gremlin97/RemoteSensingCorpus · Datasets at Hugging Face

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4
Accepted as a paper at ICLR 2023 Workshop on Machine Learning for Remote Sensing
ACKNOWLEDGMENTS
Esther Rolf was supported by the Harvard Data Science Initiative (HDSI) and the Center for Re-
search on Computation and Society (CRCS). Thank you to Konstantin Klemmer, Caleb Robinson,
and Jessie Finocchiaro for feedback on earlier drafts of this work.
REFERENCES
Antti Airola, Jonne Pohjankukka, Johanna Torppa, Maarit Middleton, Vesa Nyk ¨anen, Jukka Heikko-
nen, and Tapio Pahikkala. The spatial leave-pair-out cross-validation method for reliable AUC
estimation of spatial classiﬁers. Data Mining and Knowledge Discovery , 33(3):730–747, 2019.
Luigi Boschetti, Stephen V Stehman, and David P Roy. A stratiﬁed random sampling design in
space and time for regional to global scale burned area product validation. Remote sensing of
environment , 186:465–478, 2016.
Alexander Brenning. Spatial machine-learning model diagnostics: A model-agnostic distance-based
approach. International Journal of Geographical Information Science , pp. 1–23, 2022.
Dick J Brus. Statistical approaches for spatial sample survey: Persistent misconceptions and new
developments. European Journal of Soil Science , 72(2):686–703, 2021.
Marshall Burke, Anne Driscoll, David B Lobell, and Stefano Ermon. Using satellite imagery to
understand and promote sustainable development. Science , 371(6535):eabe8628, 2021.
Guanghua Chi, Han Fang, Sourav Chatterjee, and Joshua E Blumenstock. Microestimates of wealth
for all low-and middle-income countries. Proceedings of the National Academy of Sciences , 119
(3):e2113658119, 2022.
Ian Dowman and Hannes I Reuter. Global geospatial data from earth observation: Status and issues.
International Journal of Digital Earth , 10(4):328–341, 2017.
Wala Draidi Areed, Aiden Price, Kathryn Arnett, and Kerrie Mengersen. Spatial statistical machine
learning models to assess the relationship between development vulnerabilities and educational
factors in children in queensland, australia. BMC Public Health , 22(1):1–12, 2022.
Yoan Fourcade, Aur ´elien G Besnard, and Jean Secondi. Paintings predict the distribution of species,
or the challenge of selecting environmental predictors and evaluation statistics. Global Ecology
and Biogeography , 27(2):245–256, 2018.
Carlo Gaetan and Xavier Guyon. Spatial statistics and modeling , volume 90. Springer, 2010.
Robert Haining. The special nature of spatial data. The SAGE Handbook of Spatial Analysis. Los
Angeles: SAGE Publications , pp. 5–23, 2009.
J´ulio Hofﬁmann, Maciel Zortea, Breno De Carvalho, and Bianca Zadrozny. Geostatistical learning:
Challenges and opportunities. Frontiers in Applied Mathematics and Statistics , 7:689393, 2021.
Neal Jean, Marshall Burke, Michael Xie, W Matthew Davis, David B Lobell, and Stefano Ermon.
Combining satellite imagery and machine learning to predict poverty. Science , 353(6301):790–
794, 2016.
Konstantin Klemmer and Daniel B Neill. Auxiliary-task learning for geographic data with au-
toregressive embeddings. In Proceedings of the 29th International Conference on Advances in
Geographic Information Systems , pp. 141–144, 2021.
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Bal-
subramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, et al. Wilds: A
benchmark of in-the-wild distribution shifts. In International Conference on Machine Learning ,
pp. 5637–5664. PMLR, 2021.
K´evin Le Rest, David Pinaud, Pascal Monestiez, Jo ¨el Chadoeuf, and Vincent Bretagnolle. Spatial
leave-one-out cross-validation for variable selection in the presence of spatial autocorrelation.
Global ecology and biogeography , 23(7):811–820, 2014.
5
Accepted as a paper at ICLR 2023 Workshop on Machine Learning for Remote Sensing
Sharon L Lohr. Sampling: design and analysis . Chapman and Hall/CRC, 2021.
Aaron E Maxwell, Timothy A Warner, and Luis Andr ´es Guill ´en. Accuracy assessment in convo-
