gremlin97/RemoteSensingCorpus · Datasets at Hugging Face

text stringlengths 0 820
of Environment , 238:111558, 2020.
[50] Michael A. Wulder, David P. Roy, V olker C. Radeloff, Thomas R. Loveland, Martha C. Ander-
son, David M. Johnson, Sean Healey, Zhe Zhu, Theodore A. Scambos, Nima Pahlevan, et al.
Fifty years of Landsat science and impacts. Remote Sensing of Environment , 280:113195,
2022.
[51] Crista L. Straub, Stephen R. Koontz, John B. Loomis, et al. Economic valuation of Landsat
imagery. Open-File Report - US Geological Survey , 2019.
[52] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image
recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recog-
nition , pages 770–778, 2016.
[53] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai,
Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly,
et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv
preprint arXiv:2010.11929 , 2020.
[54] Adam J. Stewart, Caleb Robinson, Isaac A. Corley, Anthony Ortiz, Juan M. Lavista Ferres,
and Arindam Banerjee. TorchGeo: Deep learning with geospatial data. In Proceedings of the
30th International Conference on Advances in Geographic Information Systems , pages 1–12,
2022.
[55] Pareto Software, LLC. World cities database, March 2023. URL https://simplemaps.com/data/
world-cities.
[56] Antonin Guttman. R-trees: A dynamic index structure for spatial searching. In Proceedings
of the 1984 ACM SIGMOD International Conference on Management of Data , pages 47–57,
1984.
[57] Noel Gorelick, Matt Hancher, Mike Dixon, Simon Ilyushchenko, David Thau, and Rebecca
Moore. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens-
ing of Environment , 2017. URL https://doi.org/10.1016/j.rse.2017.06.031.
[58] Dheeru Dua and Casey Graff. UCI machine learning repository, 2017. URL https://archive.
ics.uci.edu/ml.
[59] M. Joseph Hughes and Daniel J. Hayes. Automated detection of cloud and cloud shadow
in single-date Landsat imagery using neural networks and spatial post-processing. Remote
Sensing , 6(6):4907–4926, 2014.
[60] U.S. Geological Survey. L7 Irish cloud validation masks. U.S. Geological Survey data release,
2016. URL https://doi.org/10.5066/F7XD0ZWC.
14
[61] Pasquale L. Scaramuzza, Michelle A. Bouchard, and John L. Dwyer. Development of the
Landsat data continuity mission cloud-cover assessment algorithms. IEEE Transactions on
Geoscience and Remote Sensing , 50(4):1140–1154, 2011.
[62] Pasquale L. Scaramuzza. Landsat 7 Collection 2 cloud truth mask validation set. U.S. Geolog-
ical Survey data release, 2022. URL https://doi.org/10.5066/P9ASLQQE.
[63] M. Joseph Hughes and Robert Kennedy. High-quality cloud masking of Landsat 8 imagery
using convolutional neural networks. Remote Sensing , 11(21):2591, 2019.
[64] U.S. Geological Survey. L8 SPARCS cloud validation masks. U.S. Geological Survey data
release, 2016. URL https://doi.org/10.5066/F7FB5146.
[65] Steve Foga, Pat L. Scaramuzza, Song Guo, Zhe Zhu, Ronald D. Dilley Jr., Tim Beckmann,
Gail L. Schmidt, John L. Dwyer, M. Joseph Hughes, and Brady Laue. Cloud detection al-
gorithm comparison and validation for operational Landsat data products. Remote Sensing of
Environment , 194:379–390, 2017.
[66] U.S. Geological Survey. L8 Biome cloud validation masks. U.S. Geological Survey data
release, 2016. URL https://doi.org/10.5066/F7251GDH.
[67] Pasquale L. Scaramuzza. Landsat 8 Collection 2 cloud truth mask validation set. U.S. Geolog-
ical Survey data release, 2021. URL https://doi.org/10.5066/P9FI4A0Y.
[68] Richard R. Irish, John L. Barker, Samuel N. Goward, and Terry Arvidson. Characterization of
the Landsat-7 ETM+ automated cloud-cover assessment (ACCA) algorithm. Photogrammetric
Engineering and Remote Sensing , 72(10):1179–1188, 2006.
[69] U.S. Geological Survey. L7 Irish cloud validation masks. USGS ScienceBase Catalog, 2015.
URL https://www.sciencebase.gov/catalog/item/573ccf18e4b0dae0d5e4b109.
[70] Jon Dewitz and U.S. Geological Survey. National Land Cover Database (NLCD) 2019 products
(ver. 2.0). U.S. Geological Survey data release, June 2021. URL https://doi.org/10.5066/
P9KZCM54.
[71] USDA National Agricultural Statistics Service (USDA-NASS). Cropland Data Layer (CDL).
Published crop-specific data layer, 2019. URL https://nassgeodata.gmu.edu/CropScape/. Ac-
cessed 2023.
[72] Collin Homer, Jon Dewitz, Suming Jin, George Xian, Catherine Costello, Patrick Danielson,
Leila Gass, Michelle Funk, James Wickham, Stephen Stehman, et al. Conterminous United
States land cover change patterns 2001–2016 from the 2016 national land cover database.
ISPRS Journal of Photogrammetry and Remote Sensing , 162:184–199, 2020.
[73] Suming Jin, Collin Homer, Limin Yang, Patrick Danielson, Jon Dewitz, Congcong Li, Zhe
Zhu, George Xian, and Danny Howard. Overall methodology design for the United States
national land cover database 2016 products. Remote Sensing , 11(24):2971, 2019.
[74] Limin Yang, Suming Jin, Patrick Danielson, Collin Homer, Leila Gass, Stacie M. Bender,
Adam Case, Catherine Costello, Jon Dewitz, Joyce Fry, et al. A new generation of the United
States National Land Cover Database: Requirements, research priorities, design, and imple-
mentation strategies. ISPRS Journal of Photogrammetry and Remote Sensing , 146:108–123,
2018.
[75] James Wickham, Stephen V . Stehman, Daniel G. Sorenson, Leila Gass, and Jon A. Dewitz.
Thematic accuracy assessment of the NLCD 2016 land cover for the conterminous United
States. Remote Sensing of Environment , 257:112357, 2021.
[76] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-Net: Convolutional networks
for biomedical image segmentation. In Medical Image Computing and Computer-Assisted
Intervention–MICCAI 2015: 18th International Conference, Proceedings, Part III 18 , pages
234–241, Munich, Germany, October 2015. Springer.
15
[77] Robert Bindschadler. Landsat coverage of the earth at high latitudes. Photogrammetric Engi-
neering & Remote Sensing , 69(12):1333–1339, 2003.
[78] John D. Boon. The tilt of the earth’s axis and the consequences thereof. Field and Laboratory ,
13(1):2, 1945.
16
A Appendix
A.1 Ethics statement
Although satellite imagery in general can pose ethical concerns for surveillance and military appli-
cations, the imagery used to pre-train our models is low resolution (30 m/px) and cannot be used
for such purposes. The primary applications Landsat imagery is useful for are Earth observation, in-
cluding downstream tasks like climate change, agriculture, and ecology. While model training does
contribute to greenhouse gas emissions, we believe that the benefits of such foundation models,
especially their ability to reduce training demands for end users, outweigh these contributions.
A.2 Licensing
All data used to create our datasets is released by the USGS under public domain, and may be
used, shared, transferred, or redistributed without restriction. All datasets and models we create are
released under a CC0 1.0 Universal license. All code, including training scripts and core Torch-
Geo contributions, is released under an MIT license. The authors bear all responsibility in case of

