.0 + # via geemap +fonttools==4.59.1 + # via matplotlib +fqdn==1.5.1 + # via jsonschema +future==1.0.0 + # via geocoder +geemap==0.36.1 + # via fimeval (pyproject.toml) +geocoder==1.38.1 + # via geemap +geopandas==1.1.1 + # via fimeval (pyproject.toml) +google-api-core==2.25.1 + # via + # google-api-python-client + # google-cloud-core + # google-cloud-storage +google-api-python-client==2.179.0 + # via earthengine-api +google-auth==2.40.3 + # via + # earthengine-api + # google-api-core + # google-api-python-client + # google-auth-httplib2 + # google-cloud-core + # google-cloud-storage +google-auth-httplib2==0.2.0 + # via + # earthengine-api + # google-api-python-client +google-cloud-core==2.4.3 + # via google-cloud-storage +google-cloud-storage==3.3.0 + # via earthengine-api +google-crc32c==1.7.1 + # via + # google-cloud-storage + # google-resumable-media +google-resumable-media==2.7.2 + # via google-cloud-storage +googleapis-common-protos==1.70.0 + # via google-api-core +h11==0.16.0 + # via httpcore +httpcore==1.0.9 + # via httpx +httplib2==0.22.0 + # via + # earthengine-api + # google-api-python-client + # google-auth-httplib2 +httpx==0.28.1 + # via jupyterlab +idna==3.10 + # via + # anyio + # httpx + # jsonschema + # requests +ipyevents==2.0.2 + # via geemap +ipyfilechooser==0.6.0 + # via geemap +ipykernel==6.30.1 + # via jupyterlab +ipyleaflet==0.20.0 + # via geemap +ipython==9.4.0 + # via + # ipykernel + # ipywidgets +ipython-pygments-lexers==1.1.1 + # via ipython +ipytree==0.2.2 + # via geemap +ipywidgets==8.1.7 + # via + # anywidget + # bqplot + # ipyevents + # ipyfilechooser + # ipyleaflet + # ipytree +isoduration==20.11.0 + # via jsonschema +jedi==0.19.2 + # via ipython +jinja2==3.1.6 + # via + # branca + # folium + # jupyter-server + # jupyterlab + # jupyterlab-server + # nbconvert +jmespath==1.0.1 + # via + # boto3 + # botocore +json5==0.12.1 + # via jupyterlab-server +jsonpointer==3.0.0 + # via jsonschema +jsonschema==4.25.0 + # via + # jupyter-events + # jupyterlab-server + # nbformat +jsonschema-specifications==2025.4.1 + # via jsonschema +jupyter-client==8.6.3 + # via + # ipykernel + # jupyter-server + # nbclient +jupyter-core==5.8.1 + # via + # ipykernel + # jupyter-client + # jupyter-server + # jupyterlab + # nbclient + # nbconvert + # nbformat +jupyter-events==0.12.0 + # via jupyter-server +jupyter-leaflet==0.20.0 + # via ipyleaflet +jupyter-lsp==2.2.6 + # via jupyterlab +jupyter-server==2.16.0 + # via + # jupyter-lsp + # jupyterlab + # jupyterlab-server + # notebook + # notebook-shim +jupyter-server-terminals==0.5.3 + # via jupyter-server +jupyterlab==4.4.6 + # via notebook +jupyterlab-pygments==0.3.0 + # via nbconvert +jupyterlab-server==2.27.3 + # via + # jupyterlab + # notebook +jupyterlab-widgets==3.0.15 + # via ipywidgets +kaleido==0.2.1 + # via fimeval (pyproject.toml) +kiwisolver==1.4.9 + # via matplotlib +lark==1.2.2 + # via rfc3987-syntax +markupsafe==3.0.2 + # via + # jinja2 + # nbconvert +matplotlib==3.10.5 + # via + # fimeval (pyproject.toml) + # geemap +matplotlib-inline==0.1.7 + # via + # ipykernel + # ipython +mistune==3.1.3 + # via nbconvert +nbclient==0.10.2 + # via nbconvert +nbconvert==7.16.6 + # via jupyter-server +nbformat==5.10.4 + # via + # fimeval (pyproject.toml) + # jupyter-server + # nbclient + # nbconvert +nest-asyncio==1.6.0 + # via ipykernel +notebook==7.4.5 + # via fimeval (pyproject.toml) +notebook-shim==0.2.4 + # via + # jupyterlab + # notebook +numpy==2.3.2 + # via + # fimeval (pyproject.toml) + # bqplot + # contourpy + # folium + # geemap + # geopandas + # matplotlib + # pandas + # pyogrio + # rasterio + # shapely +overrides==7.7.0 + # via jupyter-server +packaging==25.0 + # via + # geopandas + # ipykernel + # jupyter-events + # jupyter-server + # jupyterlab + # jupyterlab-server + # matplotlib + # nbconvert + # plotly + # pyogrio +pandas==2.3.1 + # via + # bqplot + # geemap + # geopandas +pandocfilters==1.5.1 + # via nbconvert +parso==0.8.4 + # via jedi +pexpect==4.9.0 + # via ipython +pillow==11.3.0 + # via matplotlib +platformdirs==4.3.8 + # via jupyter-core +plotly==5.24.1 + # via + # fimeval (pyproject.toml) + # geemap +prometheus-client==0.22.1 + # via jupyter-server +prompt-toolkit==3.0.51 + # via ipython +proto-plus==1.26.1 + # via google-api-core +protobuf==6.32.0 + # via + # google-api-core + # googleapis-common-protos + # proto-plus +psutil==7.0.0 + # via ipykernel +psygnal==0.14.1 + # via anywidget +ptyprocess==0.7.0 + # via + # pexpect + # terminado +pure-eval==0.2.3 + # via stack-data +pyasn1==0.6.1 + # via + # pyasn1-modules + # rsa +pyasn1-modules==0.4.2 + # via google-auth +pycparser==2.22 + # via cffi +pygments==2.19.2 + # via + # ipython + # ipython-pygments-lexers + # nbconvert +pyogrio==0.11.1 + # via geopandas +pyparsing==3.2.3 + # via + # httplib2 + # matplotlib + # rasterio +pyperclip==1.9.0 + # via geemap +pyproj==3.7.2 + # via + # fimeval (pyproject.toml) + # geopandas +pyshp==2.3.1 + # via geemap +python-box==7.3.2 + # via geemap +python-dateutil==2.9.0.post0 + # via + # arrow + # botocore + # jupyter-client + # matplotlib + # pandas +python-json-logger==3.3.0 + # via jupyter-events +pytz==2025.2 + # via pandas +pyyaml==6.0.2 + # via jupyter-events +pyzmq==27.0.1 + # via + # ipykernel + # jupyter-client + # jupyter-server +rasterio==1.4.3 + # via fimeval (pyproject.toml) +ratelim==0.1.6 + # via geocoder +referencing==0.36.2 + # via + # jsonschema + # jsonschema-specifications + # jupyter-events +requests==2.32.4 + # via + # earthengine-api + # folium + # geocoder + # google-api-core + # google-cloud-storage + # jupyterlab-server +rfc3339-validator==0.1.4 + # via + # jsonschema + # jupyter-events +rfc3986-validator==0.1.1 + # via + # jsonschema + # jupyter-events +rfc3987-syntax==1.1.0 + # via jsonschema +rpds-py==0.27.0 + # via + # jsonschema + # referencing +rsa==4.9.1 + # via google-auth +s3transfer==0.13.1 + # via boto3 +scooby==0.10.1 + # via geemap +send2trash==1.8.3 + # via jupyter-server +setuptools==80.9.0 + # via jupyterlab +shapely==2.1.1 + # via + # fimeval (pyproject.toml) + # geopandas +six==1.17.0 + # via + # geocoder + # python-dateutil + # rfc3339-validator +sniffio==1.3.1 + # via anyio +soupsieve==2.7 + # via beautifulsoup4 +stack-data==0.6.3 + # via ipython +tenacity==9.1.2 + # via plotly +terminado==0.18.1 + # via + # jupyter-server + # jupyter-server-terminals +tinycss2==1.4.0 + # via bleach +tornado==6.5.2 + # via + # ipykernel + # jupyter-client + # jupyter-server + # jupyterlab + # notebook + # terminado +traitlets==5.14.3 + # via + # bqplot + # ipykernel + # ipython + # ipywidgets + # jupyter-client + # jupyter-core + # jupyter-events + # jupyter-server + # jupyterlab + # matplotlib-inline + # nbclient + # nbconvert + # nbformat + # traittypes +traittypes==0.2.1 + # via + # bqplot + # ipyleaflet +types-python-dateutil==2.9.0.20250809 + # via arrow +typing-extensions==4.14.1 + # via + # anywidget + # beautifulsoup4 +tzdata==2025.2 + # via pandas +uri-template==1.3.0 + # via jsonschema +uritemplate==4.2.0 + # via google-api-python-client +urllib3==2.5.0 + # via + # botocore + # requests +wcwidth==0.2.13 + # via prompt-toolkit +webcolors==24.11.1 + # via jsonschema +webencodings==0.5.1 + # via + # bleach + # tinycss2 +websocket-client==1.8.0 + # via jupyter-server +widgetsnbextension==4.0.14 + # via ipywidgets +xyzservices==2025.4.0 + # via + # folium + # ipyleaflet diff --git a/src/fimeval.egg-info/PKG-INFO b/src/fimeval.egg-info/PKG-INFO new file mode 100644 index 0000000..dccc0ea --- /dev/null +++ b/src/fimeval.egg-info/PKG-INFO @@ -0,0 +1,233 @@ +Metadata-Version: 2.4 +Name: fimeval +Version: 0.1.53 +Summary: A Framework for Automatic Evaluation of Flood Inundation Mapping Predictions Evaluation +Author: Surface Dynamics Modeling Lab +Author-email: Supath Dhital , Dipshika Devi +Maintainer-email: Supath Dhital , Dipshika Devi +Requires-Python: >=3.10 +Description-Content-Type: text/markdown +License-File: LICENSE.txt +Requires-Dist: rasterio<2.0.0,>=1.4.2 +Requires-Dist: numpy>=2 +Requires-Dist: geopandas<2.0.0,>=1.0.1 +Requires-Dist: shapely<3.0.0,>=2.0.6 +Requires-Dist: matplotlib<4.0.0,>=3.9.2 +Requires-Dist: plotly<6.0.0,>=5.24.1 +Requires-Dist: kaleido==0.2.1 +Requires-Dist: nbformat<6.0.0,>=5.10.4 +Requires-Dist: pyproj<4.0.0,>=3.7.0 +Requires-Dist: notebook<8.0.0,>=7.3.2 +Requires-Dist: boto3<2.0.0,>=1.36.16 +Requires-Dist: geemap +Provides-Extra: dev +Requires-Dist: pytest; extra == "dev" +Requires-Dist: black; extra == "dev" +Dynamic: license-file + +## Flood Inundation Mapping Predictions Evaluation Framework (FIMeval) +

+ +[![Version](https://img.shields.io/github/v/release/sdmlua/fimeval)](https://github.com/sdmlua/fimeval/releases) +[![Issues](https://img.shields.io/github/issues/sdmlua/fimeval)](https://github.com/sdmlua/fimeval/issues) +[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://opensource.org/licenses/GPL-3.0) +[![PyPI version](https://badge.fury.io/py/fimeval.svg?icon=si%3Apython)](https://badge.fury.io/py/fimeval) +[![PyPI Downloads](https://static.pepy.tech/badge/fimeval)](https://pepy.tech/projects/fimeval) + +| | | +| --- | --- | +| SDML Logo | This repository provides a user-friendly Python package and source code for the automatic evaluation of flood inundation maps. It is developed under Surface Dynamics Modeling Lab (SDML), Department of Geography and the Environment at The University of Alabama, United States. + + +### **Background** +
+ +The accuracy of the flood inundation mapping (FIM) is critical for model development and disaster preparedness. The evaluation of flood maps from different sources using geospatial platforms can be tedious and requires repeated processing and analysis for each map. These preprocessing steps include extracting the correct flood extent, assigning the same projection system to all the maps, categorizing the maps as binary flood maps, removal of permanent water bodies, etc. This manual data processing is cumbersome and prone to human error. + +To address these issues, we developed Flood Inundation Mapping Prediction Evaluation Framework (FIMeval), a Python-based FIM evaluation framework capable of automatically evaluating flood maps from different sources. FIMeval takes the advantage of comparing multiple target datasets with large benchmark datasets. It includes an option to incorporate permanent waterbodies as non-flood pixels with a user input file or pre-set dataset. In addition to traditional evaluation metrics, it can also compare the number of buildings inundated using a user input file or a pre-set dataset. + + + +### **Repository structure** +
+ +The architecture of the ```fimeval``` integrates different modules to which helps the automation of flood evaluation. All those modules codes are in source (```src``` ) folder. +```bash +fimeval/ +├── docs/ # Documentation (contains 'FIMserv' Tool usage sample codes) +│ └── sampledata/ # Contains the sample data to demonstrate how this frameworks works +│ └── fimeval_usage.ipynb #Sample code usage of the Evaluation framework +├── Images/ # have sample images for documentation +├── src/ +│ └── fimeval/ +│ ├──BuildingFootprint/ # Contains the evaluation of model predicted FIM with microsoft building footprint +│ │ └── microsoftBF.py +│ │ └── evaluationwithBF.py +│ └── ContingencyMap/ # Contains all the metrics calculation and contingency map generation +│ │ ├── evaluationFIM.py # main evaluation moodule +│ │ └── methods.py # Contains 3 different methods of evaluation +│ │ └── metrics.py # metrics calculation module +│ │ └── plotevaluationmetrics.py # use to vizualize the different performance metrics +│ │ └── printcontingency.py # prints the contingency map to quickly generate the Map layout +│ │ └── PWBs3.py # module which helps to get permanent water bodies from s3 bucket +│ └── utilis.py #Includes the resampling and reprojection of FIMs +└── tests/ # Includes test cases for different functionality +``` +The graphical representation of fimeval pipeline can be summarized as follows in **```Figure 1```**. Here, it will show all the steps incorporated within the ```fimeval``` during packaging and all functionality are interconnected to each other, resulting the automation of the framework. + +
+ image +
+Figure 1: Flowchart showing the entire framework pipeline. + +### **Framework Installation and Usage** +
