Nowadays, photovoltaic panels are playing an increasingly important role in the global production of electrical energy. Unfortunately, since anyone owning a roof could potentially install PV panels, it is quite hard to assess their geographical deployement and, as a consequence, their impact on the electrical grids.

Therefore, this project, named Automatic Detection Of Photovoltaic Panels Through Remote Sensing or ADOPPTRS, aims to detect photovoltaic panels in high-resolution satellite images.

More specifically, the goal is to detect, as accurately as possible, photovoltaic panels in the WalOnMap orthorectified images in the Province of Liège.

For further explanations and technicalities, please see the project report.

All the photovoltaic installations that have been detected, can be visualized at francois-rozet.github.io/adopptrs.

The PyTorch library has been used to implement and train several neural networks models one of which is the well known U-Net: Convolutional Networks for Biomedical Image Segmentation.

For a short description of the arguments of the scripts (train.py, evaluate.py, etc.), use --help.

If you wish to run the scripts or the Jupyter notebook(s), you will need to install several Python packages including jupyter, torch, torchvision, opencv, matplotlib and their dependencies.

To do so safely, one should create a new environment :

virtualenv ~/adopptrs -p python3
source ~/adopptrs/bin/activate
pip3 install -r requirements.txt -y

or with the conda package manager

conda env create -f environment.yml
conda activate adopptrs

The neural networks that have been implemented (cf. models.py) are U-Net, SegNet and Multi-Task versions of them.

The legacy networks are trained with a Dice loss while the multi-task ones are trained with a Multi-Task loss (cf. criterions.py).

During training, the dataset is augmented, meaning that each image undergoes a different random transformation at each epoch. The transformation is a combination of rotations (90°, 180° or 270°), flips (horizontal or vertical), brightness alteration, contrast alteration, saturation alteration, blurring, smoothing, sharpening, etc.

This improves greatly the robustness of the networks.

In order to produce the networks and plots that are presented in the notebooks, the scripts train.py and evaluate.py were used. For instance, to train Multi-Task U-Net on 5 folds (except fold 0) for 20 epochs and then evaluate it on fold 0 :

python train.py -m unet -multitask -n multiunet_0 -e 20 -s multiunet.csv -k 5 -f 0
python evaluate.py -m unet -multitask -n ../products/models/multiunet_0_020.pth -k 5 -f 0

The output of evaluate.py is not very user friendly, it should be improved in a future version.

Concerning the model used for fine tuning, the images were twice upscaled and the whole Californian training set was used.

python train.py -m unet -multitask -n multiunet_x2 -e 20 -scale 2 -s multiunet_x2.csv -k 0

Then it was fine tuned for 10 more epochs on 661 hand-annotated images.

python misc/download.py -d ../products/liege/ -i ../resources/walonmap/via_liege_city.json
python train.py -m unet -multitask -n multiunet_x2 -e 10 -r 21 -scale 2 -batch 2 -special -p ../products/liege/ -i ../resources/walonmap/via_liege_city.json -s multiunet_x2.csv -k 0

Note the use of the flag -special that removes images cropping and data augmentation.

Afterwards, the fine-tuned model was applied to every images in the Province of Liège.

python walonmap.py -m unet -multitask -n ../products/models/multiunet_x2_030.pth -p ../resources/walonmap/liege_province.geojson -o ../products/json/liege_province_via.json

Finally, the resulting liege_province_via.json file was "summarized" using

python summarize.py -i ../products/json/liege_province_via.json -o liege_province.csv

which produced the liege_province.csv file.

For training our models, we used the Distributed Solar PV Array Location and Extent Data Set for Remote Sensing Object Identification provided by Duke University Energy Initiative.

This dataset contains the geospatial coordinates and border vertices for over 19 000 solar panels across 601 high resolution images from four cities in California.

wget "https://ndownloader.figshare.com/articles/3385780/versions/3" -O polygons.zip
wget "https://ndownloader.figshare.com/articles/3385828/versions/1" -O Fresno.zip
wget "https://ndownloader.figshare.com/articles/3385789/versions/1" -O Modesto.zip
wget "https://ndownloader.figshare.com/articles/3385807/versions/1" -O Oxnard.zip
wget "https://ndownloader.figshare.com/articles/3385804/versions/1" -O Stockton.zip
mkdir -p resources/california/
unzip polygons.zip -d resources/california/
unzip Fresno.zip -d resources/california/
unzip Modesto.zip -d resources/california/
unzip Oxnard.zip -d resources/california/
unzip Stockton.zip -d resources/california/
rm *.zip resources/california/*.xml # optionally

Afterwards, the file SolarArrayPolygons.json has to be converted to the VGG Image Annotator format.

python3 python/dataset.py --output products/json/california.json --path resources/california/