Welcome to the documentation of the Earth Observation Data Analysis Library (EOdal)

Sentinel-2 false color image of Lake Lucerne, Switzerland.

A truely open-source package for unified analysis of Earth Observation (EO) data.

  • cloud-native by design thanks to STAC

  • access to petabytes of satellite imagery including Landsat, Sentinel-1, Sentinel-2 and PlanetScope

  • EO data querying, I/O, processing, analysis and visualization in a single package

  • Modulare and lightweight architecture

  • Almost unlimited expandability with interfaces to xarray, numpy, geopandas, and many more

Getting started

EOdal can be installed from PyPi or from source.

To get started you might have a look at some Jupyter notebooks we prepared. Check them out here

Example

eodal_example.py
import geopandas as gpd

 from datetime import datetime
 from eodal.core.sensors.sentinel2 import Sentinel2
 from eodal.mapper.feature import Feature
 from eodal.mapper.filter import Filter
 from eodal.mapper.mapper import Mapper, MapperConfigs
 from typing import List


 #%% user-inputs
 # -------------------------- Collection -------------------------------
 collection: str = 'sentinel2-msi'

 # ------------------------- Time Range ---------------------------------
 time_start: datetime = datetime(2022,3,1)           # year, month, day (incl.)
 time_end: datetime = datetime(2022,6,30)            # year, month, day (incl.)

 # ---------------------- Spatial Feature  ------------------------------
 geom: Path = Path('data/sample_polygons/lake_lucerne.gpkg')

 # ------------------------- Metadata Filters ---------------------------
 metadata_filters: List[Filter] = [
     Filter('cloudy_pixel_percentage','<', 80),
     Filter('processing_level', '==', 'Level-2A')
 ]

 # query the scenes available (no I/O of scenes, this only fetches metadata)
 feature = Feature.from_geoseries(gpd.read_file(geom).geometry)
 mapper_configs = MapperConfigs(
     collection=collection,
     time_start=time_start,
     time_end=time_end,
     feature=feature,
     metadata_filters=metadata_filters
 )

 # now, a new Mapper instance is created
 mapper = Mapper(mapper_configs)
 # query the metadata (i.e., available scenes) first
 mapper.query_scenes()

 # load the scenes available from STAC (reading bands B02 "blue", B03 "green", B04 "red")
 scene_kwargs = {
     'scene_constructor': Sentinel2.from_safe,
     'scene_constructor_kwargs': {'band_selection': ['B02', 'B03', 'B04'], 'read_scl': False}
 }
 mapper.load_scenes(scene_kwargs=scene_kwargs)

 # the data loaded into `mapper.data` as a EOdal SceneCollection
 mapper.data

A truely open-source package for unified analysis of Earth Observation (EO) data.

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