gremlin97/RemoteSensingCorpus · Datasets at Hugging Face

text stringlengths 0 820
in russia. Remote Sensing Applications: Society and Environment , 2024.
S. Ahlswede, C. Schulz, C. Gava, P. Helber, B. Bischke, M. Förster, F. Arias, J. Hees, B. Demir, and
B. Kleinschmit. Treesatai benchmark archive: A multi-sensor, multi-label dataset for tree species
classification in remote sensing. Earth System Science Data , 2023.
K. Ayush, B. Uzkent, C. Meng, K. Tanmay, M. Burke, D. Lobell, and S. Ermon. Geography-aware
self-supervised learning. In CVPR , 2021.
N. H. Batjes, E. Ribeiro, A. Van Oostrum, J. Leenaars, T. Hengl, and J. Mendes de Jesus. Wosis:
providing standardised soil profile data for the world. Earth System Science Data , 2017.
12
V . Böhm, W. J. Leong, R. B. Mahesh, I. Prapas, E. Nemni, F. Kalaitzis, S. Ganju, and R. Ramos-
Pollan. Sar-based landslide classification pretraining leads to better segmentation. In Artificial
Intelligence for Humanitarian Assistance and Disaster Response Workshop at NeurIPS , 2022.
P. O. Bressan, J. M. Junior, J. A. C. Martins, M. J. de Melo, D. N. Gonçalves, D. M. Freitas, A. P. M.
Ramos, M. T. G. Furuya, L. P. Osco, J. de Andrade Silva, et al. Semantic segmentation with
labeling uncertainty and class imbalance applied to vegetation mapping. International Journal of
Applied Earth Observation and Geoinformation , 2022.
C. F. Brown, S. P. Brumby, B. Guzder-Williams, T. Birch, S. B. Hyde, J. Mazzariello, W. Czerwinski,
V . J. Pasquarella, R. Haertel, S. Ilyushchenko, K. Schwehr, M. Weisse, F. Stolle, C. Hanson,
O. Guinan, R. Moore, and A. M. Tait. Dynamic world, near real-time global 10 m land use land
cover mapping. Scientific Data , Jun 2022.
Y . Cong, S. Khanna, C. Meng, P. Liu, E. Rozi, Y . He, M. Burke, D. B. Lobell, and S. Ermon.
SatMAE: Pre-training transformers for temporal and multi-spectral satellite imagery. In A. H.
Oh, A. Agarwal, D. Belgrave, and K. Cho, editors, NeurIPS , 2022. URL https://openreview.
net/forum?id=WBhqzpF6KYH .
S. Di Tommaso, S. Wang, V . Vajipey, N. Gorelick, R. Strey, and D. B. Lobell. Annual field-
scale maps of tall and short crops at the global scale using gedi and sentinel-2. arXiv preprint
arXiv:2212.09681 , 2022.
S. English, T. McNally, N. Bormann, K. Salonen, M. Matricardi, A. Moranyi, M. Rennie,
M. Janisková, S. Di Michele, A. Geer, et al. Impact of satellite data, 2013.
B. Ferreira, M. Iten, and R. G. Silva. Monitoring sustainable development by means of earth
observation data and machine learning: A review. Environmental Sciences Europe , 2020.
A. Fuller, K. Millard, and J. R. Green. CROMA: Remote sensing representations with contrastive
radar-optical masked autoencoders. In Thirty-seventh Conference on Neural Information Process-
ing Systems , 2023. URL https://openreview.net/forum?id=ezqI5WgGvY .
P. Gao, T. Ma, H. Li, Z. Lin, J. Dai, and Y . Qiao. Convmae: Masked convolution meets masked
autoencoders. arXiv preprint arXiv:2205.03892 , 2022.
N. Gorelick, M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R. Moore. Google earth engine:
Planetary-scale geospatial analysis for everyone. Remote sensing of Environment , 2017.
M. C. Hansen, P. V . Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V .
Stehman, S. J. Goetz, T. R. Loveland, et al. High-resolution global maps of 21st-century forest
cover change. Science , 2013.
K. He, X. Chen, S. Xie, Y . Li, P. Dollár, and R. Girshick. Masked autoencoders are scalable vision
learners. In CVPR , 2022.
P. Helber, B. Bischke, A. Dengel, and D. Borth. Eurosat: A novel dataset and deep learning
benchmark for land use and land cover classification. IEEE Journal of Selected Topics in Applied
Earth Observations and Remote Sensing , 2019.
T. Hengl, J. Mendes de Jesus, G. B. Heuvelink, M. Ruiperez Gonzalez, M. Kilibarda, A. Blagoti ´c,
W. Shangguan, M. N. Wright, X. Geng, B. Bauer-Marschallinger, et al. Soilgrids250m: Global
gridded soil information based on machine learning. PLoS one , 2017.
