Source code for torchgeo_bench.datasets.flair2

"""FLAIR2 (GeoBench V2) benchmark dataset."""

from .base import BandSpec
from .geobench_v2 import _V2Dataset


[docs] class FLAIR2(_V2Dataset): """Aerial land-cover segmentation (13 classes). French aerial imagery with RGB, NIR, and elevation bands. The upstream ``GeoBenchFLAIR2`` accepts a flat ``band_order`` list and returns a single stacked ``image`` tensor, so this wrapper does **not** use the multi-modality dict shape. """ name = "flair2" task = "segmentation" num_classes = 13 multilabel = False rgb_bands = ["red", "green", "blue"] split_sizes = {"train": 4049, "val": 1022, "test": 3022} # fmt: off bands = [ # IGN BD ORTHO centre wavelengths (R/G/B/NIR); elevation is non-spectral. BandSpec("aerial", "red", "red", mean=111.395, std=51.2846, min=0, max=255, wavelength_um=0.66), BandSpec("aerial", "green", "green", mean=115.788, std=45.1, min=0, max=255, wavelength_um=0.55), BandSpec("aerial", "blue", "blue", mean=106.896, std=44.4006, min=0, max=255, wavelength_um=0.48), BandSpec("aerial", "nir", "nir", mean=104.085, std=39.566, min=0, max=255, wavelength_um=0.83), BandSpec("elevation", "elevation", "elevation", mean=17.7749, std=30.34, min=0, max=255), ]
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