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A set of utilities for manipulating (Geo)JSON and GeoTIFF data.

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PyGeoUtils is a part of Hydrodata software stack and provides utilities for manipulating (Geo)JSON and GeoTIFF data:

  • 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 function handle all necessary CRS transformations. 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):

$ pip install pygeoutils

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

$ conda install -c conda-forge pygeoutils


To demonstrate capabilities of PyGeoUtils lets use PyGeoOGC to access National Wetlands Inventory from WMS, and FEMA National Flood Hazard via WFS, then convert the outpus to GeoDataFrame and xarray.Dataset using PyGeoUtils.

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

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

url_wms = ""
layer = "0"
r_dict = wms_bybox(

wetlands = geoutils.gtiff2xarray(r_dict, geometry, "epsg:4326")

url_wfs = ""
wfs = WFS(
bbox = geometry.bounds
bbox = (bbox[1], bbox[0], bbox[3], bbox[2])
r = wfs.getfeature_bybox(bbox, box_crs="epsg:4326")
flood = geoutils.json2geodf(r.json(), "epsg:4269", "epsg:4326")


Contirbutions are very welcomed. Please read CODE_OF_CONDUCT.rst and CONTRIBUTING.rst files for instructions.

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