N. Jean, S. Wang, A. Samar, G. Azzari, D. Lobell, and S. Ermon. Tile2vec: Unsupervised representa-
tion learning for spatially distributed data. In AAAI , 2019.
P. Kansakar and F. Hossain. A review of applications of satellite earth observation data for global
societal benefit and stewardship of planet earth. Space Policy , 2016.
H. Kerner, G. Tseng, I. Becker-Reshef, C. Nakalembe, B. Barker, B. Munshell, M. Paliyam, and
M. Hosseini. Rapid response crop maps in data sparse regions. In ACM SIGKDD Conference on
Data Mining and Knowledge Discovery Workshops , 2020.
A. Krafft. ASU researcher combats food insecurity with AI. https://news.asu.edu/20230303-solutions-
asu-researcher-combats-food-insecurity-ai. Accessed: 2023-09-21.
M. Kshirsagar, C. Robinson, S. Yang, S. Gholami, I. Klyuzhin, S. Mukherjee, M. Nasir, A. Ortiz,
F. Oviedo, D. Tanner, et al. Becoming good at ai for good. In AAAI/ACM Conference on AI, Ethics,
and Society , 2021.
13
A. Lacoste, N. Lehmann, P. Rodriguez, E. D. Sherwin, H. Kerner, B. Lütjens, J. A. Irvin, D. Dao,
H. Alemohammad, A. Drouin, et al. Geo-bench: Toward foundation models for earth monitoring.
arXiv preprint arXiv:2306.03831 , 2023.
O. Manas, A. Lacoste, X. Giró-i Nieto, D. Vazquez, and P. Rodriguez. Seasonal contrast: Unsuper-
vised pre-training from uncurated remote sensing data. In CVPR , 2021.
C. Nakalembe and H. Kerner. Considerations for ai-eo for agriculture in sub-saharan africa. Environ-
mental Research Letters , 2023.
C. Nakalembe, C. Justice, H. Kerner, C. Justice, and I. Becker-Reshef. Sowing seeds of food security
in africa. Eos (Washington. DC) , 102, 2021.
M. Neumann, A. S. Pinto, X. Zhai, and N. Houlsby. In-domain representation learning for remote
sensing. arXiv preprint arXiv:1911.06721 , 2019.
C. Parkinson, A. Ward, and M. King. Earth science reference handbook. National Aeronautics and
Space Administration: Washington, DC, USA , 2006.
C. Pelletier, G. I. Webb, and F. Petitjean. Temporal convolutional neural network for the classification
of satellite image time series. Remote Sensing , 2019.
K. Rao, A. P. Williams, J. F. Flefil, and A. G. Konings. Sar-enhanced mapping of live fuel moisture
content. Remote Sensing of Environment , 2020.
C. J. Reed, R. Gupta, S. Li, S. Brockman, C. Funk, B. Clipp, S. Candido, M. Uyttendaele, and
T. Darrell. Scale-mae: A scale-aware masked autoencoder for multiscale geospatial representation
learning. arXiv preprint arXiv:2212.14532 , 2022.
C. Robinson, L. Hou, K. Malkin, R. Soobitsky, J. Czawlytko, B. Dilkina, and N. Jojic. Large scale
high-resolution land cover mapping with multi-resolution data. In CVPR , 2019.
E. Rolf, J. Proctor, T. Carleton, I. Bolliger, V . Shankar, M. Ishihara, B. Recht, and S. Hsiang. A
generalizable and accessible approach to machine learning with global satellite imagery. Nature
communications , 2021.
J. W. Rouse, R. H. Haas, J. A. Schell, D. W. Deering, et al. Monitoring vegetation systems in the
great plains with erts. NASA Spec. Publ , 351(1):309, 1974.
M. Rußwurm and M. Körner. Self-attention for raw optical satellite time series classification. ISPRS
journal of photogrammetry and remote sensing , 2020.
M. Rußwurm, S. Wang, M. Korner, and D. Lobell. Meta-learning for few-shot land cover classification.
InCVPR Workshops , pages 200–201, 2020.
V . Sainte Fare Garnot, L. Landrieu, S. Giordano, and N. Chehata. Satellite image time series
classification with pixel-set encoders and temporal self-attention. CVPR , 2020.
E. Strubell, A. Ganesh, and A. McCallum. Energy and policy considerations for deep learning in
nlp. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics .
Association for Computational Linguistics, 2019.
M. Sudmanns, D. Tiede, H. Augustin, and S. Lang. Assessing global sentinel-2 coverage dynamics
and data availability for operational earth observation (eo) applications using the eo-compass.

in russia. Remote Sensing Applications: Society and Environment , 2024.