lutional neural network-based deep learning remote sensing studies—part 1: Literature review.
Remote Sensing , 13(13):2450, 2021.
Hanna Meyer and Edzer Pebesma. Machine learning-based global maps of ecological variables and
the challenge of assessing them. Nature Communications , 13(1):1–4, 2022.
Hanna Meyer, Christoph Reudenbach, Stephan W ¨ollauer, and Thomas Nauss. Importance of spatial
predictor variable selection in machine learning applications–moving from data reproduction to
spatial prediction. Ecological Modelling , 411:108815, 2019.
Carles Mil `a, Jorge Mateu, Edzer Pebesma, and Hanna Meyer. Nearest neighbour distance matching
leave-one-out cross-validation for map validation. Methods in Ecology and Evolution , 2022.
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson,
Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. Model cards for model reporting. In
Proceedings of the conference on fairness, accountability, and transparency , pp. 220–229, 2019.
Catherine Nakalembe. Characterizing agricultural drought in the Karamoja subregion of Uganda
with meteorological and satellite-based indices. Natural Hazards , 91(3):837–862, 2018.
Behnam Nikparvar and Jean-Claude Thill. Machine learning of spatial data. ISPRS International
Journal of Geo-Information , 10(9):600, 2021.
Kristine Nilsen, Natalia Tejedor-Garavito, Douglas R Leasure, C Edson Utazi, Corrine W Ruk-
tanonchai, Adelle S Wigley, Claire A Dooley, Zoe Matthews, and Andrew J Tatem. A review of
geospatial methods for population estimation and their use in constructing reproductive, mater-
nal, newborn, child and adolescent health service indicators. BMC health services research , 21
(1):1–10, 2021.
Ruth Y Oliver, Carsten Meyer, Ajay Ranipeta, Kevin Winner, and Walter Jetz. Global and national
trends, gaps, and opportunities in documenting and monitoring species distributions. PLoS Biol-
ogy, 19(8):e3001336, 2021.
Madhava Paliyam, Catherine Nakalembe, Kevin Liu, Richard Nyiawung, and Hannah Kerner.
Street2sat: A machine learning pipeline for generating ground-truth geo-referenced labeled
datasets from street-level images. In Tackling Climate Change with Machine Learning Workshop
at the International Conference on Machine Learning , 2021.
Pierre Ploton, Fr ´ed´eric Mortier, Maxime R ´ejou-M ´echain, Nicolas Barbier, Nicolas Picard, Vivien
Rossi, Carsten Dormann, Guillaume Cornu, Ga ¨elle Viennois, Nicolas Bayol, et al. Spatial val-
idation reveals poor predictive performance of large-scale ecological mapping models. Nature
communications , 11(1):1–11, 2020.
Jonne Pohjankukka, Tapio Pahikkala, Paavo Nevalainen, and Jukka Heikkonen. Estimating the
prediction performance of spatial models via spatial k-fold cross validation. International Journal
of Geographical Information Science , 31(10):2001–2019, 2017.
Jonathan Proctor, Tamma Carleton, and Sandy Sum. Parameter recovery using remotely sensed
variables. Working Paper 30861, National Bureau of Economic Research, January 2023. URL
http://www.nber.org/papers/w30861 .
David R Roberts, V olker Bahn, Simone Ciuti, Mark S Boyce, Jane Elith, Gurutzeta Guillera-Arroita,
Severin Hauenstein, Jos ´e J Lahoz-Monfort, Boris Schr ¨oder, Wilfried Thuiller, et al. Cross-
validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecog-
raphy , 40(8):913–929, 2017.
Caleb Robinson, Anthony Ortiz, Hogeun Park, Nancy Lozano, Jon Kher Kaw, Tina Sederholm,
Rahul Dodhia, and Juan M Lavista Ferres. Fast building segmentation from satellite imagery and
few local labels. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern
Recognition , pp. 1463–1471, 2022.
6
Accepted as a paper at ICLR 2023 Workshop on Machine Learning for Remote Sensing

4

Accepted as a paper at ICLR 2023 Workshop on Machine Learning for Remote Sensing

ACKNOWLEDGMENTS

Esther Rolf was supported by the Harvard Data Science Initiative (HDSI) and the Center for Re-

search on Computation and Society (CRCS). Thank you to Konstantin Klemmer, Caleb Robinson,

and Jessie Finocchiaro for feedback on earlier drafts of this work.

