gremlin97/RemoteSensingCorpus · Datasets at Hugging Face

text stringlengths 0 820
sat 1. Designed by Virginia T. Norwood, the Multispectral Scanner (MSS) onboard Landsats 1–5
provided invaluable measurements of the Earth’s surface in both the visible and infrared spectra.
Although she passed away earlier this year, her legacy as “The Mother of Landsat” lives on, as the
Landsat program has become the longest-running Earth observation program in history [1].
The Landsat satellite program stretches over 50 years and includes 9 generations of satellites, each
with its own set of sensors . Landsats 1–3 carried the Return Beam Vidicon (RBV), an RGB analog
camera [2]. However, its lower number of spectral bands and electrical issues meant it was rarely
used for research purposes. Instead, the Multispectral Scanner (MSS) onboard Landsats 1–5 was the
primary scientific instrument, with a line-scanning and rotating mirror-based camera [3]. Landsats
4–5 also included the Thematic Mapper (TM), with a greater number of spectral bands (from 4 to 7)
and finer spatial resolution (from 80 m to 30 m) [4]. Although it failed to reach orbit and eventually
crashed down to Earth, Landsat 6 would have carried the Enhanced Thematic Mapper (ETM), which
added a 15 m resolution panchromatic band. Landsat 7 carried the Enhanced Thematic Mapper Plus
(ETM+), which upgraded the thermal band from 120 m to 60 m resolution [5]. Onboard Landsats
1https://github.com/microsoft/torchgeo
Preprint. Under review.arXiv:2306.09424v2 [cs.LG] 22 Oct 2023
0
Wavelength ( µm)0
0.5 1.0 1.5 2.0Landsat 8–9
(OLI/TIRS)Landsat 7
(ETM+)Landsat 4–5
(TM)
89
7 65 4
32187 5 43217 5 4321
11 12153010015306030120
Resolution (m)
11 1066Figure 1: Spectral wavelengths and spatial resolutions of each band captured by the Landsat sensors
used in our study. Numbers are band indices and colors are for visualization purposes only. As
each sensor has a different number of spectral bands, spatial resolutions, and wavelengths, it is not
possible to train a single one-size-fits-all model.
8–9, the Operational Land Imager (OLI) adds new coastal aerosol and cirrus bands for improved
cloud masking, while the Thermal Infrared Sensor (TIRS) adds an additional thermal band [6]. See
Figure 1 for a rundown of the spectral wavelengths and spatial resolutions of the primary sensors of
interest in our study and Figure 2 for a timeline of when these satellites were active.
In addition to the differences between sensors onboard each satellite, the United States Geological
Survey (USGS) distributes several different Landsat products with varying processing levels. Level-
1 data, also known as Top of Atmosphere (TOA), are images that have undergone registration against
ground control points (GCPs) and orthorectification against digital elevation models (DEMs). These
products are particularly useful for cloud masking and other atmospheric applications. Level-2 data
includes Surface Reflectance (SR) and other products that have undergone atmospheric correction.
These products are useful for a wide range of land surface applications [7].
In recent years, there has been significant activity at the intersection of self-supervised learning
(SSL) and remote sensing (RS) due to the wide availability of petabytes of free, unlabeled satellite
imagery. An early example of this is Tile2Vec [8], which uses geographic distance between sampled
patches and a triplet margin loss for contrastive learning. Geography-Aware SSL [9] instead uses
multiple images occurring at the same geospatial location at different points in time to form positive
pairs, in combination with an additional subnetwork that tries to guess the latitude/longitude from
the learned representation. More recently, masked autoencoders have seen a surge in popularity, in-
cluding SatMAE [10] and Scale-MAE [11]. Other papers instead focus on dataset curation, allowing
generic SSL techniques from computer vision to be applied. Seasonal Contrast (SeCo) [12] creates
a new dataset using random Gaussian sampling around cities to diversify the pre-training dataset.
SSL4EO-S12 [13] further extends this idea by avoiding overlap between image samples.
