/invest-ucm-calibration

Automated calibration of the InVEST urban cooling model with simulated annealing

Primary LanguagePythonGNU General Public License v3.0GPL-3.0

PyPI version fury.io Documentation Status CI/CD codecov GitHub license

InVEST urban cooling model calibration

Overview

Automated calibration of the InVEST urban cooling model with simulated annealing

Citation: Bosch, M., Locatelli, M., Hamel, P., Remme, R. P., Chenal, J., and Joost, S. 2021. "A spatially-explicit approach to simulate urban heat mitigation with InVEST (v3.8.0)". Geoscientific Model Development 14(6), 3521-3537. 10.5194/gmd-14-3521-2021

See the user guide for more information, or the lausanne-heat-islands repository for an example use of this library in an academic article.

Installation

The easiest way to install this library is using conda (or mamba), as in:

conda install -c conda-forge invest-ucm-calibration

which will install all the required dependencies including InVEST (minimum version 3.11.0). Otherwise, you can install the library with pip provided that all the dependencies (including GDAL) are installed.

TODO

  • Allow a sequence of LULC rasters (although this would require an explicit mapping of each LULC/evapotranspiration/temperature raster or station measurement to a specific date)
  • Support spatio-temporal datasets with xarray to avoid passing many separate rasters (and map each raster to a date more consistently)
  • Read both station measurements and station locations as a single geo-data frame

Acknowledgments

  • The calibration procedure is based simulated annealing implementation of perrygeo/simanneal
  • With the support of the École Polytechnique Fédérale de Lausanne (EPFL)
  • This package was created with the ppw tool. For more information, please visit the project page.