+ +This framework is published as a python package in PyPI (https://pypi.org/project/fimeval/).For directly using the package, the user can install this package using python package installer 'pip' and can import on their workflows: + +```bash +#Install to use this framework +pip install uv #Makes the downloading much faster +uv pip install fimeval + +#Use this framework in your workflows using poetry +poetry add fimeval +``` +Import the package to the jupyter notebook or any python IDE. + +```bash +#Import the package +import fimeval as fp +``` +**Note: The framework usage provided in detailed in [Here (docs/fimeval_usage.ipynb)](./docs/fimeval_usage.ipynb)**. It has detail documentation from installation, setup, running- until results. + +#### **Main Directory Structure** +The main directory contains the primary folder for storing the case studies. If there is one case study, user can directly pass the case study folder as the main directory. Each case study folder must include a Benchmark FIM (B-FIM) with a 'benchmark' word assigned within the B-FIM file and different Model Predicted FIM (M-FIM) +in tif format. +For mutilple case studies,the main directory could be structure in such a way that contain the seperate folders for individual case studies.For example, if a user has two case studies they should create two seperate folders as shown in the Figure below. +
+ image +
+Figure 2: Main directory structure for one and multiple case study. + +This directory can be defined as follows while running framework. +```bash +main_dir = Path('./path/to/main/dir') +``` + +#### **Permanent Water Bodies (PWB)** +This framework uses PWB to first to delineate the PWB in the FIM and assign into different class so that the evaluation will be more fair. For the Contiguous United States (CONUS), the PWB is already integrated within the framework however, if user have more accurate PWB or using fimeval for outside US they can initialize and use PWB within fimeval framework. Currently it is using PWB publicly hosted by ESRI: https://hub.arcgis.com/datasets/esri::usa-detailed-water-bodies/about + +If user have more precise PWB, they can input their own PWB boundary as .shp and .gpkg format and need to assign the shapefile of the PWB and define directory as, +```bash +PWD_dir = Path('./path/to/PWB/vector/file') +``` +#### **Methods for Extracting Flood Extents** +1. **```smallest_extent```** + The framework will first check all the raster extents (benchmark and FIMs). It will then determine the smallest among all the rasters. A shape file will then be created to mask all the rasters. + +2. **```convex_hull```** + Another provision of determining flood extent is the generation of the minimum bounding polygon along the valid shapes. The framework will select the smallest raster extent followed by the generation of the valid vector shapes from the raster. It will then generate the convex hull (minimum bounding polygon along the valid shapes). + +3. **```AOI```** + User can give input an already pre-defined flood extent vector file. This method will only be valid if user is working with their own evaluation boundary, + +Depending upon user preference, they need to pass those method name as a argument while running the evaluation. + +The FIM evaluation extent for ```smallest_extent``` and ```convex_hull``` can be seen in below **Figure 3** which is GIS layout version of an contengency map output of `EvaluateFIM` module defined in Table 1. +
+ image +
+Figure 3: Layout showing the difference between smallest extent and convex hull FIM extent and evaluation result. + +Methods can be defined as follows. +```bash +method_name = "smallest_extent" +``` + +For the method 'AOI', user also need to pass the shapefile of the AOI along with method name as AOI. +```bash +#For AOI based FIM evaluation +method_name = "AOI" +AOI = Path('./path/to/AOI/vectorfile') +``` + +#### **Executing the Evaluation framework** +The complete description of different modules, what they are meant for, arguments taken to run that module and what will be the end results from each is described in below **Table 1**. If user import `fimeval` framework as `fp` into workflows, they can call each module mentioned in **Table 1** as `fp.Module_Name(args)`. Here arguments in italic represents the optional field, depending upon the user requirement. + +Table 1: Modules in `fimeval` are in order of execution. +| Module Name | Objective | Arguments | Outputs | +|------------|-----------|-----------|-----------| +| `EvaluateFIM` | It runs all the evaluation of FIM between B-FIM and M-FIMs. | `main_dir`: Main directory containing the case study folders,
`method_name`: How users wants to evaluate their FIM,
`outpur_dir`: Output directory where all the results and the intermidiate files will be saved for further calculation,
*`PWB_dir`*: The permanenet water bodies vectory file directory if user wants to user their own boundary,
*`target_crs`*: this fimeval framework needs the floodmaps to be in projected CRS so define the projected CRS in epsg code format,
*`target_resolution`*: sometime if the benchmark is very high resolution than candidate FIMs, it needs heavy computational time, so user can define the resolution if there FIMs are in different spatial resolution, else it will use the coarser resolution among all FIMS within that case. |The outputs includes generated files in TIFF, SHP, CSV, and PNG formats, all stored within the output folder. Users can visualize the TIFF files using any geospatial platform. The TIFF files consist of the binary Benchmark-FIM (Benchmark.tif), Model-FIM (Candidate.tif), and Agreement-FIM (Contingency.tif). The shp files contain the boundary of the generated flood extent.| +| `PlotContingencyMap` | For better understanding, It will print the agreement maps derived in first step. | `main_dir`, `method_name`, `output_dir` : Based on the those arguments, once all the evaluation is done, it will dynamically get the corresponding contingency raster for printing.| This prints the contingency map showing different class of evaluation (TP, FP, no data, PWB etc). The outputs look like- Figure 4 first row.| +| `PlotEvaluationMetrics` | For quick understanding of the evaluation metrics, to plot bar of evaluation scores. | `main_dir`, `method_name`, `output_dir` : Based on the those arguments, once all the evaluation is done, it will dynamically get the corresponding file for printing based on all those info.| This prints the bar plots which includes different performance metrics calculated by EvaluateFIM module. The outputs look like- Figure 4 second row.| +| `EvaluationWithBuildingFootprint` | For Building Footprint Analysis, user can specify shapefile of building footprints as .shp or .gpkg format. By default it consider global Microsoft building footprint dataset. Those data are hosted in Google Earth Engine (GEE) so, It pops up to authenticate the GEE account, please allow it and it will download the data based on evaluation boundary and evaluation is done. | `main_dir`, `method_name`, `output_dir`: Those arguments are as it is, same as all other modules.
