3D-PV-Locator

Repo with documentation for "3D-PV-Locator: Large-scale detection of rooftop-mounted photovoltaic systems in 3D" published in Applied Energy.

In case you would like to explore the code with which we created the image datasets and pre-processed the CityGML files, please have a look at the following GitHub repo.

About

3D-PV-Locator is a joint research initiative between Stanford University, University of Freiburg, and LMU Munich that aims at democratizing and accelerating the access to photovoltaic (PV) systems data in Germany and beyond.

To do so, we have developed a computer vision-based pipeline leveraging aerial imagery with a spatial resolution of 10 cm/pixel and 3D building data to automatically create address-level and rooftop-level PV registries for all counties within Germany's most populous state North Rhine-Westphalia.

Address-level registry

For every address equipped with a PV system in North Rhine-Westphalia, the automatically produced address-level registry in GeoJSON-format specifies the respective PV system's:

geometry: Real-world coordinate-referenced polygon describing the shape of the rooftop-mounted PV system
area_inter: The total area covered by the PV system in square meters
area_tilted: The total area covered by the PV system in square meters, corrected by the respective rooftop tilt
capacity_not_tilted_area: The total PV capacity in kWp of area_inter
capacity_titled_area: The total PV capacity in kWp of area_tilted
location of street address in latitude and longitude
street address
city and
ZIP code

Rooftop-level registry

For every rooftop equipped with a PV system in North Rhine-Westphalia, the automatically produced rooftop-level registry in GeoJSON-format specifies the respective PV system's:

Azimuth: Orientation of the rooftop-mounted PV system, with 0° pointing to the North
Tilt: Tilt of the rooftop-mounted PV system, with 0° being flat
RoofTopID: Identifier of the respective rooftop
geometry: Real-world coordinate-referenced polygon describing the shape of the rooftop-mounted PV system
area_inter: The total area covered by the PV system in square meters
area_tilted: The total area covered by the PV system in square meters, corrected by the respective rooftop tilt
capacity_not_tilted_area: The total PV capacity in kWp of area_inter
capacity_titled_area: The total PV capacity in kWp of area_tilted
street address
city and
ZIP code

For a detailed description of the underlying pipeline and a case study for the city of Bottrop, please have a look at our spotlight talk at NeurIPS 2020:

You might also want to take a look at other projects within Stanford's EnergyAtlas initiative:

EnergyAtlas
DeepSolar for Germany: Publication and Code

Datasets and pre-processing code are public

Please note that apart from the pipeline code and documentation, we also provide you with

A pre-trained model checkpoint for PV classification on aerial imagery with a spatial resolution of 10cm/pixel.
A pre-trained model checkpoint for PV segmentation on aerial imagery with a spatial resolution of 10cm/pixel.
A 100,000+ image dataset for PV system classification.
A 4,000+ image dataset for PV system segmentation.
Pre-processed 3D building data in .GeoJSON format for the entire state of North Rhine-Westphalia.

In case you would like to explore the code with which we created the image datasets and pre-processed the CityGML files, please have a look at the following GitHub repo.

When using these resources, please cite our work as specified at the bottom of this page.

NOTE: All images and 3D building data is obtained from openNRW. Labeling of the images for PV system classification and segmentation has been conducted by us.

Usage Instructions:

git clone https://github.com/kdmayer/3D-PV-Locator.git
cd 3D-PV-Locator

Download pre-trained classification and segmentation models for PV systems from our public AWS S3 bucket. This bucket is in "requester pays" mode, which means that you need to configure your AWS CLI before being able to download the files. Instructions on how to do it can be found here.

Once you have configured your AWS CLI with

aws configure

you can list and browse our public bucket with

aws s3 ls --request-payer requester s3://pv4ger/

Please download our pre-trained networks for PV system classification and segmentation by executing

aws s3 cp --request-payer requester s3://pv4ger/NRW_models/inceptionv3_weights.tar models/classification/
aws s3 cp --request-payer requester s3://pv4ger/NRW_models/deeplabv3_weights.tar models/segmentation/

Next, set up your conda environment with all required dependencies by executing

conda env create --file environment.yml
conda activate pv4ger

Lastly, to create PV registries for any county within North Rhine-Westphalia, you need to

Download the 3D building data for your desired county from our S3 bucket by executing and replacing <YOUR_DESIRED_COUNTY.geojson> with a county name from the list below:

 aws s3 cp --request-payer requester s3://pv4ger/NRW_rooftop_data/<YOUR_DESIRED_COUNTY.geojson> data/nrw_rooftop_data/

Example for the county of Essen:

 aws s3 cp --request-payer requester s3://pv4ger/NRW_rooftop_data/Essen.geojson data/nrw_rooftop_data/

Specify the name of your desired county for analysis in the config.yml next to the "county4analysis" element by choosing one of the counties from the list below:

Example:
```
 county4analysis: Essen
```
OPTIONAL STEP: Obtain your Bing API key for geocoding from here and paste it in the config.yml file next to the "bing_key" element

Example:
```
 bing_key: <YOUR_BING_KEY>
```
NOTE: If you leave <YOUR_BING_KEY> empty, geocoding will be done by the free OSM geocoding service.

Put a "1" next to all the pipeline steps that you would like to run

Example:

 run_tile_creator: 1

 run_tile_downloader: 1

 run_tile_processor: 1

 run_tile_coords_updater: 0

 run_registry_creator: 1

and execute run_pipeline.py in the root directory.

List of available counties:

Please choose the county you would like to run the pipeline for from the following list:

Düren
Essen
Unna
Mönchengladbach
Solingen
Dortmund
Gütersloh
Olpe
Steinfurt
Bottrop
Coesfeld
Leverkusen
Köln
Soest
Mülheim-a.d.-Ruhr
Münster
Heinsberg
Oberhausen
Euskirchen
Krefeld
Warendorf
Recklinghausen
Bochum
Rhein-Kreis-Neuss
Rheinisch-Bergischer-Kreis
Herne
Kleve
Bonn
Minden-Lübbecke
Herford
Rhein-Sieg-Kreis
Düsseldorf
Hagen
Paderborn
Wuppertal
Oberbergischer-Kreis
Viersen
Rhein-Erft-Kreis
Märkischer-Kreis
Städteregion-Aachen
Remscheid
Mettmann
Lippe
Ennepe-Ruhr-Kreis
Hochsauerlandkreis
Gelsenkirchen
Höxter
Borken
Hamm
Bielefeld
Duisburg
Siegen-Wittgenstein
Wesel

OpenNRW Platform:

For the German state of North Rhine-Westphalia (NRW), OpenNRW provides:

Aerial imagery at a spatial resolution of 10cm/pixel
Extensive 3D building data in CityGML format

License:

MIT

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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