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Explore datasets and pre-processing code developed for the 3D-PV-Locator paper.

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3D-PV-Locator: Data Pre-processing Repository

Explore the datasets and CityGML pre-processing code developed for the 3D-PV-Locator paper. When building on this work, please cite our work as indicated below.

Demo Notebooks

This repository provides you with three demo notebooks.

Explore_Dataset.ipynb

This notebook allows you to visualize the locations of all the images in our classification dataset on an interactive map.

For example, the locations of non-PV images in our training dataset are illustrated below:

Interactive Map

Download_Images_from_OpenNRW.ipynb

This notebook allows you to create your own dataset by downloading images from the openNRW server directly.

Extract_Rooftop_Information_from_CityGML.ipynb

This notebook demonstrates the code to extract 3D rooftop information from the CityGML files provided by the state of North Rhine-Westphalia (here)

For example, after processing the exemplary .gml files in data/GML/, we can load the extracted rooftop polygons with their respective attributes in QGIS:

Processed CityGML Output

Dependencies

The repository requires the packages GeoPandas, Scipy, Geopy, Pandas, Numpy, Lxml, and related packages used.

BibTex Citation:

Please cite our work as

@article{MAYER2022,
title = {3D-PV-Locator: Large-scale detection of rooftop-mounted photovoltaic systems in 3D},
journal = {Applied Energy},
volume = {310},
pages = {118469},
year = {2022},
issn = {0306-2619},
doi = {https://doi.org/10.1016/j.apenergy.2021.118469},
url = {https://www.sciencedirect.com/science/article/pii/S0306261921016937},
author = {Kevin Mayer and Benjamin Rausch and Marie-Louise Arlt and Gunther Gust and Zhecheng Wang and Dirk Neumann and Ram Rajagopal},
keywords = {Solar panels, Renewable energy, Image recognition, Deep learning, Computer vision, 3D building data, Remote sensing, Aerial imagery},
}

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MIT

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Explore datasets and pre-processing code developed for the 3D-PV-Locator paper.

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