# Copyright (c) 2025 GeoBenchV2. All rights reserved.
# Licensed under the Apache License 2.0.
"""TreeSatAI dataset."""
import os
from collections.abc import Callable, Sequence
from typing import Any, Literal
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tacoreader
import torch
import torch.nn as nn
from einops import rearrange
from torch import Tensor
from torchgeo.datasets.utils import percentile_normalization
from geobench_v2.datasets import GeoBenchTreeSatAI
from .base import GeoBenchClassificationDataModule
[docs]
class GeoBenchTreeSatAIDataModule(GeoBenchClassificationDataModule):
"""GeoBench TreeSatAI Data Module."""
[docs]
def __init__(
self,
img_size: int = 304,
band_order: Sequence[float | str] = GeoBenchTreeSatAI.band_default_order,
batch_size: int = 32,
eval_batch_size: int = 64,
num_workers: int = 0,
collate_fn: Callable | None = None,
train_augmentations: nn.Module | None = None,
eval_augmentations: nn.Module | None = None,
pin_memory: bool = False,
**kwargs: Any,
) -> None:
"""Initialize GeoBench TreeSatAI dataset module.
Args:
img_size: Image size originally 304
band_order: The order of bands to return in the sample
batch_size: Batch size
eval_batch_size: Evaluation batch size
num_workers: Number of workers
collate_fn: Collate function
train_augmentations: Training augmentations
eval_augmentations: Evaluation augmentations
pin_memory: Pin memory
**kwargs: Additional keyword arguments for :class:`geobench_v2.datasets.treesatai.GeoBenchTreeSatAI`
"""
super().__init__(
dataset_class=GeoBenchTreeSatAI,
img_size=img_size,
band_order=band_order,
batch_size=batch_size,
eval_batch_size=eval_batch_size,
num_workers=num_workers,
collate_fn=collate_fn,
train_augmentations=train_augmentations,
eval_augmentations=eval_augmentations,
pin_memory=pin_memory,
**kwargs,
)
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def visualize_geospatial_distribution(
self,
split_column="tortilla:data_split",
buffer_degrees: float = 2.0,
scale: Literal["10m", "50m", "110m"] = "50m",
alpha: float = 0.5,
s: float = 0.5,
) -> plt.Figure:
"""Visualize the geospatial distribution of dataset samples on a map.
Creates a plot showing the geographic locations of samples, colored by dataset split
(train, validation, test). This helps to understand the spatial distribution
and potential geographic biases in the dataset.
Args:
split_column: Column name in the metadata DataFrame that indicates the dataset split.
buffer_degrees: Buffer around the data extent in degrees
sample_fraction: Fraction of samples to plot (0.0-1.0) for performance with large datasets
scale: Scale of cartopy features (e.g., '10m', '50m', '110m')
alpha: Transparency of plotted points
s: Size of plotted points
"""
return super().visualize_geospatial_distribution(
split_column=split_column,
buffer_degrees=buffer_degrees,
scale=scale,
alpha=alpha,
s=s,
)
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def visualize_batch(
self, split: str = "train"
) -> tuple[plt.Figure, dict[str, Tensor]]:
"""Visualize a batch of data.
Args:
split: One of 'train', 'validation', 'test'
Returns:
The matplotlib figure and the batch of data
Raises:
AssertionError: If bands needed for plotting are missing
"""
if split == "train":
batch = next(iter(self.train_dataloader()))
elif split == "validation":
batch = next(iter(self.val_dataloader()))
else:
batch = next(iter(self.test_dataloader()))
if hasattr(self.data_normalizer, "unnormalize"):
batch = self.data_normalizer.unnormalize(batch)
batch_size = batch["label"].shape[0]
n_samples = min(8, batch_size)
indices = torch.randperm(batch_size)[:n_samples]
modalities = {}
for mod in self.band_order.keys():
mod_plot_bands = self.dataset_band_config.modalities[mod].plot_bands
missing_bands = [
band for band in mod_plot_bands if band not in self.band_order[mod]
]
if missing_bands:
raise AssertionError(
f"Plotting bands {missing_bands} for modality '{mod}' not found in band_order {self.band_order[mod]}"
)
# Get plot indices for bands that exist
mod_plot_indices = [
self.band_order[mod].index(band) for band in mod_plot_bands
]
mod_images = batch[f"image_{mod}"][:, mod_plot_indices, :, :][indices]
mod_images = rearrange(mod_images, "b c h w -> b h w c").cpu().numpy()
modalities[mod] = mod_images
num_modalities = len(modalities)
fig, axes = plt.subplots(
n_samples,
num_modalities,
figsize=(num_modalities * 4, 3 * n_samples),
gridspec_kw={"width_ratios": num_modalities * [1]},
)
if n_samples == 1 and num_modalities == 1:
axes = np.array([[axes]])
elif n_samples == 1:
axes = axes.reshape(1, -1)
elif num_modalities == 1:
axes = axes.reshape(-1, 1)
labels = batch["label"][indices]
sample_labels = []
for i in range(n_samples):
present_labels = torch.where(labels[i] == 1)[0].cpu().tolist()
sample_labels.append(present_labels)
for i in range(n_samples):
for j, (mod, modality_img) in enumerate(modalities.items()):
plot_img = modality_img[i]
img = percentile_normalization(plot_img, lower=2, upper=98)
ax = axes[i, j]
ax.imshow(img)
ax.set_title(f"{mod} image" if i == 0 else "", fontsize=20)
ax.axis("off")
label_names = [self.class_names[label] for label in sample_labels[i]]
separator = ", \n"
suptitle = f"Labels: {separator.join(label_names)}"
ax = axes[i, -1]
ax.set_title(suptitle, fontsize=8)
plt.tight_layout()
plt.subplots_adjust(bottom=0.1)
return fig, batch
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def visualize_geolocation_distribution(self) -> None:
"""Visualize the geolocation distribution of the dataset."""
pass