SolarMapper with MRS (Models for Remote Sensing)

Code base, tutorials and other information for the paper SolarMapper: estimating solar array location, size, and capacity using deep learning and overhead imagery.

Usage

Model training

1. Edit hyper-parameters in config.json or make your custom config file with the same format as config.json.
2. Run python train.py --config CONFIG_PATH if you're using your custom config file and CONFIG_PATH is the path to your config file. Without the --config flag, train.py will look for config.json in the mrs repo directory.

Model evaluation

1. Edit settings parameters (GPU ID, model path, etc.) in evaluate.py. Notice that DS_NAME should match the one in the config file of the trained model.
2. Run python evaluate.py.

Model inference

1. Edit settings parameters in infer.py in the same way as for model evaluation. DS_NAME should match the one in the trained model, too.
2. The FILE_LIST parameter takes the path to a .txt file which contains full paths of testing image files (one file path per row).
3. Run python infer.py.

Tutorials

Dilation-based array grouping

Tile stratified sampling by building density

Array-wise performance evaluation

Connecticut municipality level analysis

Pixel wise prediction visualization

Other links

Connecticut annotated fine-tuning dataset

Web visualization of final predictions over the state of Connecticut

Please refer to the original documentation of the MRS framework for more technical details and more model architectures supported.