Recent review papers [14, 15, 16] targeting the intersection between SSL and RS detail prior work on
this topic and offer benchmark results on several models and SSL techniques popular in computer
vision, including MoCo [17], SwA V [18], SimSiam [19], Barlow Twins [20], SimCLR [21], and
BYOL [22]. Among these, the authors find that although SimCLR and SwA V work well on the
ImageNet [23] dataset, MoCo and BYOL tend to learn better representations on RS imagery [15, 16].
Due to their higher spatial resolution and faster repeat period, a lot of recent work, especially in
the SSL space, focuses on Sentinel-2 [24, 25, 13], Maxar [9, 26, 27], and Planet [28, 29] satellites.
However, many applications are not suited to these satellites. In particular, applications involving
long-term trends—including agriculture [30, 31, 32, 33, 34], climate change [35, 36, 37, 38, 39],
2
1970 1980 1990 2000 2010 2020987654321Landsat Mission
Sep 2021–PresentFeb 2013–PresentApr 1999–PresentOct 1993Mar 1984–Jun 2013Jul 1982–Jun 2001Mar 1978–Sep 1983Jan 1975–Mar 1983Jul 1972–Jan 1978Figure 2: Timeline of all Landsat missions. Crosshatched regions represent partial or complete
sensor failure, including electrical issues with RBV on Landsat 1 and SLC-off on Landsat 7. Each
bar ranges from launch date to decommissioning date. Note that satellites may be placed in standby
mode before the decommissioning date, as is the case with Landsat 4.
deforestation [40, 41, 42, 43, 44], and ecology [45, 46, 47, 48, 49]—require a much longer temporal
history. While Sentinel-2 has an 8 year history, Landsat’s 50+ year history makes it essential for
monitoring long-term land surface changes. There are an order of magnitude more papers that use
Landsat as compared to Sentinel, and Landsat continues to dominate the scientific literature even
after the launch of Sentinel-2 and MODIS [50]. The United States Geological Survey (USGS)
estimates that Landsat imagery provides users with an annual benefit of $4.2 billion [51].
In this work, we further extend the ideas proposed in SeCo [12] and SSL4EO-S12 [13] to the Landsat
imagery domain. Specifically, we improve on the sampling method of the two previously mentioned
papers and sample Landsat imagery across the world and across three different sensors and two
product levels. We pre-train ResNet [52] and ViT [53] models using SimCLR v1 and MoCo v2 on
each combination of sensor and product to produce a suite of pre-trained Landsat foundation models
that can be used in downstream tasks. To test these models, we modernize two older datasets based
on Landsat 7 and 8 imagery, and create several additional crop classification and land cover mapping
tasks. In summary, the contributions of this paper include:
• the first ever SSL dataset for the Landsat family of satellites,
• the largest Landsat dataset in history (1M images per sensor/product, 5M in total),
• modernized and re-released versions of the L7 Irish and L8 Biome cloud detection datasets,
• two new benchmark datasets that can be used across all Landsat sensors and product levels,
• the first ever benchmark datasets for TM and ETM+ SR imagery, and
• the first ever foundation models pre-trained on Landsat imagery.
Importantly, all of these SSL techniques, datasets, and pre-trained models are distributed via the
TorchGeo library [54], allowing for ease of use, experimentation, and reproduciblity.
2 Datasets
In this section, we detail our methodology behind the collection of the SSL dataset we create, in-
cluding differences from prior work. We also introduce the existing and newly created benchmark
datasets we use to evaluate the representations learned by our pre-trained models.
3
2.1 SSL4EO-L pre-training dataset
For our SSL pre-training dataset, we extend the methodology introduced by Manas et al. [12] and
refined by Wang et al. [13]. Specifically, we iterate over the following steps:
1) sample one of the 10K most populous cities in the world [55] uniformly at random;
2) sample a 264×264px (7.92×7.92km) patch from a Gaussian distribution with a 50 km

sat 1. Designed by Virginia T. Norwood, the Multispectral Scanner (MSS) onboard Landsats 1–5

provided invaluable measurements of the Earth’s surface in both the visible and infrared spectra.