of Environment , 238:111558, 2020.

[50] Michael A. Wulder, David P. Roy, V olker C. Radeloff, Thomas R. Loveland, Martha C. Ander-

son, David M. Johnson, Sean Healey, Zhe Zhu, Theodore A. Scambos, Nima Pahlevan, et al.

Fifty years of Landsat science and impacts. Remote Sensing of Environment , 280:113195,

2022.

[51] Crista L. Straub, Stephen R. Koontz, John B. Loomis, et al. Economic valuation of Landsat

imagery. Open-File Report - US Geological Survey , 2019.

[52] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image

recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recog-

nition , pages 770–778, 2016.

[53] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai,

Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly,

et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv

preprint arXiv:2010.11929 , 2020.

[54] Adam J. Stewart, Caleb Robinson, Isaac A. Corley, Anthony Ortiz, Juan M. Lavista Ferres,

and Arindam Banerjee. TorchGeo: Deep learning with geospatial data. In Proceedings of the

30th International Conference on Advances in Geographic Information Systems , pages 1–12,

2022.

[55] Pareto Software, LLC. World cities database, March 2023. URL https://simplemaps.com/data/

world-cities.

[56] Antonin Guttman. R-trees: A dynamic index structure for spatial searching. In Proceedings

of the 1984 ACM SIGMOD International Conference on Management of Data , pages 47–57,

1984.

[57] Noel Gorelick, Matt Hancher, Mike Dixon, Simon Ilyushchenko, David Thau, and Rebecca

Moore. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens-

ing of Environment , 2017. URL https://doi.org/10.1016/j.rse.2017.06.031.

[58] Dheeru Dua and Casey Graff. UCI machine learning repository, 2017. URL https://archive.

ics.uci.edu/ml.

[59] M. Joseph Hughes and Daniel J. Hayes. Automated detection of cloud and cloud shadow

in single-date Landsat imagery using neural networks and spatial post-processing. Remote

Sensing , 6(6):4907–4926, 2014.

[60] U.S. Geological Survey. L7 Irish cloud validation masks. U.S. Geological Survey data release,

2016. URL https://doi.org/10.5066/F7XD0ZWC.

14

[61] Pasquale L. Scaramuzza, Michelle A. Bouchard, and John L. Dwyer. Development of the

Landsat data continuity mission cloud-cover assessment algorithms. IEEE Transactions on

Geoscience and Remote Sensing , 50(4):1140–1154, 2011.

