Boreholes Data Extraction is a pipeline to extract structured data from borehole profiles in PDF files. Extracted properties (currently coordinates, depths and associated material descriptions) are returned in JSON format, and (optionally) visualized as PNG images. This project was initiated by the Swiss Federal Office of Topography swisstopo, and is developed with support from Visium.
The Federal Office of Topography swisstopo is Switzerland's geoinformation centre. The Swiss Geological Survey at swisstopo is the federal competence centre for the collection, analysis, storage, and provision of geological data of national interest.
Data from boreholes is an essential source for our knowledge about the subsurface. In order to manage and publish borehole data of national interest, swisstopo has developed the application boreholes.swissgeol.ch (currently for internal use only), part of the swissgeol.ch platform. As of August 2024, over 30.000 boreholes are registered in the application database, a number that is rapidly increasing thanks to an improved data exchange with cantonal offices, other government agencies and federal corporations such as the Swiss Federal Railways SBB. In the coming years, the number of boreholes in the database is expected to keep increasing to a multiple of the current size. Data is being added from both boreholes that were recently constructed and documented, as well as from older boreholes that were until now only documented in separate databases or in analogue archives. Data from older boreholes can still be very relevant, as geology only changes very slowly, and newer data is often unavailable (and expensive to collect).
In order to use the collected borehole data efficiently, it is critical that both metadata as well as geological information is digitally stored in a structured database. However, the relevant data for most boreholes that are received by swisstopo, is contained in PDF-files that lack a standardized structure. Older data is often only available in the form of a scanned image, obtained from a printed document or from a microfiche. Manually entering all the relevant data from these various sources into a structured database is not feasible, given the large amount of boreholes and the continuous influx of new data.
Therefore, the goal of this project is to automate the extraction of structured data from borehole profiles as much as possible. As far as swisstopo is concerned, the use case is to integrate the data extraction pipeline with the application boreholes.swissgeol.ch (GitHub), where a user interface for efficient quality control of the automatically extracted data will also be implemented.
All code and documentation is published in this GitHub repository as open source software. All other persons, companies or agencies who manage borehole data of their own, are welcome to use the data extraction pipeline on their own data and to contribute to the project with their own improvements/additions.
Below is a list of the most relevant properties for the extraction of structure data from borehole profiles, using the following styles:
- Properties that can already be automatically extracted by the current pipeline implementation are in bold.
- Properties for which the implementation of automatic extraction is actively being worked on, are in italics.
- Properties that are likely to be added to the data extraction pipeline in the future, but are not under active development yet, are in regular font.
- Metadata
- Coordinates
- Date
- Drilling method
- Lithology / stratigraphy
- Depths (upper and lower bound of each layer)
- Material descriptions (as plain text)
- USCS classification, color, consistency, plasticity...
- Geological interpretations
- Other
- Hydrogeology (ground water levels)
- Instrumentation
- Casing
- Borehole geometry
- ...
Existing work related to this project is mostly focussed on the extraction and classification of specific properties from textual geological descriptions. Notable examples include GEOBERTje (Belgium), geo-ner-model (UK), GeoVec und dh2loop (Australia). The scope of this project is considerable wider, in particular regarding the goal of understanding borehole profiles in various languages and with an unknown layout, where the document structure first needs to be understood, before the relevant text fragments can be identified and extracted. A commercial solution with similar goals is Civils.ai (Youtube video).
The automatic data extraction pipeline can be considered to belong to the field of automatic/intelligent document processing. As such, it involves a combination of methods from multiple fields in data science and machine learning, in particular computer vision (e.g. object detection, line detection) and natural language processing (large language models, named entity recognition). Some of these have already been implemented (e.g. the Line Segment Detector algorithm), others are planned as future work.
The project is under active development and there is no release to this date. The quality/accuracy of the results may vary strongly depending on the documents that are used as input.
The input PDF files must contain digital text content. For PDF files that are not digitally-born (e.g. scanned documents), this means that OCR must be performed, and the OCR results stored in the PDF file, before using the file as an input for this data extraction pipeline. The quality of the extracted data is dependent on the quality of the OCR. At swisstopo, we use the AWS Textract service together with our own code from the swissgeol-ocr repository for this purpose.