REFERENCES

Antti Airola, Jonne Pohjankukka, Johanna Torppa, Maarit Middleton, Vesa Nyk ¨anen, Jukka Heikko-

nen, and Tapio Pahikkala. The spatial leave-pair-out cross-validation method for reliable AUC

estimation of spatial classiﬁers. Data Mining and Knowledge Discovery , 33(3):730–747, 2019.

Luigi Boschetti, Stephen V Stehman, and David P Roy. A stratiﬁed random sampling design in

space and time for regional to global scale burned area product validation. Remote sensing of

environment , 186:465–478, 2016.

Alexander Brenning. Spatial machine-learning model diagnostics: A model-agnostic distance-based

approach. International Journal of Geographical Information Science , pp. 1–23, 2022.

Dick J Brus. Statistical approaches for spatial sample survey: Persistent misconceptions and new

developments. European Journal of Soil Science , 72(2):686–703, 2021.

Marshall Burke, Anne Driscoll, David B Lobell, and Stefano Ermon. Using satellite imagery to

understand and promote sustainable development. Science , 371(6535):eabe8628, 2021.

Guanghua Chi, Han Fang, Sourav Chatterjee, and Joshua E Blumenstock. Microestimates of wealth

for all low-and middle-income countries. Proceedings of the National Academy of Sciences , 119

(3):e2113658119, 2022.

Ian Dowman and Hannes I Reuter. Global geospatial data from earth observation: Status and issues.

International Journal of Digital Earth , 10(4):328–341, 2017.

Wala Draidi Areed, Aiden Price, Kathryn Arnett, and Kerrie Mengersen. Spatial statistical machine

learning models to assess the relationship between development vulnerabilities and educational

factors in children in queensland, australia. BMC Public Health , 22(1):1–12, 2022.

Yoan Fourcade, Aur ´elien G Besnard, and Jean Secondi. Paintings predict the distribution of species,

or the challenge of selecting environmental predictors and evaluation statistics. Global Ecology

and Biogeography , 27(2):245–256, 2018.

Carlo Gaetan and Xavier Guyon. Spatial statistics and modeling , volume 90. Springer, 2010.

Robert Haining. The special nature of spatial data. The SAGE Handbook of Spatial Analysis. Los

Angeles: SAGE Publications , pp. 5–23, 2009.

J´ulio Hofﬁmann, Maciel Zortea, Breno De Carvalho, and Bianca Zadrozny. Geostatistical learning:

Challenges and opportunities. Frontiers in Applied Mathematics and Statistics , 7:689393, 2021.

Neal Jean, Marshall Burke, Michael Xie, W Matthew Davis, David B Lobell, and Stefano Ermon.

Combining satellite imagery and machine learning to predict poverty. Science , 353(6301):790–

794, 2016.

Konstantin Klemmer and Daniel B Neill. Auxiliary-task learning for geographic data with au-

toregressive embeddings. In Proceedings of the 29th International Conference on Advances in

Geographic Information Systems , pp. 141–144, 2021.

Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Bal-

subramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, et al. Wilds: A

benchmark of in-the-wild distribution shifts. In International Conference on Machine Learning ,

pp. 5637–5664. PMLR, 2021.

K´evin Le Rest, David Pinaud, Pascal Monestiez, Jo ¨el Chadoeuf, and Vincent Bretagnolle. Spatial

leave-one-out cross-validation for variable selection in the presence of spatial autocorrelation.

Global ecology and biogeography , 23(7):811–820, 2014.

5

Accepted as a paper at ICLR 2023 Workshop on Machine Learning for Remote Sensing

Sharon L Lohr. Sampling: design and analysis . Chapman and Hall/CRC, 2021.

