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Automated calibration of the InVEST urban cooling model with simulated annealing

Project description

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InVEST urban cooling model calibration


Automated calibration of the InVEST urban cooling model with simulated annealing

See the user guide for more information.


This library requires specific versions of the gdal and rtree libraries, which can easily be installed with conda as in:

$ conda install -c conda-forge 'gdal<3.0' rtree 'shapely<1.7.0'

Then, this library can be installed as in:

$ pip install invest-ucm-calibration

An alternative for the last step is to clone the repository and install it as in:

$ git clone
$ python install


  • 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)
  • Test calibration based on cc_method='intensity'
  • 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

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