The pipeline has been optimized for and tested on boreholes profiles from Switzerland that have been written in German or (to a more limited extent) in French.
With regard to the extraction of coordinates, the Swiss coordinate systems LV95 as well as the older LV03 are supported (visualization of the differences).
With the current version of the code, groundwater can only be found at depth smaller than 200 meters. This threshold is defined in src/stratigraphy/groundwater/groundwater_extraction.py by the constant MAX_DEPTH.
The groundwater is extracted in two main ways from the borehole documents. The first one aims to match a groundwater-related keyword in the text extracted from the document (e.g., groundwater, groundwater-level). The second technique focuses on extracting the groundwater-related illustration from the document by using template matching. The matching of the groundwater illustration is disabled by default as it significantly increases the runtime of the data extraction pipeline. You can control the activation of this feature by using the IS_SEARCHING_GROUNDWATER_ILLUSTRATION environment variable.
Add the following line to the .env document to turn on the groundwater detection:
IS_SEARCHING_GROUNDWATER_ILLUSTRATION="True"
The extraction of groundwater relies on the scikit-image library. This library is part of the optional dependencies of this project as part of the groundwater_illustration_matching dependencies in the pyproject.toml file. If you wish to use the template matching algorithm to determine the groundwater elevation, depth, and date, please install this dependency before running the code.
This project is managed and financed by the Swiss Federal Office of Topography swisstopo. Many contributions come from Visium, in their role as contractor for swisstopo for this project.
This project is released as open source software, under the principle of "public money, public code", in accordance with the 2023 federal law "EMBAG", and following the guidance of the tools for OSS published by the Federal Chancellery.
We welcome feedback, bug reports and code contributions (provided they are compatible with swisstopo's roadmap) from third parties. Feature requests and support requests can only be fulfilled as long as they are compatible with swisstopo's legal mandate. Other organisations (both within Switzerland or internationally) who manage their own borehole data and are interested in using and/or contributing to this project, are encouraged to contact us, in order to discuss the possibility of establishing a partnership for collaborating more closely on this project.
- Stijn Vermeeren @stijnvermeeren-swisstopo (swisstopo) - Project Lead
- David Cleres @dcleres (Visium)
- Renato Durrer @redur (Visium)
The source code of this project is licensed under the MIT License. Certain libraries in the project dependencies might be distributed under more restrictive open source licenses. Most notably, PyMuPDF is available under either the AGPL license or a commercial license. These more restrictive licenses would apply when distributing this project in a single package together with its dependencies.
We use pip to manage the packages dependencies. We recommend using a virtual environment within which to install all dependencies.
The below commands will install the package for you (assuming you have successfully cloned the repository):
python -m venv env
source env/bin/activate
pip install -e '.[all]'Alternatively you can replace the pip install -e '.[all]' command with pip install git+https://github.com/swisstopo/swissgeol-boreholes-dataextraction.git in production scenarios.
Adding pip packages can be done by editing the pyproject.toml of the project and adding the required package.
To execute the data extraction pipeline, follow these steps:
- Activate the virtual environment
Activate your virtual environment. On unix systems this is
source env/bin/activate- Download the borehole profiles, optional
Use boreholes-download-profiles to download the files to be processed from an AWS S3 storage. In order to do so, you need to authenticate with aws first. We recommend to use the aws CLI for that purpose. This step is optional, you can continue with step 3 on your own set of borehole profiles.
- Run the extraction script
The main script for the extraction pipeline is located at src/stratigraphy/main.py. A cli command is created to run this script.
Run boreholes-extract-all to run the main extraction script. You need to specify the input directory or a single PDF file using the -i or --input-directory flag.
The script will source all PDFs from the specified directory and create PNG files in the data/output/draw directory.
Use boreholes-extract-all --help to see all options for the extraction script.
- Check the results
The script produces output in two different formats:
- A file
data/output/predictions.jsonthat contains all extracted data in a machine-readable format. The structure of this file is documented in README.predictions-json.md. - A PNG image of each processed PDF page in the
data/output/drawdirectory, where the extracted data is highlighted.
