GeoJSON Cleaner Dashboard

A prototype of Streamlit app for automated cleaning and geometry fixing of GeoJSON files and previewing on a leaflet map.

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Features

• Upload a GeoJSON file.
• Clean it by removing invalid features or properties.
• Explore logs with transparent, user-friendly error reporting.
• Preview the cleaned GeoJSON on a map.
• Download the cleaned version.

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Project Index

dashboard.py	❯ Main Streamlit app script, handles page layout, file upload, shows logs, attribute tables, map preview and download button
utils.py	❯ Helper functions for fixing GeoJSON geometries, performing validation, removing duplicates and creating a folium map
Farm_file.geojson	❯ Sample input GeoJSON file
sample_output.geojson	❯ Sample of a cleaned GeoJSON file produced by the app
requirements.txt	❯ List of all needed Python dependencies
Dockerfile	❯ Instructions for building a Docker container image

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🚀 Installation & Running

1. Clone the repo

   git clone https://github.com/viktor-ko/GeoJSON_Cleaner_Dashboard.git
   cd GeoJSON_Cleaner_Dashboard

2. Create and activate a virtual environment

   python -m venv venv
   source venv/bin/activate   # On Windows: venv\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt

4. Run the Streamlit app

   streamlit run dashboard.py

   Open http://localhost:8501 in your browser.

🐳Docker Containerization📦

To containerize the Streamlit app using Docker on a Windows machine, follow these steps:

1️⃣ Install Docker Desktop

Download it here: https://www.docker.com/get-started
Run it after installation — you can skip the account creation if you don’t need Docker Hub.

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2️⃣ Create a requirements.txt file

List all required Python packages with versions. For this app:

folium==0.19.5
geopandas==1.0.1
shapely==2.0.7
streamlit==1.44.0
streamlit_folium==0.24.0

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3️⃣ Create a Dockerfile

This file contains instructions to build the Docker image.

Dockerfile

FROM python:3.13-slim
WORKDIR /dashboard
COPY dashboard.py utils.py requirements.txt ./
RUN pip install --no-cache-dir -r requirements.txt
EXPOSE 8501
CMD ["bash", "-c", "echo 'Use this URL: http://localhost:8501' && streamlit run dashboard.py --server.port=8501 --server.address=0.0.0.0"]

Explanation:

• FROM — base image, use a smaller version of Python 3.13 to reduce the Docker image size.
• WORKDIR — container working directory
• COPY — copy app files and requirements.txt into container. ./ refers to the current directory
• RUN — install Python packages. --no-cache-dir ignores packages cache, which reduces Docker image size
• EXPOSE — open port 8501 (Streamlit default)
• CMD — run the app and print access URL

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4️⃣ Create a .dockerignore file (optional)

If you're using PyCharm or other IDEs, exclude .idea or other unneeded folders:

.idea

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5️⃣ Build the Docker image

Run in terminal where your Dockerfile is:

docker build -t geojson-dashboard .

. at the end - current directory. If it built successfully, you should see the messageFINISHEDin the terminal.

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6️⃣ Verify the Docker image

docker images

List all Docker images on your machine. You should see geojson-dashboard in the list of images.

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7️⃣ Run the Docker container

docker run -p 8501:8501 geojson-dashboard

Now open http://localhost:8501 in your browser.

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8️⃣ Save the Docker image to a .tar file

docker save -o geojson-dashboard.tar geojson-dashboard

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📦 Load the image on another machine

Copy the .tar file to the target machine, then run:

docker load -i geojson-dashboard.tar

And start the container:

docker run -p 8501:8501 geojson-dashboard

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✅ Done

That’s it — GeoJSON Cleaner Dashboard is containerized and portable!