RID - Roof information dataset

This code refers to the publication "RID—Roof Information Dataset for Computer Vision-Based Photovoltaic Potential Assessment", https://doi.org/10.3390/rs14102299.

This repository includes datasets for semantic segmentation of roof superstructures and roof roof segments. The code supports data preparation, training and model evaluation. Furthermore, it includes the labels of an annotation experiment with five labelers and its evaluation.

Getting Started

• First clone the repository
• Download required raster data (images and masks) from https://doi.org/10.14459/2022mp1655470
• Create a folder with the name "segmentation_model_data" and copy "filenames_train_val_test_split" to this folder
• Copy all other data folders into the "data" folder

Prerequisites

Required packages are included in the requirements.txt.

Installing

When running the code on Windows, packages fiona, gdal, and geopandas need to be installed from wheel files. When using Python 3.7, package dataclasses is already included in python and can be deleted from requirements.txt. Code can be used with Python 3.9 by installing packages from requirements_python_39.txt

Running the Model/Code

The whole pipeline can be run using main.py. The steps include:

1. Create roof superstructure masks from vector labels
2. Create roof segment masks from vector labels
3. Analyze the dataset
4. Evaluate annotation experiment and visualize results
5. Train model for semantic segmentation of superstructure
6. Evaluate model and visualize results
7. Conduct PV potential assessment

The pipeline can be run using different input datasets:

1. initial labels (with inferior label quality)
2. reviewed labels (with enhanced label quality)

Settings should be defined in definitions.py

It is recommended to run parts of the pipeline seperately, e.g. when training the model for semantic segmentation of roof segments, or when optimizing the model parameters.

Deployment

Built With Python 3.6

Versioning

V0.1 Initial version

Authors

Author: Sebastian Krapf

Contributors

Fabian Netzler, Lukas Bogenrieder, Nils Kemmerzell

Credits

This work would not have been possible without numerous python packages such as keras, segmentation models, shapely, geopandas, and more. See requirements.txt for packages used.

License

1. This project is licensed under the LGPL License - see the LICENSE.md file for details

2. The use of Google Satelite Images is allowed under principles of fair use. Images can be used for non-commercial purposes such as news, blogs, educational, recreational, or instructional use. For information, see: https://about.google/brand-resource-center/products-and-services/geo-guidelines/