The project structure and the most important files are as follows:
root/: The root directory of the project.src/: The source code of the project.app/: The API of the project.main.py: The main script that launches the API.common/config.py: Config file for the API.v1/: Contain all the code for the version 1 of the API.v1/router.py: Presents at a glance all the available endpoints.
stratigraphy/: The main package of the project.main.py: The main script of the project. This script runs the data extraction pipeline.line_detection.py: Contains functionalities for line detection on pdf pages.util/: Utility scripts and modules.benchmark/: Scripts to evaluate the data extraction.
data/: The data used by the project.output/:draw/: The directory where the PNG files are saved.predictions.json: The output file of the project, containing the results of the data extraction process.
tests/: The tests for the project.README.md: The README file for the project.pyproject.toml: Contain the python requirements and provides specific for a python environment.Dockerfile: Dockerfile to launch the Borehole App as API.
main.py: This is the main script of the project. It runs the data extraction pipeline, which analyzes the PDF files in thedata/Benchmarkdirectory and saves the results in thepredictions.jsonfile.
The API for this project is built using FastAPI, a modern, fast (high-performance), web framework for building APIs with Python.
To launch the API and access its endpoints, follow these steps:
- Activate the virtual environment
Activate your virtual environment. On Unix systems, this can be done with the following command:
source env/bin/activatePlease make sure to define the environment variables needed for the API to access the S3 Bucket of interest.
AWS_ACCESS_KEY_IDAWS_SECRET_ACCESS_KEYAWS_ENDPOINT, in the formathttps://s3.<RegionName>.amazonaws.com- During local development, a S3-compatible service like MinIO can be used. In this case, the endpoint will look like
http://minio:9000.
- During local development, a S3-compatible service like MinIO can be used. In this case, the endpoint will look like
AWS_S3_BUCKET
The data extraction API in this repository is designed to be integrated into swissgeol-boreholes-suite that is configured by swissgeol-boreholes-config. You can find the AWS S3 bucket configuration used for that deployment in charts/swissgeol-boreholes/values.yaml.
- Start the FastAPI server
Run the following command to start the FastAPI server:
uvicorn src.app.main:app --reload --host 0.0.0.0 --port 8002This will start the server on port 8002 of the localhost and enable automatic reloading whenever changes are made to the code. You can see the OpenAPI Specification (formerly Swagger Specification) by opening: http://127.0.0.1:8002/docs#/ in your favorite browser.
- Access the API endpoints
Once the server is running, you can access the API endpoints using a web browser or an API testing tool like Postman.
The main endpoint for the data extraction pipeline is http://localhost:8000/extract-data. You can send a POST request to this endpoint with the PDF file you want to extract data from.
Additional endpoints and their functionalities can be found in the project's source code.
Note: Make sure to replace localhost with the appropriate hostname or IP address if you are running the server on a remote machine.
- Stop the server
To stop the FastAPI server, press Ctrl + C in the terminal where the server is running. Please refer to the FastAPI documentation for more information on how to work with FastAPI and build APIs using this framework.
The borehole application offers a given amount of functionalities (extract text, number, and coordinates) through an API. To build this API using a Docker Container, you can run the following commands.
- Navigate to the project directory
Change your current directory to the project directory:
cd swissgeol-boreholes-dataextraction- Build the Docker image
Build the Docker image using the following command:
docker build -t borehole-api . -f Dockerfiledocker build --platform linux/amd64 -t borehole-api:test .This command will build the Docker image with the tag borehole-api.
- Verify the Docker image
Verify that the Docker image has been successfully built by running the following command:
docker imagesYou should see the borehole-api image listed in the output.
- Run the Docker container
4.1. Run the Docker Container without concerning about AWS Credentials
To run the Docker container, use the following command:
docker run -p 8000:8000 borehole-apiThis command will start the container and map port 8000 of the container to port 8000 of the host machine.
4.2. Run the docker image with the AWS credentials
You should pass AWS credentials and S3 configuration as environment variables when starting the Docker container.