Although she passed away earlier this year, her legacy as “The Mother of Landsat” lives on, as the

Landsat program has become the longest-running Earth observation program in history [1].

The Landsat satellite program stretches over 50 years and includes 9 generations of satellites, each

with its own set of sensors . Landsats 1–3 carried the Return Beam Vidicon (RBV), an RGB analog

camera [2]. However, its lower number of spectral bands and electrical issues meant it was rarely

used for research purposes. Instead, the Multispectral Scanner (MSS) onboard Landsats 1–5 was the

primary scientific instrument, with a line-scanning and rotating mirror-based camera [3]. Landsats

4–5 also included the Thematic Mapper (TM), with a greater number of spectral bands (from 4 to 7)

and finer spatial resolution (from 80 m to 30 m) [4]. Although it failed to reach orbit and eventually

crashed down to Earth, Landsat 6 would have carried the Enhanced Thematic Mapper (ETM), which

added a 15 m resolution panchromatic band. Landsat 7 carried the Enhanced Thematic Mapper Plus

(ETM+), which upgraded the thermal band from 120 m to 60 m resolution [5]. Onboard Landsats

1https://github.com/microsoft/torchgeo

Preprint. Under review.arXiv:2306.09424v2 [cs.LG] 22 Oct 2023

0

Wavelength ( µm)0

0.5 1.0 1.5 2.0Landsat 8–9

(OLI/TIRS)Landsat 7

(ETM+)Landsat 4–5

(TM)

89

7 65 4

32187 5 43217 5 4321

11 12153010015306030120

Resolution (m)

11 1066Figure 1: Spectral wavelengths and spatial resolutions of each band captured by the Landsat sensors

used in our study. Numbers are band indices and colors are for visualization purposes only. As

each sensor has a different number of spectral bands, spatial resolutions, and wavelengths, it is not

possible to train a single one-size-fits-all model.

8–9, the Operational Land Imager (OLI) adds new coastal aerosol and cirrus bands for improved

cloud masking, while the Thermal Infrared Sensor (TIRS) adds an additional thermal band [6]. See

Figure 1 for a rundown of the spectral wavelengths and spatial resolutions of the primary sensors of

interest in our study and Figure 2 for a timeline of when these satellites were active.

In addition to the differences between sensors onboard each satellite, the United States Geological

Survey (USGS) distributes several different Landsat products with varying processing levels. Level-

1 data, also known as Top of Atmosphere (TOA), are images that have undergone registration against

ground control points (GCPs) and orthorectification against digital elevation models (DEMs). These

products are particularly useful for cloud masking and other atmospheric applications. Level-2 data

includes Surface Reflectance (SR) and other products that have undergone atmospheric correction.

These products are useful for a wide range of land surface applications [7].

In recent years, there has been significant activity at the intersection of self-supervised learning

(SSL) and remote sensing (RS) due to the wide availability of petabytes of free, unlabeled satellite

imagery. An early example of this is Tile2Vec [8], which uses geographic distance between sampled

patches and a triplet margin loss for contrastive learning. Geography-Aware SSL [9] instead uses

multiple images occurring at the same geospatial location at different points in time to form positive

pairs, in combination with an additional subnetwork that tries to guess the latitude/longitude from

the learned representation. More recently, masked autoencoders have seen a surge in popularity, in-

cluding SatMAE [10] and Scale-MAE [11]. Other papers instead focus on dataset curation, allowing

generic SSL techniques from computer vision to be applied. Seasonal Contrast (SeCo) [12] creates

a new dataset using random Gaussian sampling around cities to diversify the pre-training dataset.

SSL4EO-S12 [13] further extends this idea by avoiding overlap between image samples.

