This repository implements some ideas from PIGEON: Predicting Image Geolocations, Haas et al. 2023. It starts from GADM administrative boundary data (link to the needed GeoPackage) and then uses the distribution of your geotagged training set in order to build geocells (i.e. geometries) that are relevant in terms of point density and structure, increasing the cell resolution where needed.

Notice that the point dataset needs to be a .csv with lat (latitude) and lng (longitude) as columns. In order to get the final result, the order of calls is the following:

1. init_cells
2. merge_cells
3. cluster_split

If needed, the function geocell_centroid can be useful to retrieve the centroids for each computed cell.