gremlin97/RemoteSensingCorpus · Datasets at Hugging Face

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violation of rights.
A.3 Downloading
All data, metadata, and pre-trained models used or created in this paper can be downloaded from
https://huggingface.co./torchgeo, either manually or using TorchGeo (see Listing 1). Dataset images
are stored in the widely used GeoTIFF format. These datasets and models will be maintained in
perpetuity and may be improved over time. All datasets include dataset cards describing the dataset
size, source, and license. All models include model cards describing the library used to load them,
source, and license.
from torchgeo.datasets import SSL4EOL
ds = SSL4EOL(root="data", split="oli_sr", download=True)
Listing 1: Example download script for the OLI SR split of the SSL4EO-L pre-training dataset.
A.4 Reproducibility
Instructions to recreate the pre-training and benchmark datasets, results, or plots, can be found
at https://github.com/microsoft/torchgeo/blob/releases/v0.5/experiments/ssl4eo/landsat/README.
md. Listing 2 shows example code for pre-training on SSL4EO-L and fine-tuning/evaluating on
our benchmark datasets, and can be modified to control other aspects of the training process or to
train on a different sensor/product. The TorchGeo v0.5 release is the first release containing the
datasets and models used and created in this paper. If you encounter any problems, please open an
issue on GitHub and we will clarify the documentation.
17
from lightning.pytorch import Trainer
from torchgeo.datamodules import (
SSL4EOLDataModule, SSL4EOLBenchmarkDataModule
)
from torchgeo.trainers import MoCoTask, SemanticSegmentationTask
# Pre-train on SSL4EO-L using MoCo
datamodule = SSL4EOLDataModule(split="oli_sr", seasons=2, download=True)
task = MoCoTask(model="resnet18", weights=True, in_channels=7)
trainer = Trainer(max_epochs=200)
trainer.fit(model=task, datamodule=datamodule)
# Fine-tune and evaluate performance
datamodule = SSL4EOLBenchmarkDataModule(sensor="oli_sr", product="cdl")
task = SemanticSegmentationTask(model="unet", backbone="resnet18")
trainer = Trainer(max_epochs=100)
trainer.fit(model=task, datamodule=datamodule)
trainer.test(model=task, datamodule=datamodule)
Listing 2: Example training script to pre-train and benchmark a model on SSL4EO-L.
A.5 Class distribution
The benchmark datasets we use suffer from extreme class imbalance. Below are tables documenting
the value, description, and percentage of each class in all datasets. Fill/background classes are
ignored during training and are not considered when computing these statistics.
A.5.1 Cloud detection datasets
Clear pixels cover more area than all other classes combined.
Table 3: Class distribution for cloud detection datasets.
Value Description L7 Irish L8 Biome
0 Fill - -
64 Cloud Shadow 0.7 1.5
128 Clear 66.1 50.5
192 Thin Cloud 10.2 14.7
255 Cloud 23.0 33.2
A.5.2 SSL4EO-L benchmark datasets
The top 3 classes cover more area than all other classes combined. Only classes with > 1% area are
considered during evaluation, the rest are mapped to the background class. TM data is downloaded
from 2011, while ETM+ and OLI data is downloaded from 2019. The TOA and SR versions have
the same geographic locations, and therefore the same class distribution.
18
Table 4: Class distribution for SSL4EO-L NLCD.
Value Description TM ETM+ OLI
0 Background - - -
11 Open Water 2.4 2.2 2.3
21 Developed, Open Space 2.7 2.7 2.6
22 Developed, Low Intensity 1.7 1.7 1.7
31 Barren Land (Rock/Sand/Clay) 1.0 1.0 1.0
41 Deciduous Forest 9.2 9.2 8.8
42 Evergreen Forest 12.2 11.9 12.1
43 Mixed Forest 3.4 3.4 3.2
52 Shrub/Scrub 22.4 22.8 23.6
71 Grassland/Herbaceous 14.9 14.6 14.6
81 Pasture/Hay 6.2 5.9 5.8
82 Cultivated Crops 16.6 17.3 17.1
90 Woody Wetlands 4.5 4.4 4.3
95 Emergent Herbaceous Wetlands 1.6 1.5 1.6
- Other 1.2 1.4 1.3
Table 5: Class distribution for SSL4EO-L CDL.
Value Description TM ETM+ OLI
0 Background - - -
1 Corn 4.6 4.9 4.7
5 Soybeans 3.6 4.1 3.9
24 Winter Wheat 1.9 1.6 1.6
36 Alfalfa 0.9 1.1 1.2
37 Other Hay/Non Alfalfa 1.2 1.6 1.6
61 Fallow/Idle Cropland 1.4 1.9 1.8
111 Open Water 1.7 1.7 1.7
121 Developed/Open Space 3.3 2.9 2.8
122 Developed/Low intensity 1.4 1.5 1.5
131 Barren 1.1 1.1 1.1
141 Deciduous Forest 11.9 10.6 10.2
142 Evergreen Forest 13.3 12.7 12.9
143 Mixed Forest 1.5 3.2 2.9
152 Shrubland 22.4 24.2 25.0
176 Grass/Pasture 20.3 16.6 16.5
190 Woody Wetlands 3.9 4.2 4.1
195 Herbaceous Wetlands 1.3 1.4 1.5
- Other 4.2 4.7 4.8
19
A.6 Spectral bands
0
Wavelength ( µm)0
0.5 1.0 1.5 2.0Landsat 8–9
(OLI/TIRS)Landsat 7

violation of rights.

