building-to-parcel-training

building2parcel-training is a Python package for mapping parcels and buildings, designed to assist in training models to associate buildings with their corresponding parcels. It provides functionality for loading, processing, and visualizing geospatial data for parcels and buildings.

Features

• Load and process parcel and building data from shapefiles or geoJSON
• Optional loading and processing of block data
• Join parcel and building data based on spatial relationships
• Split buildings that span multiple parcels
• Generate maps using Mapbox satellite imagery
• Customize map output with various options
• Generate dataset specifications and statistics
• Support for creating training datasets for building-to-parcel association models

Installation

You can install building-to-parcel-training using pip:

pip install building2parcel-trainingdata

For development, clone the repository and install in editable mode:

git clone https://github.com/scalable-design-participation-lab/building-to-parcel-trainingdata.git
cd building2parcel-trainingdata
pip install -e .

Configuration

Before using the package, you need to set up your environment:

1. Create a .env file in the main folder (it will be a hidden file on Unix-based systems).
2. In the .env file, add the following lines:

MAPBOX_ACCESS_TOKEN="YOUR-API-KEY"
LOCAL_PATH="YOUR-DROPBOX-PATH/Million Neighborhoods/"

Replace YOUR-API-KEY with your Mapbox Access Token for the Mapbox Web API, and YOUR-DROPBOX-PATH with the path to your Dropbox folder containing the parcel and building data (NYC data is available on our Dropbox).

Usage

Here's a basic example of how to use building2parcel-training:

from building2parcel_trainingdata import Building2ParcelMapper

# Initialize the mapper with paths to your data
parcels_path = "path/to/your/parcels.shp"
buildings_path = "path/to/your/buildings.shp"
blocks_path = "path/to/your/blocks.shp"  # Optional

mapper = Building2ParcelMapper(parcels_path, buildings_path, blocks_path)

# Split buildings (optional)
mapper.split_buildings(threshold_high=0.75, threshold_low=0.15)

# Assign colors to parcels and buildings
mapper.assign_colors()

# Generate dataset specifications and statistics
mapper.generate_dataset_specs(output_folder='./dataset_specs')

# Generate images
parcel_images_directory = "./parcels_output/"
buildings_images_directory = "./buildings_output/"
number_of_images = 100
mapper.generate_images(parcel_images_directory, buildings_images_directory, number_of_images)

Command-line Usage

The package also provides a command-line interface:

python -m building2parcel_trainingdata --buildings_path path/to/buildings.shp --parcels_path path/to/parcels.shp --blocks_path path/to/blocks.shp --split_buildings True --threshold_high 0.75 --threshold_low 0.15 --parcel_images_directory ./parcels_output/ --buildings_images_directory ./buildings_output/ --number_of_images 100

Requirements

• matplotlib
• cartopy
• geopandas
• python-dotenv
• Pillow
• numpy
• owslib
• tqdm
• pandas

Generating Documentation

To generate documentation for this package, we use pdoc. Follow these steps:

1. Install pdoc if you haven't already:

   pip install pdoc
   

2. Navigate to the directory containing your building2parcel_trainingdata.py file.

3. Run the following command to generate HTML documentation:

   pdoc -o ./docs building2parcel_trainingdata.py
   

   This will create a docs directory and generate HTML documentation inside it.

4. To view the documentation, open ./docs/building2parcel_trainingdata.html in a web browser.

For more comprehensive documentation:

• Ensure all functions and classes have proper docstrings.

• Add a module-level docstring at the top of building2parcel_trainingdata.py.

• To generate documentation for the entire package (if you have multiple Python files):

  pdoc -o ./docs building2parcel_trainingdata
  

• To generate documentation in Markdown format:

  pdoc -o ./docs --format md building2parcel_trainingdata.py
  

Remember to regenerate the documentation after making significant changes to your code or docstrings.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Thanks to Mapbox for providing satellite imagery services.
• This project was developed to support machine learning efforts in associating buildings with their corresponding parcels.