gremlin97/RemoteSensingCorpus · Datasets at Hugging Face

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Optimized GeoTIFFs (COGs), resample to 30 m resolution, and stack them into single multi-band
files with consistent filenames. The compression algorithm used by COGs resulted in a dataset that
is 33% of the original size and therefore faster to download and load from disk. The final ML-ready
dataset is available on Hugging Face and can be automatically downloaded using TorchGeo.
2.2.2 L8 Biome dataset
The L8 Biome dataset, created by Foga et al. [65], is a validation dataset for cloud cover assessment
algorithms consisting of 96 Landsat 8 OLI/TIRS Level-1T scenes and manually generated cloud
masks evenly divided between 8 unique biomes. Each scene is an 11-band, roughly 9000×9000 px
multispectral image with 30 m/px resolution. Cloud masks consist of the same 5 classes as L7 Irish.
Comparatively, L8 Biome has fewer issues than L7 Irish. The masks lack geolocation, but we can
copy this from the image files. While the dataset can be programmatically downloaded, it requires
scraping a webpage for 96 different URLs for each scene. We convert the raw uint16 images to uint8
to match L7 Irish, and create compressed COGs of all files, resulting in a dataset 9% of the original
size. We resample all images to 30 m/px resolution and stack them in single multi-band files. The
dataset is available on Hugging Face and can be automatically downloaded using TorchGeo.
2.3 SSL4EO-L benchmark dataset
As there are no existing benchmark datasets for TM or ETM+ SR, we need to design our own.
Crucially, we want a single benchmark dataset that can be used for a consistent comparison across all
5 sensors/products for which we are pre-training models. We create our own land cover classification
datasets based on NLCD [70] and CDL [71] masks, described in more detail below. They are the
only large, Landsat-based semantic segmentation masks with a long enough history to benchmark
foundation models for historical satellites.
Our sampling strategy is similar to the one used for our pre-training dataset, with a few differ-
ences. As CDL only exists for the continental U.S. (CONUS), we restrict our sampling strategy to
CONUS. To achieve maximum coverage, especially in lower population regions where agriculture
is most prevalent, we replace the city-centered Gaussian distribution with a uniform sampling distri-
bution. We choose a single 60-day window centered around August 1stwhen crop types are easiest
to distinguish. As CDL data is not available before the ETM+ SLC failure, we do not exclude no-
data pixels for this sensor. Additionally, nodata masks are copied from SLC-off imagery to masks
so as to avoid penalizing models for making incorrect predictions where there is no data. The 2019
NLCD and CDL datasets are used for ETM+ and OLI/TIRS evaluation since 2019 is the most recent
year for which both datasets exist. The 2011 datasets are used for TM since 2011 is the most recent
year for which both Landsat 5 and NLCD/CDL overlap. These years are different than the years
collected for our pre-training dataset, allowing us to accurately measure performance on images that
the pre-trained model has never seen before.
The resulting dataset consists of 25K Landsat, NLCD, and CDL triplets, converted from float32
to uint8 using the same scaling as above. All images have the same resolution and dimensions as
the pre-training dataset. The datasets form a parallel corpus between TOA and SR products, and
have approximately 85% spatial overlap across sensors, although not necessarily during the same
year, allowing for multimodal data fusion studies. All datasets are available for download from
Hugging Face using the TorchGeo library, making it easy for other researchers to compare against
our preliminary benchmark results.
6
NLCD The National Land Cover Database (NLCD) [70] is a land cover product produced every
2–3 years by the USGS, in collaboration with the Multi-Resolution Land Characteristics (MRLC)
consortium. The dataset spans the entire U.S. from 2001–2019. The final products are generated
at a 30 m resolution by random forest models trained on spectral, spatial, temporal, and ancillary
data [72, 73, 74]. We use the 21 class version, with an estimated overall accuracy of 77.5±1.0% [75].
CDL The Cropland Data Layer (CDL) [32] is an annual land cover product produced by the U.S.
Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) focusing on
crop classification. Although the dataset is available starting in 1997, full CONUS coverage is not
available until 2008. The dataset consists of 134 classes, primarily for agricultural crops grown in
