brails.inferers.nlcd_inferer.nlcd_to_coarse_land_cover module

Ruleset that maps USGS NLCD classes to a coarser land cover categories.

NLCDToCoarseLandCover()

Convert detailed USGS NLCD classes to a set of coarse categories.

class brails.inferers.nlcd_inferer.nlcd_to_coarse_land_cover.NLCDToCoarseLandCover

Bases: InferenceEngine

Convert detailed USGS NLCD classes to a set of coarse categories.

This class maps USGS National Land Cover Dataset (NLCD) classes to one of five coarse categories: 'Open', 'Light Suburban', 'Suburban', 'Light Trees', or 'Trees'.

It operates exclusively on the 'land_cover' field of each asset in an inventory and adds a new field 'LandCover' containing the mapped coarse category. Missing or unmapped values are assigned a placeholder (default: 'Unknown').

To use NLCDToCoarseLandCover, include the following lines in your code:

from brails import Importer

importer = Importer()
nlcd_inferer = importer.get_class(NLCDToCoarseLandCover)

infer(input_inventory)

Update each asset in the inventory with a coarse land cover category.

The method reads the 'land_cover' field of each asset, maps it to a coarse category using the provided mapping, and writes the result to the 'coarse_land_cover' field. If the original value is missing or unmapped, a placeholder is used.

Parameters:

asset_inventory (AssetInventory) – An inventory object containing assets with a 'land_cover' field.

Example

>>> from brails import Importer
>>> from brails.types.asset_inventory import Asset, AssetInventory
>>> importer = Importer()
>>> inv = AssetInventory()
>>> _ = inv.add_asset('A1', Asset(
...     'A1',
...     [[-105.0, 40.0]],
...     {'land_cover': 'Evergreen Forest'}
... ))
>>> _ = inv.add_asset('A2', Asset(
...     'A2',
...     [[-104.9, 39.5]],
...     {'land_cover': 'Grasslands/Herbaceous'}
... ))
>>> _ = inv.add_asset('A3', Asset(
...     'A3',
...     [[-105.1, 39.7]],
...     {'land_cover': 'Desert'}  # unmapped
... ))
>>> _ = inv.add_asset('A4', Asset(
...     'A4',
...     [[-104.8, 40.2]],
...     {}  # missing
... ))
>>> nlcd_inferer = importer.get_class('NLCDToCoarseLandCover')()
>>> updated_inv = nlcd_inferer.infer(inv)
>>> for asset_id, asset in updated_inv.inventory.items():
...     print(asset_id, asset.features['LandCover'])
A1 Trees
A2 Open
A3 Unknown
A4 Unknown