S. Ahlswede, C. Schulz, C. Gava, P. Helber, B. Bischke, M. Förster, F. Arias, J. Hees, B. Demir, and

B. Kleinschmit. Treesatai benchmark archive: A multi-sensor, multi-label dataset for tree species

classification in remote sensing. Earth System Science Data , 2023.

K. Ayush, B. Uzkent, C. Meng, K. Tanmay, M. Burke, D. Lobell, and S. Ermon. Geography-aware

self-supervised learning. In CVPR , 2021.

N. H. Batjes, E. Ribeiro, A. Van Oostrum, J. Leenaars, T. Hengl, and J. Mendes de Jesus. Wosis:

providing standardised soil profile data for the world. Earth System Science Data , 2017.

12

V . Böhm, W. J. Leong, R. B. Mahesh, I. Prapas, E. Nemni, F. Kalaitzis, S. Ganju, and R. Ramos-

Pollan. Sar-based landslide classification pretraining leads to better segmentation. In Artificial

Intelligence for Humanitarian Assistance and Disaster Response Workshop at NeurIPS , 2022.

P. O. Bressan, J. M. Junior, J. A. C. Martins, M. J. de Melo, D. N. Gonçalves, D. M. Freitas, A. P. M.

Ramos, M. T. G. Furuya, L. P. Osco, J. de Andrade Silva, et al. Semantic segmentation with

labeling uncertainty and class imbalance applied to vegetation mapping. International Journal of

Applied Earth Observation and Geoinformation , 2022.

C. F. Brown, S. P. Brumby, B. Guzder-Williams, T. Birch, S. B. Hyde, J. Mazzariello, W. Czerwinski,

V . J. Pasquarella, R. Haertel, S. Ilyushchenko, K. Schwehr, M. Weisse, F. Stolle, C. Hanson,

O. Guinan, R. Moore, and A. M. Tait. Dynamic world, near real-time global 10 m land use land

cover mapping. Scientific Data , Jun 2022.

Y . Cong, S. Khanna, C. Meng, P. Liu, E. Rozi, Y . He, M. Burke, D. B. Lobell, and S. Ermon.

SatMAE: Pre-training transformers for temporal and multi-spectral satellite imagery. In A. H.

Oh, A. Agarwal, D. Belgrave, and K. Cho, editors, NeurIPS , 2022. URL https://openreview.

net/forum?id=WBhqzpF6KYH .

S. Di Tommaso, S. Wang, V . Vajipey, N. Gorelick, R. Strey, and D. B. Lobell. Annual field-

scale maps of tall and short crops at the global scale using gedi and sentinel-2. arXiv preprint

arXiv:2212.09681 , 2022.

S. English, T. McNally, N. Bormann, K. Salonen, M. Matricardi, A. Moranyi, M. Rennie,

M. Janisková, S. Di Michele, A. Geer, et al. Impact of satellite data, 2013.

B. Ferreira, M. Iten, and R. G. Silva. Monitoring sustainable development by means of earth

observation data and machine learning: A review. Environmental Sciences Europe , 2020.

A. Fuller, K. Millard, and J. R. Green. CROMA: Remote sensing representations with contrastive

radar-optical masked autoencoders. In Thirty-seventh Conference on Neural Information Process-

ing Systems , 2023. URL https://openreview.net/forum?id=ezqI5WgGvY .

P. Gao, T. Ma, H. Li, Z. Lin, J. Dai, and Y . Qiao. Convmae: Masked convolution meets masked

autoencoders. arXiv preprint arXiv:2205.03892 , 2022.

N. Gorelick, M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R. Moore. Google earth engine:

Planetary-scale geospatial analysis for everyone. Remote sensing of Environment , 2017.

M. C. Hansen, P. V . Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V .

Stehman, S. J. Goetz, T. R. Loveland, et al. High-resolution global maps of 21st-century forest

cover change. Science , 2013.

K. He, X. Chen, S. Xie, Y . Li, P. Dollár, and R. Girshick. Masked autoencoders are scalable vision

learners. In CVPR , 2022.

P. Helber, B. Bischke, A. Dengel, and D. Borth. Eurosat: A novel dataset and deep learning

benchmark for land use and land cover classification. IEEE Journal of Selected Topics in Applied

Earth Observations and Remote Sensing , 2019.

T. Hengl, J. Mendes de Jesus, G. B. Heuvelink, M. Ruiperez Gonzalez, M. Kilibarda, A. Blagoti ´c,

W. Shangguan, M. N. Wright, X. Geng, B. Bauer-Marschallinger, et al. Soilgrids250m: Global

gridded soil information based on machine learning. PLoS one , 2017.

