The following people contributed equally to this repository (in alphabetical order):

A presentation of the project is available here.

This repository is forked on https://github.com/jjx3455/solar_berlin and https://github.com/cverst/solar_berlin to reflect the work of all contributors.

Estimating the potential photovoltaic production of Berlin rooftops

Prerequisites

Install Python 3.7 (other versions may work) and the packages listed in requirements.txt
To run the unet model effectively, gpu-accelertion in tensorflow is advisable - https://www.tensorflow.org/install/gpu
To run the Mask R-CNN model additionally install the packages listed in maskr/requirements.txt and maskr/requirements-detectron.txt - gpu-acceleration for pytorch is advisable
It may be necessary to install gdal into your operating system and pin gdal in the requirements.txt to the same version as your system (see https://pypi.org/project/GDAL/)
In order to convert the original raster tiles from ecw to GeoTIFF GDAL must be built with ecw support (see https://trac.osgeo.org/gdal/wiki/ECW)
- Alternatively, skip downloading the tiles and use the pre-processed GeoTIFFs available as converted.zip (72GB)
- Alternatively 2, skip the extraction too and use the pre-extracted sample data available as extracted.zip (45GB)

Getting the original data

To download and convert the original data:

Create a subfolder "data" in the repository
If not using the pre-processed GeoTIFFs, download the original data:
- Download map and vector files: cd conversion && sh download.sh
- Convert from ecw to GeoTIFF: sh convert_rasters.sh
- Clip the vector map to the same size as the raster tiles: python clip_shape.py

Extracting the data for the model

To extract the data into usable map/mask tiles and select a sample for the model:

Adjust parameters in select_data.py for desired sample size and tile size
Run python select_data.py
(This will take a while as it extracts all tiles of that size from the original data. The tiles are saved in the data/extracted folder so sampling again from the same tiles will be much faster.)

Running the current models

Two models can be run on the available data.

The first is a U-Net model with a pre-trained MobileNetV2 as the backbone for semantic segementation of rooftops (pixel-by-pixel prediction of roof/no-roof).

To run the unet model:

If not using the pre-extracted sample tiles:
- modify the parameters in run_model.py to point at a different data selection folder
- Run python run_model.py
- Inspect results in logs The best model is unet/unet_resnet101v2.py

The second is a Detectron2 Mask R-CNN model with a pre-trained Resnet50 backbone for instance segmentation of roofs along with their PV category.

To run the model (Mask R-CNN):

Run the model:
- modify the parameters in maskr/run_mask_r_cnn.py to point at a different data selection folder
- Run python maskr/run_mask_r_cnn.py
Generate predictions:
- modify the parameters in predict_from_mask_r_cnn.py
- Run python predict_from_mask_r_cnn.py

Running Tests

Data for testing is managed with DVC (https://dvc.org/). To run the tests:

Download the test data dvc pull
Run python run_tests.py

moreshiny/solar_berlin

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