A.3 Downloading

All data, metadata, and pre-trained models used or created in this paper can be downloaded from

https://huggingface.co./torchgeo, either manually or using TorchGeo (see Listing 1). Dataset images

are stored in the widely used GeoTIFF format. These datasets and models will be maintained in

perpetuity and may be improved over time. All datasets include dataset cards describing the dataset

size, source, and license. All models include model cards describing the library used to load them,

source, and license.

from torchgeo.datasets import SSL4EOL

ds = SSL4EOL(root="data", split="oli_sr", download=True)

Listing 1: Example download script for the OLI SR split of the SSL4EO-L pre-training dataset.

A.4 Reproducibility

Instructions to recreate the pre-training and benchmark datasets, results, or plots, can be found

at https://github.com/microsoft/torchgeo/blob/releases/v0.5/experiments/ssl4eo/landsat/README.

md. Listing 2 shows example code for pre-training on SSL4EO-L and fine-tuning/evaluating on

our benchmark datasets, and can be modified to control other aspects of the training process or to

train on a different sensor/product. The TorchGeo v0.5 release is the first release containing the

datasets and models used and created in this paper. If you encounter any problems, please open an

issue on GitHub and we will clarify the documentation.

17

from lightning.pytorch import Trainer

from torchgeo.datamodules import (

SSL4EOLDataModule, SSL4EOLBenchmarkDataModule

)

from torchgeo.trainers import MoCoTask, SemanticSegmentationTask

# Pre-train on SSL4EO-L using MoCo

datamodule = SSL4EOLDataModule(split="oli_sr", seasons=2, download=True)

task = MoCoTask(model="resnet18", weights=True, in_channels=7)

trainer = Trainer(max_epochs=200)

trainer.fit(model=task, datamodule=datamodule)

# Fine-tune and evaluate performance

datamodule = SSL4EOLBenchmarkDataModule(sensor="oli_sr", product="cdl")

task = SemanticSegmentationTask(model="unet", backbone="resnet18")

trainer = Trainer(max_epochs=100)

trainer.fit(model=task, datamodule=datamodule)

trainer.test(model=task, datamodule=datamodule)

Listing 2: Example training script to pre-train and benchmark a model on SSL4EO-L.

A.5 Class distribution

The benchmark datasets we use suffer from extreme class imbalance. Below are tables documenting

the value, description, and percentage of each class in all datasets. Fill/background classes are

ignored during training and are not considered when computing these statistics.

A.5.1 Cloud detection datasets

Clear pixels cover more area than all other classes combined.

Table 3: Class distribution for cloud detection datasets.

Value Description L7 Irish L8 Biome

0 Fill - -

64 Cloud Shadow 0.7 1.5

128 Clear 66.1 50.5

192 Thin Cloud 10.2 14.7

255 Cloud 23.0 33.2

A.5.2 SSL4EO-L benchmark datasets

The top 3 classes cover more area than all other classes combined. Only classes with > 1% area are

considered during evaluation, the rest are mapped to the background class. TM data is downloaded

from 2011, while ETM+ and OLI data is downloaded from 2019. The TOA and SR versions have

the same geographic locations, and therefore the same class distribution.

18

Table 4: Class distribution for SSL4EO-L NLCD.

Value Description TM ETM+ OLI

0 Background - - -

11 Open Water 2.4 2.2 2.3

21 Developed, Open Space 2.7 2.7 2.6

22 Developed, Low Intensity 1.7 1.7 1.7

31 Barren Land (Rock/Sand/Clay) 1.0 1.0 1.0

41 Deciduous Forest 9.2 9.2 8.8

42 Evergreen Forest 12.2 11.9 12.1

43 Mixed Forest 3.4 3.4 3.2

52 Shrub/Scrub 22.4 22.8 23.6

71 Grassland/Herbaceous 14.9 14.6 14.6

81 Pasture/Hay 6.2 5.9 5.8

82 Cultivated Crops 16.6 17.3 17.1

90 Woody Wetlands 4.5 4.4 4.3

95 Emergent Herbaceous Wetlands 1.6 1.5 1.6

- Other 1.2 1.4 1.3

Table 5: Class distribution for SSL4EO-L CDL.

Value Description TM ETM+ OLI

0 Background - - -

1 Corn 4.6 4.9 4.7

5 Soybeans 3.6 4.1 3.9

24 Winter Wheat 1.9 1.6 1.6

36 Alfalfa 0.9 1.1 1.2

37 Other Hay/Non Alfalfa 1.2 1.6 1.6

61 Fallow/Idle Cropland 1.4 1.9 1.8

111 Open Water 1.7 1.7 1.7

121 Developed/Open Space 3.3 2.9 2.8

122 Developed/Low intensity 1.4 1.5 1.5

131 Barren 1.1 1.1 1.1

141 Deciduous Forest 11.9 10.6 10.2

142 Evergreen Forest 13.3 12.7 12.9

143 Mixed Forest 1.5 3.2 2.9

152 Shrubland 22.4 24.2 25.0

176 Grass/Pasture 20.3 16.6 16.5

190 Woody Wetlands 3.9 4.2 4.1

195 Herbaceous Wetlands 1.3 1.4 1.5

- Other 4.2 4.7 4.8

19

A.6 Spectral bands

0

Wavelength ( µm)0

0.5 1.0 1.5 2.0Landsat 8–9

(OLI/TIRS)Landsat 7