the U.S. Labels are generated at a 30 m resolution using a decision tree classifier. The most common
crop classes are estimated to have an accuracy of 85–95% [32]. All non-agricultural classes are
taken from NLCD, and should be considered to have a similar accuracy.
3 Experimental setup
For pre-training we conduct experiments similar to those performed in SSL4EO-S12 [13] for each
sensor/product in the dataset described in Section 2.1. We pre-train various ResNet [52] and ViT [53]
backbones initialized with ImageNet weights using the SimCLR v1 [21] and MoCo v2 [17] SSL
methods. RGB ImageNet weights are repeated (RGBRGB. . . ) and scaled ( 3/CforCchannels)
in the first convolutional layer in order to handle multispectral images. During pre-training we
use the same default augmentations and hyperparameters as SimCLR and MoCo with a couple of
exceptions. As saturation and hue are undefined for multispectral imagery, we skip these parts of
color jitter. Instead, we use the random season contrast technique proposed by Manas et al. [12]
by utilizing 2 randomly sampled multitemporal images from the same location as the augmented
views. Additionally, although grayscale is undefined for multispectral imagery, we take the average
of all bands to compute random grayscale images. We pre-train each model for 200 epochs using
a batch size of 1024. All pre-training experiments are performed on a GPU cluster, with 80 GB of
memory per GPU. Each experiment takes anywhere from 15–40 hrs depending on the number of
spectral bands and model size, each trained in parallel on 4 ×GPUs, for a total of ∼4K GPU hours
including hyperparameter tuning.
For benchmarking, we freeze the encoder and fine-tune a U-Net [76] decoder for all cloud detection
and land cover classification datasets mentioned above. For the L7 Irish and L8 Biome datasets,
we use a random 60-20-20 train-val-test split. For the NLCD and CDL datasets, we use a random
70-15-15 train-val-test split. NLCD and CDL classes are limited to those with > 1% area, with
remaining classes mapped to the background class. Splits are defined using a fixed random seed for
reproducibility. Random horizontal and vertical flip and random resized crop data augmentations
are used during training. Models are trained for a minimum of 20 epochs and a maximum of 100
epochs using early stopping and a learning rate schedule patience of 6 epochs. Only learning rate
undergoes hyperparameter tuning, with the most common optimal learning rate being 3e-3. All
benchmarking experiments are conducted on NVIDIA RTX A6000 (2.5 hr/experiment) and A100
(1 hr/experiment) GPUs for a total of ∼200 GPU hours. Configuration files and training scripts for
reproducing all experiments are made available in the TorchGeo library [54].
4 Benchmark results
In order to evaluate the effectiveness of our pre-trained models, we report overall accuracy and
mean intersection over union (mIoU) on four semantic segmentation datasets. Table 1 demonstrates
substantial gains over ImageNet, with up to an 18.43% accuracy and 24.25 mIoU improvement for
MoCo and up to a 14.43% accuracy and 18.69 mIoU improvement for SimCLR. Although MoCo
outperforms ImageNet in 5 out of 6 experiments, SimCLR shows mixed results, outperforming
ImageNet in only 2 out of 6 experiments. Our SimCLR models suffered from convergence issues
with the smaller batch size we used, and may improve with better hyperparameter tuning.
Note that both our sampling method and pretext task are explicitly designed to ignore clouds. During
sampling, we only select patches from scenes with < 20% cloud cover, decreasing the frequency of
clouds in our pre-training dataset. Our pretext task involves mapping patches taken from 2 different
seasons to the same representation. If one patch contains partial cloud cover, the model must learn to
7
Table 1: Cloud detection benchmark results. Overall accuracy and mean intersection over union
(mIoU) are reported for the test splits of the L7 Irish (Landsat 7 ETM+ TOA) and L8 Biome (Landsat
8 OLI/TIRS TOA) datasets for a range of backbones and pre-training techniques. All predictions are
made by U-Nets with frozen backbones. Three random seeds are used to compute mean ±standard

Optimized GeoTIFFs (COGs), resample to 30 m resolution, and stack them into single multi-band

files with consistent filenames. The compression algorithm used by COGs resulted in a dataset that

is 33% of the original size and therefore faster to download and load from disk. The final ML-ready

dataset is available on Hugging Face and can be automatically downloaded using TorchGeo.