N. Jean, S. Wang, A. Samar, G. Azzari, D. Lobell, and S. Ermon. Tile2vec: Unsupervised representa-

tion learning for spatially distributed data. In AAAI , 2019.

P. Kansakar and F. Hossain. A review of applications of satellite earth observation data for global

societal benefit and stewardship of planet earth. Space Policy , 2016.

H. Kerner, G. Tseng, I. Becker-Reshef, C. Nakalembe, B. Barker, B. Munshell, M. Paliyam, and

M. Hosseini. Rapid response crop maps in data sparse regions. In ACM SIGKDD Conference on

Data Mining and Knowledge Discovery Workshops , 2020.

A. Krafft. ASU researcher combats food insecurity with AI. https://news.asu.edu/20230303-solutions-

asu-researcher-combats-food-insecurity-ai. Accessed: 2023-09-21.

M. Kshirsagar, C. Robinson, S. Yang, S. Gholami, I. Klyuzhin, S. Mukherjee, M. Nasir, A. Ortiz,

F. Oviedo, D. Tanner, et al. Becoming good at ai for good. In AAAI/ACM Conference on AI, Ethics,

and Society , 2021.

13

A. Lacoste, N. Lehmann, P. Rodriguez, E. D. Sherwin, H. Kerner, B. Lütjens, J. A. Irvin, D. Dao,

H. Alemohammad, A. Drouin, et al. Geo-bench: Toward foundation models for earth monitoring.

arXiv preprint arXiv:2306.03831 , 2023.

O. Manas, A. Lacoste, X. Giró-i Nieto, D. Vazquez, and P. Rodriguez. Seasonal contrast: Unsuper-

vised pre-training from uncurated remote sensing data. In CVPR , 2021.

C. Nakalembe and H. Kerner. Considerations for ai-eo for agriculture in sub-saharan africa. Environ-

mental Research Letters , 2023.

C. Nakalembe, C. Justice, H. Kerner, C. Justice, and I. Becker-Reshef. Sowing seeds of food security

in africa. Eos (Washington. DC) , 102, 2021.

M. Neumann, A. S. Pinto, X. Zhai, and N. Houlsby. In-domain representation learning for remote

sensing. arXiv preprint arXiv:1911.06721 , 2019.

C. Parkinson, A. Ward, and M. King. Earth science reference handbook. National Aeronautics and

Space Administration: Washington, DC, USA , 2006.

C. Pelletier, G. I. Webb, and F. Petitjean. Temporal convolutional neural network for the classification

of satellite image time series. Remote Sensing , 2019.

K. Rao, A. P. Williams, J. F. Flefil, and A. G. Konings. Sar-enhanced mapping of live fuel moisture

content. Remote Sensing of Environment , 2020.

C. J. Reed, R. Gupta, S. Li, S. Brockman, C. Funk, B. Clipp, S. Candido, M. Uyttendaele, and

T. Darrell. Scale-mae: A scale-aware masked autoencoder for multiscale geospatial representation

learning. arXiv preprint arXiv:2212.14532 , 2022.

C. Robinson, L. Hou, K. Malkin, R. Soobitsky, J. Czawlytko, B. Dilkina, and N. Jojic. Large scale

high-resolution land cover mapping with multi-resolution data. In CVPR , 2019.

E. Rolf, J. Proctor, T. Carleton, I. Bolliger, V . Shankar, M. Ishihara, B. Recht, and S. Hsiang. A

generalizable and accessible approach to machine learning with global satellite imagery. Nature

communications , 2021.

J. W. Rouse, R. H. Haas, J. A. Schell, D. W. Deering, et al. Monitoring vegetation systems in the

great plains with erts. NASA Spec. Publ , 351(1):309, 1974.

M. Rußwurm and M. Körner. Self-attention for raw optical satellite time series classification. ISPRS

journal of photogrammetry and remote sensing , 2020.

M. Rußwurm, S. Wang, M. Korner, and D. Lobell. Meta-learning for few-shot land cover classification.

InCVPR Workshops , pages 200–201, 2020.

V . Sainte Fare Garnot, L. Landrieu, S. Giordano, and N. Chehata. Satellite image time series

classification with pixel-set encoders and temporal self-attention. CVPR , 2020.

E. Strubell, A. Ganesh, and A. McCallum. Energy and policy considerations for deep learning in

nlp. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics .

Association for Computational Linguistics, 2019.

M. Sudmanns, D. Tiede, H. Augustin, and S. Lang. Assessing global sentinel-2 coverage dynamics

and data availability for operational earth observation (eo) applications using the eo-compass.