4.2.1. Using a .env file
Adapt the .env.template file to your needs, by renaming the file to .env and adding your AWS credentials to the file.
The values from the .env file are automatically loaded into your Python environment thanks to the python-dotenv package.
To ensure that the values from the .env file are also passed along when starting a Docker container, you can use the --env-file argument, for example:
docker run --env-file .env -d -p 8000:8000 borehole-api4.2.2. Defining the environment variables in your shell
For example, on Unix-based systems (Linux/macOS), add the following lines to your ~/.bashrc, ~/.bash_profile, or ~/.zshrc (depending on your shell):
export AWS_ACCESS_KEY_ID=your_access_key_id
export AWS_SECRET_ACCESS_KEY=your_secret_access_key
export AWS_ENDPOINT=your_endpoint_url
export AWS_S3_BUCKET=your_bucket_nameAfter editing, run the following command to apply the changes:
source ~/.bashrc # Or ~/.bash_profile, ~/.zshrc based on your configuration- Access the API
Once the container is running, you can access the API by opening a web browser and navigating to http://localhost:8000.
You can also use an API testing tool like Postman to send requests to the API endpoints.
Note: If you are running Docker on a remote machine, replace localhost with the appropriate hostname or IP address.
- Query the API
curl -X 'POST' \
'http://localhost:8000/api/V1/create_pngs' \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
"filename": "10021.pdf"
}'- Stop the Docker container
To stop the Docker container, press Ctrl + C in the terminal where the container is running.
Alternatively, you can use the following command to stop the container:
docker stop <container_id>Replace <container_id> with the ID of the running container, which can be obtained by running docker ps.
This repository provides a Docker image hosted in the GitHub Container Registry (GHCR) that can be used to run the application easily. Below are the steps to pull and run the Docker image.
- Pull the Docker Image from the GitHub Container Registry
docker pull ghcr.io/swisstopo/swissgeol-boreholes-dataextraction-api:edge- a. Run the docker image from the Terminal
docker run -d --name swissgeol-boreholes-dataextraction-api -e AWS_ACCESS_KEY_ID=XXX -e AWS_SECRET_ACCESS_KEY=YYY -e AWS_ENDPOINT=ZZZ -e AWS_S3_BUCKET=AAA -p 8000:8000 ghcr.io/swisstopo/swissgeol-boreholes-dataextraction-api:TAGWhere XXX, YYY, ZZZ, AAA, and TAG are placeholder values that users should replace with their actual credentials and desired tag.
Adjust the port mapping (8000:8000) based on the app's requirements.
NOTE: Do not forget to specify your AWS Credentials.
- b. Run the docker image from the Docker Desktop App
Open the Docker Desktop app and navigate to Images, you should be able to see the image you just pulled from GHCR. Click on the image and click on the Run button on the top right of the screen.
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Then open the Optional Settings menu and specify the port and the AWS credentials
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- Verify the Container is Running
To check if the container is running, use:
docker psWe perform experiment tracking using MLFlow. Each developer has his own local MLFlow instance.
In order to use MLFlow, you will need to place a .env file at the project root. The required environment variables specified in the .env are:
MLFLOW_TRACKING="True"
MLFLOW_TRACKING_URI="http://localhost:5000"
If the MLFLOW_TRACKING variable is not set, no experiments will be logged.
In order to view your experiment, start the mlflow server using mlflow ui in your terminal.
Extensive documentation about MLFlow can be found here.
We use pre-commit hooks to format our code in a unified way.
Pre-commit comes in the boreholes-dev conda environment. After activating the conda environment you have to install pre-commit by running pre-commit install in your terminal. You only have to do this once.
After installing pre-commit, it will trigger 'hooks' upon each git commit -m ... command. The hooks will be applied on all the files in the commit. A hook is nothing but a script specified in .pre-commit-config.yaml.
We use ruffs pre-commit package for linting and formatting.
The specific linting and formatting settings applied are defined in pyproject.toml.
If you want to skip the hooks, you can use git commit -m "..." --no-verify.
More information about pre-commit can be found here.