2.2.2 L8 Biome dataset

The L8 Biome dataset, created by Foga et al. [65], is a validation dataset for cloud cover assessment

algorithms consisting of 96 Landsat 8 OLI/TIRS Level-1T scenes and manually generated cloud

masks evenly divided between 8 unique biomes. Each scene is an 11-band, roughly 9000×9000 px

multispectral image with 30 m/px resolution. Cloud masks consist of the same 5 classes as L7 Irish.

Comparatively, L8 Biome has fewer issues than L7 Irish. The masks lack geolocation, but we can

copy this from the image files. While the dataset can be programmatically downloaded, it requires

scraping a webpage for 96 different URLs for each scene. We convert the raw uint16 images to uint8

to match L7 Irish, and create compressed COGs of all files, resulting in a dataset 9% of the original

size. We resample all images to 30 m/px resolution and stack them in single multi-band files. The

dataset is available on Hugging Face and can be automatically downloaded using TorchGeo.

2.3 SSL4EO-L benchmark dataset

As there are no existing benchmark datasets for TM or ETM+ SR, we need to design our own.

Crucially, we want a single benchmark dataset that can be used for a consistent comparison across all

5 sensors/products for which we are pre-training models. We create our own land cover classification

datasets based on NLCD [70] and CDL [71] masks, described in more detail below. They are the

only large, Landsat-based semantic segmentation masks with a long enough history to benchmark

foundation models for historical satellites.

Our sampling strategy is similar to the one used for our pre-training dataset, with a few differ-

ences. As CDL only exists for the continental U.S. (CONUS), we restrict our sampling strategy to

CONUS. To achieve maximum coverage, especially in lower population regions where agriculture

is most prevalent, we replace the city-centered Gaussian distribution with a uniform sampling distri-

bution. We choose a single 60-day window centered around August 1stwhen crop types are easiest

to distinguish. As CDL data is not available before the ETM+ SLC failure, we do not exclude no-

data pixels for this sensor. Additionally, nodata masks are copied from SLC-off imagery to masks

so as to avoid penalizing models for making incorrect predictions where there is no data. The 2019

NLCD and CDL datasets are used for ETM+ and OLI/TIRS evaluation since 2019 is the most recent

year for which both datasets exist. The 2011 datasets are used for TM since 2011 is the most recent

year for which both Landsat 5 and NLCD/CDL overlap. These years are different than the years

collected for our pre-training dataset, allowing us to accurately measure performance on images that

the pre-trained model has never seen before.

The resulting dataset consists of 25K Landsat, NLCD, and CDL triplets, converted from float32

to uint8 using the same scaling as above. All images have the same resolution and dimensions as

the pre-training dataset. The datasets form a parallel corpus between TOA and SR products, and

have approximately 85% spatial overlap across sensors, although not necessarily during the same

year, allowing for multimodal data fusion studies. All datasets are available for download from

Hugging Face using the TorchGeo library, making it easy for other researchers to compare against

our preliminary benchmark results.

6

NLCD The National Land Cover Database (NLCD) [70] is a land cover product produced every

2–3 years by the USGS, in collaboration with the Multi-Resolution Land Characteristics (MRLC)

consortium. The dataset spans the entire U.S. from 2001–2019. The final products are generated

at a 30 m resolution by random forest models trained on spectral, spatial, temporal, and ancillary

data [72, 73, 74]. We use the 21 class version, with an estimated overall accuracy of 77.5±1.0% [75].

CDL The Cropland Data Layer (CDL) [32] is an annual land cover product produced by the U.S.

Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) focusing on

crop classification. Although the dataset is available starting in 1997, full CONUS coverage is not

available until 2008. The dataset consists of 134 classes, primarily for agricultural crops grown in

the U.S. Labels are generated at a 30 m resolution using a decision tree classifier. The most common

crop classes are estimated to have an accuracy of 85–95% [32]. All non-agricultural classes are

taken from NLCD, and should be considered to have a similar accuracy.

3 Experimental setup

For pre-training we conduct experiments similar to those performed in SSL4EO-S12 [13] for each

sensor/product in the dataset described in Section 2.1. We pre-train various ResNet [52] and ViT [53]

backbones initialized with ImageNet weights using the SimCLR v1 [21] and MoCo v2 [17] SSL

methods. RGB ImageNet weights are repeated (RGBRGB. . . ) and scaled ( 3/CforCchannels)

in the first convolutional layer in order to handle multispectral images. During pre-training we

use the same default augmentations and hyperparameters as SimCLR and MoCo with a couple of

exceptions. As saturation and hue are undefined for multispectral imagery, we skip these parts of

color jitter. Instead, we use the random season contrast technique proposed by Manas et al. [12]

by utilizing 2 randomly sampled multitemporal images from the same location as the augmented

views. Additionally, although grayscale is undefined for multispectral imagery, we take the average

of all bands to compute random grayscale images. We pre-train each model for 200 epochs using

a batch size of 1024. All pre-training experiments are performed on a GPU cluster, with 80 GB of

memory per GPU. Each experiment takes anywhere from 15–40 hrs depending on the number of

spectral bands and model size, each trained in parallel on 4 ×GPUs, for a total of ∼4K GPU hours

including hyperparameter tuning.

For benchmarking, we freeze the encoder and fine-tune a U-Net [76] decoder for all cloud detection

and land cover classification datasets mentioned above. For the L7 Irish and L8 Biome datasets,

we use a random 60-20-20 train-val-test split. For the NLCD and CDL datasets, we use a random

70-15-15 train-val-test split. NLCD and CDL classes are limited to those with > 1% area, with

remaining classes mapped to the background class. Splits are defined using a fixed random seed for

reproducibility. Random horizontal and vertical flip and random resized crop data augmentations

are used during training. Models are trained for a minimum of 20 epochs and a maximum of 100

epochs using early stopping and a learning rate schedule patience of 6 epochs. Only learning rate

undergoes hyperparameter tuning, with the most common optimal learning rate being 3e-3. All

benchmarking experiments are conducted on NVIDIA RTX A6000 (2.5 hr/experiment) and A100

(1 hr/experiment) GPUs for a total of ∼200 GPU hours. Configuration files and training scripts for

reproducing all experiments are made available in the TorchGeo library [54].

4 Benchmark results

In order to evaluate the effectiveness of our pre-trained models, we report overall accuracy and

mean intersection over union (mIoU) on four semantic segmentation datasets. Table 1 demonstrates

substantial gains over ImageNet, with up to an 18.43% accuracy and 24.25 mIoU improvement for

MoCo and up to a 14.43% accuracy and 18.69 mIoU improvement for SimCLR. Although MoCo

outperforms ImageNet in 5 out of 6 experiments, SimCLR shows mixed results, outperforming

ImageNet in only 2 out of 6 experiments. Our SimCLR models suffered from convergence issues

with the smaller batch size we used, and may improve with better hyperparameter tuning.

Note that both our sampling method and pretext task are explicitly designed to ignore clouds. During

sampling, we only select patches from scenes with < 20% cloud cover, decreasing the frequency of

clouds in our pre-training dataset. Our pretext task involves mapping patches taken from 2 different

seasons to the same representation. If one patch contains partial cloud cover, the model must learn to

7

Table 1: Cloud detection benchmark results. Overall accuracy and mean intersection over union

(mIoU) are reported for the test splits of the L7 Irish (Landsat 7 ETM+ TOA) and L8 Biome (Landsat

8 OLI/TIRS TOA) datasets for a range of backbones and pre-training techniques. All predictions are

made by U-Nets with frozen backbones. Three random seeds are used to compute mean ±standard