[62] Pasquale L. Scaramuzza. Landsat 7 Collection 2 cloud truth mask validation set. U.S. Geolog-

ical Survey data release, 2022. URL https://doi.org/10.5066/P9ASLQQE.

[63] M. Joseph Hughes and Robert Kennedy. High-quality cloud masking of Landsat 8 imagery

using convolutional neural networks. Remote Sensing , 11(21):2591, 2019.

[64] U.S. Geological Survey. L8 SPARCS cloud validation masks. U.S. Geological Survey data

release, 2016. URL https://doi.org/10.5066/F7FB5146.

[65] Steve Foga, Pat L. Scaramuzza, Song Guo, Zhe Zhu, Ronald D. Dilley Jr., Tim Beckmann,

Gail L. Schmidt, John L. Dwyer, M. Joseph Hughes, and Brady Laue. Cloud detection al-

gorithm comparison and validation for operational Landsat data products. Remote Sensing of

Environment , 194:379–390, 2017.

[66] U.S. Geological Survey. L8 Biome cloud validation masks. U.S. Geological Survey data

release, 2016. URL https://doi.org/10.5066/F7251GDH.

[67] Pasquale L. Scaramuzza. Landsat 8 Collection 2 cloud truth mask validation set. U.S. Geolog-

ical Survey data release, 2021. URL https://doi.org/10.5066/P9FI4A0Y.

[68] Richard R. Irish, John L. Barker, Samuel N. Goward, and Terry Arvidson. Characterization of

the Landsat-7 ETM+ automated cloud-cover assessment (ACCA) algorithm. Photogrammetric

Engineering and Remote Sensing , 72(10):1179–1188, 2006.

[69] U.S. Geological Survey. L7 Irish cloud validation masks. USGS ScienceBase Catalog, 2015.

URL https://www.sciencebase.gov/catalog/item/573ccf18e4b0dae0d5e4b109.

[70] Jon Dewitz and U.S. Geological Survey. National Land Cover Database (NLCD) 2019 products

(ver. 2.0). U.S. Geological Survey data release, June 2021. URL https://doi.org/10.5066/

P9KZCM54.

[71] USDA National Agricultural Statistics Service (USDA-NASS). Cropland Data Layer (CDL).

Published crop-specific data layer, 2019. URL https://nassgeodata.gmu.edu/CropScape/. Ac-

cessed 2023.

[72] Collin Homer, Jon Dewitz, Suming Jin, George Xian, Catherine Costello, Patrick Danielson,

Leila Gass, Michelle Funk, James Wickham, Stephen Stehman, et al. Conterminous United

States land cover change patterns 2001–2016 from the 2016 national land cover database.

ISPRS Journal of Photogrammetry and Remote Sensing , 162:184–199, 2020.

[73] Suming Jin, Collin Homer, Limin Yang, Patrick Danielson, Jon Dewitz, Congcong Li, Zhe

Zhu, George Xian, and Danny Howard. Overall methodology design for the United States

national land cover database 2016 products. Remote Sensing , 11(24):2971, 2019.

[74] Limin Yang, Suming Jin, Patrick Danielson, Collin Homer, Leila Gass, Stacie M. Bender,

Adam Case, Catherine Costello, Jon Dewitz, Joyce Fry, et al. A new generation of the United

States National Land Cover Database: Requirements, research priorities, design, and imple-

mentation strategies. ISPRS Journal of Photogrammetry and Remote Sensing , 146:108–123,

2018.

[75] James Wickham, Stephen V . Stehman, Daniel G. Sorenson, Leila Gass, and Jon A. Dewitz.

Thematic accuracy assessment of the NLCD 2016 land cover for the conterminous United

States. Remote Sensing of Environment , 257:112357, 2021.

[76] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-Net: Convolutional networks

for biomedical image segmentation. In Medical Image Computing and Computer-Assisted

Intervention–MICCAI 2015: 18th International Conference, Proceedings, Part III 18 , pages

234–241, Munich, Germany, October 2015. Springer.

15

[77] Robert Bindschadler. Landsat coverage of the earth at high latitudes. Photogrammetric Engi-

neering & Remote Sensing , 69(12):1333–1339, 2003.

[78] John D. Boon. The tilt of the earth’s axis and the consequences thereof. Field and Laboratory ,

13(1):2, 1945.

16

A Appendix

A.1 Ethics statement

Although satellite imagery in general can pose ethical concerns for surveillance and military appli-

cations, the imagery used to pre-train our models is low resolution (30 m/px) and cannot be used

for such purposes. The primary applications Landsat imagery is useful for are Earth observation, in-

cluding downstream tasks like climate change, agriculture, and ecology. While model training does

contribute to greenhouse gas emissions, we believe that the benefits of such foundation models,

especially their ability to reduce training demands for end users, outweigh these contributions.

A.2 Licensing

All data used to create our datasets is released by the USGS under public domain, and may be

used, shared, transferred, or redistributed without restriction. All datasets and models we create are

released under a CC0 1.0 Universal license. All code, including training scripts and core Torch-

Geo contributions, is released under an MIT license. The authors bear all responsibility in case of