*`building_footprint`*: If user wants to use their own building footprint file then pass the directory here, *`country`*: It is the 3 letter based country ISO code (eg. 'USA', NEP' etc), for the building data automation using GEE based on the evaluation extent, *`shapefile_dir`*: this is the directory of user defined AOI if user is working with their own boundary and automatic Building footprint download and evaluation, *`geeprojectID`*: this is the google earth engine google cloud project ID, which helps to access the GEE data and resources to work with building footprint download and process. | It will calculate the different metrics (e.g. TP, FP, CSI, F1, Accuracy etc) based on hit and miss of building on different M-FIM and B-FIM. Those all metrics will be saved as CSV format in `output_dir` and finally using that info it prints the counts of building foorpint in each FIMs as well as scenario on the evaluation end via bar plot.| + +

+ +

+ +Figure 4: Combined raw output from framework for different two method. First row (subplot a and b) and second row (subplot c and d) is contingency maps and evaluation metrics of FIM derived using `PrintContingencyMaP` and `PlotEvaluationMetrics` module. Third row (subplot e and f) is the output after processing and calculating of evaluation with BF by unsing `EvaluateWithBuildingFoorprint` module. + +## Installation Instructions + +### 1. Prerequisites + +Before installing `fimeval`, ensure the following software are installed: + +- **Python**: Version 3.10 or higher +- **Anaconda**: For managing environments and dependencies +- **GIS Software**: For Visulalisation + - [ArcGIS](https://www.esri.com/en-us/arcgis/products/index) or [QGIS](https://qgis.org/en/site/) +- **Optional**: + - [Google Earth Engine](https://earthengine.google.com/) account + - Java Runtime Environment (for using GEE API) + +--- + +### 2. Install Anaconda + +If Anaconda is not installed, download and install it from the [official website](https://www.anaconda.com/products/distribution). + +--- + +### 3. Set Up Virtual Environment + +#### For Mac Users + +Open **Terminal** and run: +```bash +# Create a new environment named 'fimeval' +conda create --name fimeval python=3.10 + +# Activate the environment +conda activate fimeval + +# Install fimeval package +pip install uv +uv pip install fimeval +``` + +### Google Colab Version + +To use fimeval in Google Colab, follow the steps below: + +## Upload Files +Upload all necessary input files (e.g., raster, shapefiles, model outputs) to your Google Drive. +## Open Google Colab +Go to Google Colab and sign in with a valid Google account. +## Mount Google Drive +In a new Colab notebook, mount the Google Drive +```bash +pip install fimeval +``` +### **Acknowledgements** +| | | +| --- | --- | +| ![alt text](https://ciroh.ua.edu/wp-content/uploads/2022/08/CIROHLogo_200x200.png) | Funding for this project was provided by the National Oceanic & Atmospheric Administration (NOAA), awarded to the Cooperative Institute for Research to Operations in Hydrology (CIROH) through the NOAA Cooperative Agreement with The University of Alabama. + +### **For More Information** +Contact Sagy Cohen + (sagy.cohen@ua.edu) + Dipsikha Devi, (ddevi@ua.edu) +Supath Dhital, (sdhital@crimson.ua.edu) diff --git a/src/fimeval.egg-info/SOURCES.txt b/src/fimeval.egg-info/SOURCES.txt new file mode 100644 index 0000000..ac5e4f3 --- /dev/null +++ b/src/fimeval.egg-info/SOURCES.txt @@ -0,0 +1,21 @@ +LICENSE.txt +README.md +pyproject.toml +src/fimeval/__init__.py +src/fimeval/utilis.py +src/fimeval.egg-info/PKG-INFO +src/fimeval.egg-info/SOURCES.txt +src/fimeval.egg-info/dependency_links.txt +src/fimeval.egg-info/requires.txt +src/fimeval.egg-info/top_level.txt +src/fimeval/BuildingFootprint/__init__.py +src/fimeval/BuildingFootprint/evaluationwithBF.py +src/fimeval/BuildingFootprint/microsoftBF.py +src/fimeval/ContingencyMap/PWBs3.py +src/fimeval/ContingencyMap/__init__.py +src/fimeval/ContingencyMap/evaluationFIM.py +src/fimeval/ContingencyMap/methods.py +src/fimeval/ContingencyMap/metrics.py +src/fimeval/ContingencyMap/plotevaluationmetrics.py +src/fimeval/ContingencyMap/printcontingency.py +tests/test_evaluationfim.py \ No newline at end of file diff --git a/src/fimeval.egg-info/dependency_links.txt b/src/fimeval.egg-info/dependency_links.txt new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/src/fimeval.egg-info/dependency_links.txt @@ -0,0 +1 @@ + diff --git a/src/fimeval.egg-info/requires.txt b/src/fimeval.egg-info/requires.txt new file mode 100644 index 0000000..7bf489e --- /dev/null +++ b/src/fimeval.egg-info/requires.txt @@ -0,0 +1,16 @@ +rasterio<2.0.0,>=1.4.2 +numpy>=2 +geopandas<2.0.0,>=1.0.1 +shapely<3.0.0,>=2.0.6 +matplotlib<4.0.0,>=3.9.2 +plotly<6.0.0,>=5.24.1 +kaleido==0.2.1 +nbformat<6.0.0,>=5.10.4 +pyproj<4.0.0,>=3.7.0 +notebook<8.0.0,>=7.3.2 +boto3<2.0.0,>=1.36.16 +geemap + +[dev] +pytest +black diff --git a/src/fimeval.egg-info/top_level.txt b/src/fimeval.egg-info/top_level.txt new file mode 100644 index 0000000..1eae136 --- /dev/null +++ b/src/fimeval.egg-info/top_level.txt @@ -0,0 +1 @@ +fimeval diff --git a/src/fimeval/BuildingFootprint/evaluationwithBF.py b/src/fimeval/BuildingFootprint/evaluationwithBF.py index cbe51d3..157b149 100644 --- a/src/fimeval/BuildingFootprint/evaluationwithBF.py +++ b/src/fimeval/BuildingFootprint/evaluationwithBF.py @@ -1,5 +1,4 @@ import os -import ee import glob import geopandas as gpd import rasterio @@ -21,6 +20,7 @@ def Changeintogpkg(input_path, output_dir, layer_name): gdf.to_file(output_gpkg, driver="GPKG") return output_gpkg + def GetFloodedBuildingCountInfo( building_fp_path, study_area_path, @@ -85,7 +85,7 @@ def count_centroids_in_raster(raster_path, label): if "bm" in str(raster1_path).lower(): count_centroids_in_raster(raster1_path, "Benchmark") count_centroids_in_raster(raster2_path, "Candidate") - + elif "candidate" in str(raster2_path).lower(): count_centroids_in_raster(raster1_path, "Candidate") count_centroids_in_raster(raster2_path, "Benchmark") @@ -106,9 +106,10 @@ def count_centroids_in_raster(raster_path, label): CSI = TP / (TP + FP + FN) if (TP + FP + FN) > 0 else 0 FAR = FP / (TP + FP) if (TP + FP) > 0 else 0 POD = TP / (TP + FN) if (TP + FN) > 0 else 0 - - - BDR = (centroid_counts["Candidate"]- centroid_counts["Benchmark"])/centroid_counts["Benchmark"] + + BDR = ( + centroid_counts["Candidate"] - centroid_counts["Benchmark"] + ) / centroid_counts["Benchmark"] counts_data = { "Category": [ @@ -223,6 +224,7 @@ def count_centroids_in_raster(raster_path, label): print(f"Performance metrics chart is saved as PNG at {output_path}") fig.show() + def process_TIFF( tif_files, contingency_files, building_footprint, boundary, method_path ): @@ -266,22 +268,46 @@ def process_TIFF( print("Warning: No benchmark file found.") elif not candidate_paths: print("Warning: No candidate files found.") - + + def find_existing_footprint(out_dir): gpkg_files = list(Path(out_dir).glob("*.gpkg")) return gpkg_files[0] if gpkg_files else None -#Incase user defined individual shapefile for each case study + +# Incase user defined individual shapefile for each case study def detect_shapefile(folder): - shapefile = None - for ext in (".shp", ".gpkg", ".geojson", ".kml"): - for file in os.listdir(folder): - if file.lower().endswith(ext): - shapefile = os.path.join(folder, file) - print(f"Auto-detected shapefile: {shapefile}") - return shapefile - return None - + shapefile = None + for ext in (".shp", ".gpkg", ".geojson", ".kml"): + for file in os.listdir(folder): + if file.lower().endswith(ext): + shapefile = os.path.join(folder, file) + print(f"Auto-detected shapefile: {shapefile}") + return shapefile + return None + + +def ensure_pyspark(version: str | None = "3.5.4") -> None: + """Install pyspark at runtime via `uv pip` into this env (no-op if present).""" + import importlib, shutil, subprocess, sys, re + try: + import importlib.util + if importlib.util.find_spec("pyspark"): + return + except Exception: + pass + uv = shutil.which("uv") + if not uv: + raise RuntimeError("`uv` not found on PATH. Please install uv or add it to PATH.") + if version is None: + spec = "pyspark" + else: + v = version.strip() + spec = f"pyspark{v}" if re.match(r"^[<>=!~]", v) else f"pyspark=={v}" + subprocess.check_call([uv, "pip", "install", "--python", sys.executable, spec]) + + + def EvaluationWithBuildingFootprint( main_dir, method_name, @@ -289,6 +315,7 @@ def EvaluationWithBuildingFootprint( country=None, building_footprint=None, shapefile_dir=None, + geeprojectID=None, ): tif_files_main = glob.glob(os.path.join(main_dir, "*.tif")) if tif_files_main: @@ -303,9 +330,7 @@ def EvaluationWithBuildingFootprint( if shapefile_dir: boundary = shapefile_dir - elif os.path.exists( - os.path.join(method_path, "BoundaryforEvaluation") - ): + elif os.path.exists(os.path.join(method_path, "BoundaryforEvaluation")): boundary = os.path.join( method_path, "BoundaryforEvaluation", "FIMEvaluatedExtent.shp" ) @@ -313,9 +338,11 @@ def EvaluationWithBuildingFootprint( boundary = detect_shapefile(main_dir) building_footprintMS = building_footprint + if building_footprintMS is None: - import msfootprint as msf - + ensure_pyspark() + from .microsoftBF import BuildingFootprintwithISO + out_dir = os.path.join(method_path, "BuildingFootprint") if not os.path.exists(out_dir): os.makedirs(out_dir) @@ -323,7 +350,15 @@ def EvaluationWithBuildingFootprint( if not EX_building_footprint: boundary_dir = shapefile_dir if shapefile_dir else boundary - msf.BuildingFootprintwithISO(country, boundary_dir, out_dir) + if geeprojectID: + BuildingFootprintwithISO( + country, + boundary_dir, + out_dir, + geeprojectID=geeprojectID, + ) + else: + BuildingFootprintwithISO(country, boundary_dir, out_dir) building_footprintMS = os.path.join( out_dir, f"building_footprint.gpkg" ) @@ -355,15 +390,19 @@ def EvaluationWithBuildingFootprint( os.path.join(method_path, "BoundaryforEvaluation") ): boundary = os.path.join( - method_path, "BoundaryforEvaluation", "FIMEvaluatedExtent.shp" + method_path, + "BoundaryforEvaluation", + "FIMEvaluatedExtent.shp", ) else: boundary = detect_shapefile(os.path.join(main_dir, folder)) building_footprintMS = building_footprint + if building_footprintMS is None: - import msfootprint as msf - + ensure_pyspark() + from .microsoftBF import BuildingFootprintwithISO + out_dir = os.path.join(method_path, "BuildingFootprint") if not os.path.exists(out_dir): os.makedirs(out_dir) @@ -372,9 +411,17 @@ def EvaluationWithBuildingFootprint( boundary_dir = ( shapefile_dir if shapefile_dir else boundary ) - msf.BuildingFootprintwithISO( - country, boundary_dir, out_dir - ) + if geeprojectID: + BuildingFootprintwithISO( + country, + boundary_dir, + out_dir, + geeprojectID=geeprojectID, + ) + else: + BuildingFootprintwithISO( + country, boundary_dir, out_dir + ) building_footprintMS = os.path.join( out_dir, f"building_footprint.gpkg" ) diff --git a/src/fimeval/BuildingFootprint/microsoftBF.py b/src/fimeval/BuildingFootprint/microsoftBF.py new file mode 100644 index 0000000..110f551 --- /dev/null +++ b/src/fimeval/BuildingFootprint/microsoftBF.py @@ -0,0 +1,131 @@ +# Importing necessary libraries +import geemap +import ee +import os +from shapely.geometry import box +import pandas as pd +from pathlib import Path +import geopandas as gpd +from pyspark.sql import SparkSession +from shapely.wkt import loads +import shutil + +# Suppress the warnings +import warnings + +warnings.filterwarnings("ignore") + +# Authenticate and initialize Earth Engine +ee.Authenticate() + +# %% +def split_into_tiles(boundary, tile_size=0.1): + bounds = boundary.total_bounds + x_min, y_min, x_max, y_max = bounds + tiles = [] + x = x_min + while x < x_max: + y = y_min + while y < y_max: + tile = box(x, y, x + tile_size, y + tile_size) + if tile.intersects(boundary.unary_union): + tiles.append(tile) + y += tile_size + x += tile_size + return gpd.GeoDataFrame(geometry=tiles, crs=boundary.crs) + + +# Merge the final geojson files +def mergeGeoJSONfiles(output_dir, merged_file): + output_dir = Path(output_dir) + files = list(output_dir.glob("*.geojson")) + gdfs = [gpd.read_file(file) for file in files] + merged_gdf = gpd.GeoDataFrame(pd.concat(gdfs, ignore_index=True), crs="EPSG:4326") + merged_gdf.to_file(merged_file, driver="GPKG") + + +# Process each batch with number of tiles +def process_batch(partition, collection_name, output_dir, boundary_wkt, projectID=None): + try: + if projectID: + ee.Initialize(project=projectID) + else: + ee.Initialize() + + except Exception: + print("To initialize, please provide the earth engine project ID") + + # Convert WKT boundary to geometry + boundary = loads(boundary_wkt) + results = [] + + for tile_wkt in partition: + try: + tile = loads(tile_wkt) + aoi = ee.Geometry(tile.