Yes, you should be able to pass a list / tuple (these are iterables) of Shapely geometries as input to geometry_mask.

You need to convert each polygon in your numpy array of coordinates into a GeoJSON-like polygon. You could use Shapely to do so. You should then be able to use those as input to rasterize or geometry_

Ben - I'm not sure I follow your intent here. Is the idea to make the smallest possible geotiff after rasterizing all shapes in test.shp to pixels, where SPARSE_OK omits non-rasterized pixels where po

The target release date that will include this fix is 5/1/2020 (https://github.com/mapbox/rasterio/issues/1911) If you need changes sooner, you have a few options: 1) clone the repo and checkout the m

This looks like an oversight when we implemented bounds(). Rasterio currently supports FeatureCollection or individual geometry objects in this operation. Logged here: https://github.com/mapbox/raster

Nic, if your goal is to mask the data, you can replicate some of the processing steps in the `mask` chain: https://github.com/mapbox/rasterio/blob/master/rasterio/mask.py Specifically, you'd need to f

I'm not entirely sure I follow what you are aiming for here yet. Are you trying to aggregate across features per pixel to calculate various statistics? Like, what is the average feature size of all fe

Hi Philipp, How are you looping over each value to create a new raster? Are you able to share a gist of what you are doing?