Aaron E Maxwell, Timothy A Warner, and Luis Andr ´es Guill ´en. Accuracy assessment in convo-

lutional neural network-based deep learning remote sensing studies—part 1: Literature review.

Remote Sensing , 13(13):2450, 2021.

Hanna Meyer and Edzer Pebesma. Machine learning-based global maps of ecological variables and

the challenge of assessing them. Nature Communications , 13(1):1–4, 2022.

Hanna Meyer, Christoph Reudenbach, Stephan W ¨ollauer, and Thomas Nauss. Importance of spatial

predictor variable selection in machine learning applications–moving from data reproduction to

spatial prediction. Ecological Modelling , 411:108815, 2019.

Carles Mil `a, Jorge Mateu, Edzer Pebesma, and Hanna Meyer. Nearest neighbour distance matching

leave-one-out cross-validation for map validation. Methods in Ecology and Evolution , 2022.

Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson,

Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. Model cards for model reporting. In

Proceedings of the conference on fairness, accountability, and transparency , pp. 220–229, 2019.

Catherine Nakalembe. Characterizing agricultural drought in the Karamoja subregion of Uganda

with meteorological and satellite-based indices. Natural Hazards , 91(3):837–862, 2018.

Behnam Nikparvar and Jean-Claude Thill. Machine learning of spatial data. ISPRS International

Journal of Geo-Information , 10(9):600, 2021.

Kristine Nilsen, Natalia Tejedor-Garavito, Douglas R Leasure, C Edson Utazi, Corrine W Ruk-

tanonchai, Adelle S Wigley, Claire A Dooley, Zoe Matthews, and Andrew J Tatem. A review of

geospatial methods for population estimation and their use in constructing reproductive, mater-

nal, newborn, child and adolescent health service indicators. BMC health services research , 21

(1):1–10, 2021.

Ruth Y Oliver, Carsten Meyer, Ajay Ranipeta, Kevin Winner, and Walter Jetz. Global and national

trends, gaps, and opportunities in documenting and monitoring species distributions. PLoS Biol-

ogy, 19(8):e3001336, 2021.

Madhava Paliyam, Catherine Nakalembe, Kevin Liu, Richard Nyiawung, and Hannah Kerner.

Street2sat: A machine learning pipeline for generating ground-truth geo-referenced labeled

datasets from street-level images. In Tackling Climate Change with Machine Learning Workshop

at the International Conference on Machine Learning , 2021.

Pierre Ploton, Fr ´ed´eric Mortier, Maxime R ´ejou-M ´echain, Nicolas Barbier, Nicolas Picard, Vivien

Rossi, Carsten Dormann, Guillaume Cornu, Ga ¨elle Viennois, Nicolas Bayol, et al. Spatial val-

idation reveals poor predictive performance of large-scale ecological mapping models. Nature

communications , 11(1):1–11, 2020.

Jonne Pohjankukka, Tapio Pahikkala, Paavo Nevalainen, and Jukka Heikkonen. Estimating the

prediction performance of spatial models via spatial k-fold cross validation. International Journal

of Geographical Information Science , 31(10):2001–2019, 2017.

Jonathan Proctor, Tamma Carleton, and Sandy Sum. Parameter recovery using remotely sensed

variables. Working Paper 30861, National Bureau of Economic Research, January 2023. URL

http://www.nber.org/papers/w30861 .

David R Roberts, V olker Bahn, Simone Ciuti, Mark S Boyce, Jane Elith, Gurutzeta Guillera-Arroita,

Severin Hauenstein, Jos ´e J Lahoz-Monfort, Boris Schr ¨oder, Wilfried Thuiller, et al. Cross-

validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecog-

raphy , 40(8):913–929, 2017.

Caleb Robinson, Anthony Ortiz, Hogeun Park, Nancy Lozano, Jon Kher Kaw, Tina Sederholm,

Rahul Dodhia, and Juan M Lavista Ferres. Fast building segmentation from satellite imagery and

few local labels. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern

Recognition , pp. 1463–1471, 2022.

6

Accepted as a paper at ICLR 2023 Workshop on Machine Learning for Remote Sensing