__geo_interface__) + collection = ee.FeatureCollection(collection_name).filterBounds(aoi) + + # Download features and filter by boundary + gdf = geemap.ee_to_gdf(collection) + gdf = gdf[gdf.geometry.intersects(boundary)] + + # Save each tile as a GeoJSON file + tile_id = f"tile_{hash(tile)}" + output_file = Path(output_dir) / f"{tile_id}.geojson" + gdf.to_file(output_file, driver="GeoJSON") + results.append(f"Saved: {output_file}") + except Exception as e: + results.append(f"Error processing tile: {e}") + + return results + + +def getBuildingFootprintSpark( + countryISO, boundary_file, out_dir, tile_size, projectID=None +): + spark = SparkSession.builder.appName("BuildingFootprints").getOrCreate() + + # Make temporary directory + temp_dir = out_dir / "temp" + temp_dir.mkdir(parents=True, exist_ok=True) + + # Load and process boundary + boundary = gpd.read_file(boundary_file).to_crs("EPSG:4326") + tiles = split_into_tiles(boundary, tile_size) + boundary_wkt = boundary.unary_union.wkt + + collection_names = [f"projects/sat-io/open-datasets/VIDA_COMBINED/{countryISO}"] + + # Distribute processing + for collection_name in collection_names: + tiles_rdd = spark.sparkContext.parallelize( + tiles.geometry.apply(lambda x: x.wkt).tolist(), numSlices=10 + ) + results = tiles_rdd.mapPartitions( + lambda partition: process_batch( + partition, collection_name, str(temp_dir), boundary_wkt, projectID + ) + ).collect() + + # Merge GeoJSON files + mergeGeoJSONfiles(temp_dir, out_dir / "building_footprint.gpkg") + + # Clean up the temp directory + shutil.rmtree(temp_dir, ignore_errors=True) + + print(f"Building footprint data saved to {out_dir / 'building_footprint.gpkg'}") + + +# %% +# Export the building footprint +def BuildingFootprintwithISO(countryISO, ROI, out_dir, geeprojectID=None): + out_dir = Path(out_dir) + out_dir.mkdir(parents=True, exist_ok=True) + filename = out_dir / "building_footprint.gpkg" + + if filename.exists(): + os.remove(filename) + + getBuildingFootprintSpark( + countryISO, ROI, out_dir, tile_size=0.05, projectID=geeprojectID + ) diff --git a/src/fimeval/ContingencyMap/PWBs3.py b/src/fimeval/ContingencyMap/PWBs3.py index d0cb372..83ecdca 100644 --- a/src/fimeval/ContingencyMap/PWBs3.py +++ b/src/fimeval/ContingencyMap/PWBs3.py @@ -1,4 +1,4 @@ -#import Libraries +# import Libraries import geopandas as gpd import boto3 import botocore @@ -7,27 +7,27 @@ # Initialize an anonymous S3 client s3 = boto3.client( - 's3', - config=botocore.config.Config(signature_version=botocore.UNSIGNED) + "s3", config=botocore.config.Config(signature_version=botocore.UNSIGNED) ) -bucket_name = 'sdmlab' -pwb_folder = "PWB/" +bucket_name = "sdmlab" +pwb_folder = "PWB/" + def PWB_inS3(s3_client, bucket, prefix): """Download all components of a shapefile from S3 into a temporary directory.""" tmp_dir = tempfile.mkdtemp() response = s3_client.list_objects_v2(Bucket=bucket, Prefix=prefix) - if 'Contents' not in response: + if "Contents" not in response: raise ValueError("No files found in the specified S3 folder.") - - for obj in response['Contents']: - file_key = obj['Key'] - file_name = os.path.basename(file_key) - if file_name.endswith(('.shp', '.shx', '.dbf', '.prj', '.cpg')): + + for obj in response["Contents"]: + file_key = obj["Key"] + file_name = os.path.basename(file_key) + if file_name.endswith((".shp", ".shx", ".dbf", ".prj", ".cpg")): local_path = os.path.join(tmp_dir, file_name) s3_client.download_file(bucket, file_key, local_path) - + shp_files = [f for f in os.listdir(tmp_dir) if f.endswith(".shp")] if not shp_files: raise ValueError("No .shp file found after download.") @@ -35,7 +35,8 @@ def PWB_inS3(s3_client, bucket, prefix): shp_path = os.path.join(tmp_dir, shp_files[0]) return shp_path + def get_PWB(): shp_path = PWB_inS3(s3, bucket_name, pwb_folder) pwb = gpd.read_file(shp_path) - return pwb \ No newline at end of file + return pwb diff --git a/src/fimeval/ContingencyMap/evaluationFIM.py b/src/fimeval/ContingencyMap/evaluationFIM.py index b09c962..2db795b 100644 --- a/src/fimeval/ContingencyMap/evaluationFIM.py +++ b/src/fimeval/ContingencyMap/evaluationFIM.py @@ -21,12 +21,14 @@ from .PWBs3 import get_PWB from ..utilis import MakeFIMsUniform -#giving the permission to the folder + +# giving the permission to the folder def is_writable(path): """Check if the directory and its contents are writable.""" path = Path(path) return os.access(path, os.W_OK) + def fix_permissions(path): path = Path(path).resolve() script_path = Path(__file__).parent / "fix_permissions.sh" @@ -35,11 +37,15 @@ def fix_permissions(path): raise FileNotFoundError(f"Shell script not found: {script_path}") if is_writable(path): - return + return try: - result = subprocess.run(["bash", str(script_path), str(path)], - check=True, capture_output=True, text=True) + result = subprocess.run( + ["bash", str(script_path), str(path)], + check=True, + capture_output=True, + text=True, + ) print(result.stdout) except subprocess.CalledProcessError as e: print(f"Shell script failed:\n{e.stderr}") @@ -79,7 +85,7 @@ def evaluateFIM( # Get the smallest matched raster extent and make a boundary shapefile smallest_raster_path = get_smallest_raster_path(benchmark_path, *candidate_paths) - #If method is AOI, and direct shapefile directory is not provided, then it will search for the shapefile in the folder + # If method is AOI, and direct shapefile directory is not provided, then it will search for the shapefile in the folder if method.__name__ == "AOI": # If shapefile is not provided, search in the folder if shapefile is None: @@ -358,7 +364,8 @@ def resize_image( print(f"Evaluation metrics saved to {csv_file}") return results -#Safely deleting the folder + +# Safely deleting the folder def safe_delete_folder(folder_path): fix_permissions(folder_path) try: @@ -370,19 +377,28 @@ def safe_delete_folder(folder_path): except Exception as e: print(f"Error deleting {folder_path}: {e}") -def EvaluateFIM(main_dir, method_name, output_dir, PWB_dir=None, shapefile_dir=None, target_crs=None, target_resolution=None): + +def EvaluateFIM( + main_dir, + method_name, + output_dir, + PWB_dir=None, + shapefile_dir=None, + target_crs=None, + target_resolution=None, +): main_dir = Path(main_dir) # Read the permanent water bodies if PWB_dir is None: gdf = get_PWB() else: gdf = gpd.read_file(PWB_dir) - - #Grant the permission to the main directory + + # Grant the permission to the main directory print(f"Fixing permissions for {main_dir}...") fix_permissions(main_dir) - #runt the process + # runt the process def process_TIFF(tif_files, folder_dir): benchmark_path = None candidate_path = [] @@ -422,7 +438,9 @@ def process_TIFF(tif_files, folder_dir): # Check if main_dir directly contains tif files TIFFfiles_main_dir = list(main_dir.glob("*.tif")) if TIFFfiles_main_dir: - MakeFIMsUniform(main_dir, target_crs=target_crs, target_resolution=target_resolution) + MakeFIMsUniform( + main_dir, target_crs=target_crs, target_resolution=target_resolution + ) # processing folder processing_folder = main_dir / "processing" @@ -434,15 +452,20 @@ def process_TIFF(tif_files, folder_dir): for folder in main_dir.iterdir(): if folder.is_dir(): tif_files = list(folder.glob("*.tif")) - + if tif_files: - MakeFIMsUniform(folder, target_crs=target_crs, target_resolution=target_resolution) - + MakeFIMsUniform( + folder, + target_crs=target_crs, + target_resolution=target_resolution, + ) + processing_folder = folder / "processing" TIFFfiles = list(processing_folder.glob("*.tif")) process_TIFF(TIFFfiles, folder) safe_delete_folder(processing_folder) else: - print(f"Skipping {folder.name} as it doesn't contain any tif files.") - + print( + f"Skipping {folder.name} as it doesn't contain any tif files." + ) diff --git a/src/fimeval/ContingencyMap/metrics.py b/src/fimeval/ContingencyMap/metrics.py index 0299837..95099be 100644 --- a/src/fimeval/ContingencyMap/metrics.py +++ b/src/fimeval/ContingencyMap/metrics.py @@ -7,21 +7,21 @@ def evaluationmetrics(out_image1, out_image2): unique_values, counts = np.unique(merged, return_counts=True) class_pixel_counts = dict(zip(unique_values, counts)) class_pixel_counts - TN = class_pixel_counts.get(1,0) - FP = class_pixel_counts.get(2,0) - FN = class_pixel_counts.get(3,0) - TP = class_pixel_counts.get(4,0) + TN = class_pixel_counts.get(1, 0) + FP = class_pixel_counts.get(2, 0) + FN = class_pixel_counts.get(3, 0) + TP = class_pixel_counts.get(4, 0) epsilon = 1e-8 - TPR = TP / (TP + FN+epsilon) - FNR = FN / (TP + FN+epsilon) - Acc = (TP + TN) / (TP + TN + FP + FN+epsilon) - Prec = TP / (TP + FP+epsilon) - sen = TP / (TP + FN+epsilon) - F1_score = 2 * (Prec * sen) / (Prec + sen+epsilon) - CSI = TP / (TP + FN + FP+epsilon) - POD = TP / (TP + FN+epsilon) - FPR = FP / (FP + TN+epsilon) - FAR = FP / (TP + FP+epsilon) + TPR = TP / (TP + FN + epsilon) + FNR = FN / (TP + FN + epsilon) + Acc = (TP + TN) / (TP + TN + FP + FN + epsilon) + Prec = TP / (TP + FP + epsilon) + sen = TP / (TP + FN + epsilon) + F1_score = 2 * (Prec * sen) / (Prec + sen + epsilon) + CSI = TP / (TP + FN + FP + epsilon) + POD = TP / (TP + FN + epsilon) + FPR = FP / (FP + TN + epsilon) + FAR = FP / (TP + FP + epsilon) return ( unique_values, diff --git a/src/fimeval/ContingencyMap/printcontingency.py b/src/fimeval/ContingencyMap/printcontingency.py index ad40745..aaf6d0e 100644 --- a/src/fimeval/ContingencyMap/printcontingency.py +++ b/src/fimeval/ContingencyMap/printcontingency.py @@ -65,7 +65,7 @@ def getContingencyMap(raster_path, method_path): 2: "False positive", 3: "False negative", 4: "True positive", - 5: "Permanent water bodies" + 5: "Permanent water bodies", } legend_patches = [ Patch( diff --git a/src/fimeval/__init__.py b/src/fimeval/__init__.py index dbed34e..74cedc8 100644 --- a/src/fimeval/__init__.py +++ b/src/fimeval/__init__.py @@ -1,11 +1,20 @@ -#Evaluation modules +# Evaluation modules from .ContingencyMap.evaluationFIM import EvaluateFIM from .ContingencyMap.printcontingency import PrintContingencyMap from .ContingencyMap.plotevaluationmetrics import PlotEvaluationMetrics from .ContingencyMap.PWBs3 import get_PWB -#Utility modules +# Utility modules from .utilis import compress_tif_lzw # Evaluation with Building foorprint module from .BuildingFootprint.evaluationwithBF import EvaluationWithBuildingFootprint + +__all__ = [ + "EvaluateFIM", + "PrintContingencyMap", + "PlotEvaluationMetrics", + "get_PWB", + "EvaluationWithBuildingFootprint", + "compress_tif_lzw", +] diff --git a/src/fimeval/utilis.py b/src/fimeval/utilis.py index 4b65348..b2c5d6e 100644 --- a/src/fimeval/utilis.py +++ b/src/fimeval/utilis.py @@ -6,24 +6,27 @@ import geopandas as gpd from rasterio.warp import calculate_default_transform, reproject, Resampling -#Lossless compression to reduce the file size + +# Lossless compression to reduce the file size def compress_tif_lzw(tif_path): # Read original file with rasterio.open(tif_path) as src: profile = src.profile.copy() data = src.read() - profile.update(compress='lzw') + profile.update(compress="lzw") - with rasterio.open(tif_path, 'w', **profile) as dst: + with rasterio.open(tif_path, "w", **profile) as dst: dst.write(data) -#Check whether it is a projected CRS + +# Check whether it is a projected CRS def is_projected_crs(crs): return crs and crs.is_projected -#Check if the FIM bounds are within the CONUS + +# Check if the FIM bounds are within the CONUS def is_within_conus(bounds, crs=None): - CONUS_BBOX = (-125, 24, -66.5, 49.5) + CONUS_BBOX = (-125, 24, -66.5, 49.5) left, bottom, right, top = bounds if crs and crs.is_projected: @@ -38,7 +41,8 @@ def is_within_conus(bounds, crs=None): and top <= CONUS_BBOX[3] ) -#Reproject the FIMs to EPSG:5070 if withinUS and user doesnot define any target CRS, else user need to define it + +# Reproject the FIMs to EPSG:5070 if withinUS and user doesnot define any target CRS, else user need to define it def reprojectFIMs(src_path, dst_path, target_crs): with rasterio.open(src_path) as src: if src.crs != target_crs: @@ -46,14 +50,16 @@ def reprojectFIMs(src_path, dst_path, target_crs): src.crs, target_crs, src.width, src.height, *src.bounds ) kwargs = src.meta.copy() - kwargs.update({ - 'crs': target_crs, - 'transform': transform, - 'width': width, - 'height': height - }) - - with rasterio.open(dst_path, 'w', **kwargs) as dst: + kwargs.update( + { + "crs": target_crs, + "transform": transform, + "width": width, + "height": height, + } + ) + + with rasterio.open(dst_path, "w", **kwargs) as dst: for i in range(1, src.count + 1): reproject( source=rasterio.band(src, i), @@ -62,14 +68,15 @@ def reprojectFIMs(src_path, dst_path, target_crs): src_crs=src.crs, dst_transform=transform, dst_crs=target_crs, - resampling=Resampling.nearest + resampling=Resampling.nearest, ) else: print(f"Source raster is already in {target_crs}. No reprojection needed.") shutil.copy(src_path, dst_path) compress_tif_lzw(dst_path) -#Resample into the coarser resoution amoung all FIMS within the case + +# Resample into the coarser resoution amoung all FIMS within the case def resample_to_resolution(src_path, x_resolution, y_resolution): src_path = Path(src_path) print(src_path) @@ -78,20 +85,15 @@ def resample_to_resolution(src_path, x_resolution, y_resolution): with rasterio.open(src_path) as src: transform = rasterio.transform.from_origin( - src.bounds.left, src.bounds.top, - x_resolution, y_resolution + src.bounds.left, src.bounds.top, x_resolution, y_resolution ) width = int((src.bounds.right - src.bounds.left) / x_resolution) height = int((src.bounds.top - src.bounds.bottom) / y_resolution) kwargs = src.meta.copy() - kwargs.update({ - 'transform': transform, - 'width': width, - 'height': height - }) + kwargs.update({"transform": transform, "width": width, "height": height}) # Write to temporary file - with rasterio.open(temp_path, 'w', **kwargs) as dst: + with rasterio.open(temp_path, "w", **kwargs) as dst: for i in range(1, src.count + 1): reproject( source=rasterio.band(src, i), @@ -100,22 +102,23 @@ def resample_to_resolution(src_path, x_resolution, y_resolution): src_crs=src.crs, dst_transform=transform, dst_crs=src.crs, - resampling=Resampling.nearest + resampling=Resampling.nearest, ) - os.remove(src_path) # delete original - os.rename(temp_path, src_path) + os.remove(src_path) # delete original + os.rename(temp_path, src_path) + -#Check if the FIMs are in the same CRS or not else do further operation +# Check if the FIMs are in the same CRS or not else do further operation def MakeFIMsUniform(fim_dir, target_crs=None, target_resolution=None): fim_dir = Path(fim_dir) - tif_files = list(fim_dir.glob('*.tif')) + tif_files = list(fim_dir.glob("*.tif")) if not tif_files: print(f"No TIFF files found in {fim_dir}") return # Create processing folder to save standardized files - processing_folder = fim_dir / 'processing' + processing_folder = fim_dir / "processing" processing_folder.mkdir(exist_ok=True) # Collect info about each TIFF @@ -131,7 +134,7 @@ def MakeFIMsUniform(fim_dir, target_crs=None, target_resolution=None): print(f"Error opening {tif_path}: {e}") return - #CRS Check & Reproject if needed + # CRS Check & Reproject if needed all_projected = all(projected_flags) all_same_crs = len(set(crs_list)) == 1 @@ -143,22 +146,26 @@ def MakeFIMsUniform(fim_dir, target_crs=None, target_resolution=None): final_crs = "EPSG:5070" print(f"Defaulting to CONUS CRS: {final_crs}") else: - print("Mixed or non-CONUS CRS detected. Please provide a valid target CRS.") + print( + "Mixed or non-CONUS CRS detected. Please provide a valid target CRS." + ) return - + print(f"Reprojecting all rasters to {final_crs}") for src_path in tif_files: dst_path = processing_folder / src_path.name reprojectFIMs(str(src_path), str(dst_path), final_crs) - compress_tif_lzw(dst_path) - + compress_tif_lzw(dst_path) + else: - print("All rasters are in the same projected CRS. Copying to processing folder.") + print( + "All rasters are in the same projected CRS. Copying to processing folder." + ) for src_path in tif_files: shutil.copy(src_path, processing_folder / src_path.name) # Resolution Check & Resample if needed - processed_tifs = list(processing_folder.glob('*.tif')) + processed_tifs = list(processing_folder.glob("*.tif")) final_resolutions = [] for tif_path in processed_tifs: with rasterio.open(tif_path) as src: diff --git a/tests/test_evaluationfim.py b/tests/test_evaluationfim.py index 478c668..1a269cb 100644 --- a/tests/test_evaluationfim.py +++ b/tests/test_evaluationfim.py @@ -5,12 +5,14 @@ # "../docs/sampledata" ) PWD_dir = "./path/to/PWB" -output_dir = "./path/to/output" # This is the output directory where the results will be saved -target_crs = "EPSG:5070" # Target CRS for reprojecting the FIMs, need to be in EPSG code of Projected CRS -target_resolution = 10 #This will be in meters, if it passes the FIMS will be resampled to this resolution else, it will find the coarser resolution among all FIMS for this case and use that to resample! +output_dir = ( + # "./path/to/output" # This is the output directory where the results will be saved +) +target_crs = "EPSG:5070" # Target CRS for reprojecting the FIMs, need to be in EPSG code of Projected CRS +target_resolution = 10 # This will be in meters, if it passes the FIMS will be resampled to this resolution else, it will find the coarser resolution among all FIMS for this case and use that to resample! -building_footprint = "../path/to/building_footprint.shp" # If user is working with user defined building footprint shapefile +building_footprint = "path/to/building_footprint.shp" # If user is working with user defined building footprint shapefile # If user is working with user defined shapefile AOI = "path/to/shapefile.shp" # This shapefile should be in projected CRS, if not, it will be reprojected to the target CRS @@ -19,33 +21,31 @@ # 1. Smallest extent # 2. Convex Hull # 3. AOI (User defined shapefile) -method_name = "AOI" +method_name = "smallest_extent" # 3 letter country ISO code countryISO = "USA" -# Run the evaluation -# It has the Permanent Water Bodies (PWB) dataset as default for United States -# fe.EvaluateFIM(Main_dir, method_name, output_dir) - -# OR, If the Evaluation Study Area is outside the US or, user has their own PWB dataset -fe.EvaluateFIM(Main_dir, method_name, output_dir) +def test_evaluation_framework(): + # Run the evaluation + # It has the Permanent Water Bodies (PWB) dataset as default for United States + # fe.EvaluateFIM(Main_dir, method_name, output_dir) -#If the FIMS are not in projected crs or are in different spatial resolution -# fe.EvaluateFIM(Main_dir, method_name, output_dir, target_crs=target_crs, shapefile_dir = AOI, target_resolution=target_resolution) + # OR, If the Evaluation Study Area is outside the US or, user has their own PWB dataset + # fe.EvaluateFIM(Main_dir, method_name, output_dir) -# # Once the FIM evaluation is done, print the contingency map -# fe.PrintContingencyMap(Main_dir, method_name, output_dir) + # If the FIMS are not in projected crs or are in different spatial resolution + # fe.EvaluateFIM(Main_dir, method_name, output_dir, target_crs=target_crs, shapefile_dir = AOI, target_resolution=target_resolution) + # # Once the FIM evaluation is done, print the contingency map + # fe.PrintContingencyMap(Main_dir, method_name, output_dir) -# # Plot rhe evaluation metrics after the FIM evaluation -# fe.PlotEvaluationMetrics(Main_dir, method_name, output_dir) + # # Plot the evaluation metrics after the FIM evaluation + # fe.PlotEvaluationMetrics(Main_dir, method_name, output_dir) -# # # FIM Evaluation with Building Footprint (by default, it uses the Microsoft Building Footprint dataset) -# fe.EvaluationWithBuildingFootprint( -# Main_dir, method_name, output_dir, country=countryISO -# ) + # FIM Evaluation with Building Footprint (by default, it uses the Microsoft Building Footprint dataset) + # fe.EvaluationWithBuildingFootprint(Main_dir, method_name, output_dir, country=countryISO, geeprojectID="supathdh") -# # If user have their own building footprint dataset, they can use it as well -# fe.EvaluationWithBuildingFootprint(Main_dir, method_name, output_dir, building_footprint = building_footprint, shapefile_dir=AOI) + # If user have their own building footprint dataset, they can use it as well + fe.EvaluationWithBuildingFootprint(Main_dir, method_name, output_dir, building_footprint=building_footprint) diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..1a02de5 --- /dev/null +++ b/uv.lock @@ -0,0 +1,2844 @@ +version = 1 +revision = 2 +requires-python = ">=3.10" +resolution-markers = [ + "python_full_version >= '3.14'", + "python_full_version >= '3.12' and python_full_version < '3.14'", + "python_full_version == '3.11.*'", + "python_full_version < '3.11'", +] + +[[package]] +name = "affine" +version = "2.4.0" +source = { registry = 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