osm_completeness

This series of notebooks builds a model for predicting OSM building footprint area using random forest regression.

First, an Observable notebook is used to identify those areas of a given region that already have complete OSM building mapping.Those areas are used as a training set for building the regression model. The order of notebook execution is:

1. TrainOSM.ipynb — create and save a random forest regression model that predicts OSM building footprint area
2. SplitArea.ipynb — create 250-m cell polygons over a region of interest from a shapefile
3. Enrich.ipynb — calculate features over each cell using Google Earth Engine and rasterio
4. ApplyModel.ipynb — run the regions of cells through the trained model to get predicted OSM footprint area

Results of ApplyModel.ipynb can be visualized in a second Observable notebook. Be aware of a 15 MB limit to the file that can be attached to the notebook.

The conda environment necessary to run the Jupyter notebooks can be installed using the file envs/wbenv.yml. To build it enter:
conda env create --name envname --file wbenv.yml
in the the anaconda terminal when inside the directory containing the yml file. Windows machines may encounter an issue with the rtree dependency for geopandas, specifically the error 'OSError: could not find or load spatialindex_c-64.dll' when importing geopandas. The workaround is to find the core.py file in the rtree library and change the line:

elif 'conda' in sys.version:

to

elif os.path.exists(os.path.join(sys.prefix, 'conda-meta')):