Recent review papers [14, 15, 16] targeting the intersection between SSL and RS detail prior work on

this topic and offer benchmark results on several models and SSL techniques popular in computer

vision, including MoCo [17], SwA V [18], SimSiam [19], Barlow Twins [20], SimCLR [21], and

BYOL [22]. Among these, the authors find that although SimCLR and SwA V work well on the

ImageNet [23] dataset, MoCo and BYOL tend to learn better representations on RS imagery [15, 16].

Due to their higher spatial resolution and faster repeat period, a lot of recent work, especially in

the SSL space, focuses on Sentinel-2 [24, 25, 13], Maxar [9, 26, 27], and Planet [28, 29] satellites.

However, many applications are not suited to these satellites. In particular, applications involving

long-term trends—including agriculture [30, 31, 32, 33, 34], climate change [35, 36, 37, 38, 39],

2

1970 1980 1990 2000 2010 2020987654321Landsat Mission

Sep 2021–PresentFeb 2013–PresentApr 1999–PresentOct 1993Mar 1984–Jun 2013Jul 1982–Jun 2001Mar 1978–Sep 1983Jan 1975–Mar 1983Jul 1972–Jan 1978Figure 2: Timeline of all Landsat missions. Crosshatched regions represent partial or complete

sensor failure, including electrical issues with RBV on Landsat 1 and SLC-off on Landsat 7. Each

bar ranges from launch date to decommissioning date. Note that satellites may be placed in standby

mode before the decommissioning date, as is the case with Landsat 4.

deforestation [40, 41, 42, 43, 44], and ecology [45, 46, 47, 48, 49]—require a much longer temporal

history. While Sentinel-2 has an 8 year history, Landsat’s 50+ year history makes it essential for

monitoring long-term land surface changes. There are an order of magnitude more papers that use

Landsat as compared to Sentinel, and Landsat continues to dominate the scientific literature even

after the launch of Sentinel-2 and MODIS [50]. The United States Geological Survey (USGS)

estimates that Landsat imagery provides users with an annual benefit of $4.2 billion [51].

In this work, we further extend the ideas proposed in SeCo [12] and SSL4EO-S12 [13] to the Landsat

imagery domain. Specifically, we improve on the sampling method of the two previously mentioned

papers and sample Landsat imagery across the world and across three different sensors and two

product levels. We pre-train ResNet [52] and ViT [53] models using SimCLR v1 and MoCo v2 on

each combination of sensor and product to produce a suite of pre-trained Landsat foundation models

that can be used in downstream tasks. To test these models, we modernize two older datasets based

on Landsat 7 and 8 imagery, and create several additional crop classification and land cover mapping

tasks. In summary, the contributions of this paper include:

• the first ever SSL dataset for the Landsat family of satellites,

• the largest Landsat dataset in history (1M images per sensor/product, 5M in total),

• modernized and re-released versions of the L7 Irish and L8 Biome cloud detection datasets,

• two new benchmark datasets that can be used across all Landsat sensors and product levels,

• the first ever benchmark datasets for TM and ETM+ SR imagery, and

• the first ever foundation models pre-trained on Landsat imagery.

Importantly, all of these SSL techniques, datasets, and pre-trained models are distributed via the

TorchGeo library [54], allowing for ease of use, experimentation, and reproduciblity.

2 Datasets

In this section, we detail our methodology behind the collection of the SSL dataset we create, in-

cluding differences from prior work. We also introduce the existing and newly created benchmark

datasets we use to evaluate the representations learned by our pre-trained models.

3

2.1 SSL4EO-L pre-training dataset

For our SSL pre-training dataset, we extend the methodology introduced by Manas et al. [12] and

refined by Wang et al. [13]. Specifically, we iterate over the following steps:

1) sample one of the 10K most populous cities in the world [55] uniformly at random;

2) sample a 264×264px (7.92×7.92km) patch from a Gaussian distribution with a 50 km