PyGeoUtils is a part of HyRiver software stack that is designed to aid in watershed analysis through web services. This package provides utilities for manipulating (Geo)JSON and (Geo)TIFF responses from web services. These utilities are:

  • json2geodf: For converting (Geo)JSON objects to GroPandas dataframe.
  • arcgis2geojson: For converting ESRIGeoJSON objects to standard GeoJSON format.
  • gtiff2xarray: For converting (Geo)TIFF objects to xarray datasets.

All these functions handle all necessary CRS transformations.

You can find some example notebooks here.

Please note that since this project is in early development stages, while the provided functionalities should be stable, changes in APIs are possible in new releases. But we appreciate it if you give this project a try and provide feedback. Contributions are most welcome.

Moreover, requests for additional functionalities can be submitted via issue tracker.


You can install PyGeoUtils using pip after installing libgdal on your system (for example, in Ubuntu run sudo apt install libgdal-dev). Moreover, PyGeoUtils has an optional dependency for using persistent caching, requests-cache. We highly recommend to install this package as it can significantly speedup send/receive queries. You don't have to change anything in your code, since PyGeoUtils under-the-hood looks for requests-cache and if available, it will automatically use persistent caching:

$ pip install pygeoutils

Alternatively, PyGeoUtils can be installed from the conda-forge repository using Conda:

$ conda install -c conda-forge pygeoutils

Quick start

To demonstrate capabilities of PyGeoUtils let's use PyGeoOGC to access National Wetlands Inventory from WMS, and FEMA National Flood Hazard via WFS, then convert the output to xarray.Dataset and GeoDataFrame, respectively.

import pygeoutils as geoutils
from pygeoogc import WFS, WMS
from shapely.geometry import Polygon

geometry = Polygon(
        [-118.72, 34.118],
        [-118.31, 34.118],
        [-118.31, 34.518],
        [-118.72, 34.518],
        [-118.72, 34.118],

url_wms = ""
wms = WMS(
r_dict = wms.getmap_bybox(
wetlands = geoutils.gtiff2xarray(r_dict, geometry, "epsg:4326")

url_wfs = ""
wfs = WFS(
r = wfs.getfeature_bybox(geometry.bounds, box_crs="epsg:4326")
flood = geoutils.json2geodf(r.json(), "epsg:4269", "epsg:4326")