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As geospatial machine learning models and maps derived from their predictions are increasingly used for downstream analyses in science and policy, it is imperative to evaluate their accuracy and applicability.
The special nature of spatial data
Robert Haining · 2009
Earlier work this paper cites.
Spatial statistics and modeling , volume 90
Carlo Gaetan and Xavier Guyon · 2010
Earlier work this paper cites.
Spatial leave-one-out cross-validation for variable selection in the presence of spatial autocorrelation
Kévin Le Rest, David Pinaud, Pascal Monestiez, Joël Chadoeuf, and Vincent Bretagnolle · 2014
Earlier work this paper cites.
A stratified random sampling design in space and time for regional to global scale burned area product validation
Luigi Boschetti, Stephen V Stehman, and David P Roy · 2016
Earlier work this paper cites.
Combining satellite imagery and machine learning to predict poverty
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Earlier work this paper cites.
Domain adaptation for the classification of remote sensing data: An overview of recent advances
Devis Tuia, Claudio Persello, and Lorenzo Bruzzone · 2016
Earlier work this paper cites.
Global geospatial data from earth observation: Status and issues
Ian Dowman and Hannes I Reuter · 2017
Earlier work this paper cites.
Estimating the prediction performance of spatial models via spatial k-fold cross validation
Jonne Pohjankukka, Tapio Pahikkala, Paavo Nevalainen, and Jukka Heikkonen · 2017
Earlier work this paper cites.
Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure
David R Roberts, Volker Bahn, Simone Ciuti, Mark S Boyce, Jane Elith, Gurutzeta Guillera-Arroita, Severin Hauenstein, José J Lahoz-Monfort, Boris Schröder, Wilfried Thuiller, et al · 2017
Earlier work this paper cites.
Paintings predict the distribution of species, or the challenge of selecting environmental predictors and evaluation statistics
Yoan Fourcade, Aurélien G Besnard, and Jean Secondi · 2018
Earlier work this paper cites.
Characterizing agricultural drought in the Karamoja subregion of Uganda with meteorological and satellite-based indices
Catherine Nakalembe · 2018
Earlier work this paper cites.
blockcv: An r package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models
Roozbeh Valavi, Jane Elith, José J Lahoz-Monfort, and Gurutzeta Guillera-Arroita · 2018
Earlier work this paper cites.
The spatial leave-pair-out cross-validation method for reliable AUC estimation of spatial classifiers
Antti Airola, Jonne Pohjankukka, Johanna Torppa, Maarit Middleton, Vesa Nykänen, Jukka Heikkonen, and Tapio Pahikkala · 2019
Earlier work this paper cites.
Importance of spatial predictor variable selection in machine learning applications–moving from data reproduction to spatial prediction
Hanna Meyer, Christoph Reudenbach, Stephan Wöllauer, and Thomas Nauss · 2019
Earlier work this paper cites.
Model cards for model reporting
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru · 2019
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Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data
Patrick Schratz, Jannes Muenchow, Eugenia Iturritxa, Jakob Richter, and Alexander Brenning · 2019
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Development and delivery of species distribution models to inform decision-making
Helen R Sofaer, Catherine S Jarnevich, Ian S Pearse, Regan L Smyth, Stephanie Auer, Gericke L Cook, Thomas C Edwards Jr, Gerald F Guala, Timothy G Howard, Jeffrey T Morisette, et al · 2019
Cited alongside, same era.
Spatial validation reveals poor predictive performance of large-scale ecological mapping models
Pierre Ploton, Frédéric Mortier, Maxime Réjou-Méchain, Nicolas Barbier, Nicolas Picard, Vivien Rossi, Carsten Dormann, Guillaume Cornu, Gaëlle Viennois, Nicolas Bayol, et al · 2020
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Post-estimation smoothing: A simple baseline for learning with side information
Esther Rolf, Michael I Jordan, and Benjamin Recht · 2020
Global and national trends, gaps, and opportunities in documenting and monitoring species distributions
Ruth Y Oliver, Carsten Meyer, Ajay Ranipeta, Kevin Winner, and Walter Jetz · 2021
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Street2sat: A machine learning pipeline for generating ground-truth geo-referenced labeled datasets from street-level images
Madhava Paliyam, Catherine Nakalembe, Kevin Liu, Richard Nyiawung, and Hannah Kerner · 2021
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A generalizable and accessible approach to machine learning with global satellite imagery
Esther Rolf, Jonathan Proctor, Tamma Carleton, Ian Bolliger, Vaishaal Shankar, Miyabi Ishihara, Benjamin Recht, and Solomon Hsiang · 2021
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Validation of the us geological survey’s land change monitoring, assessment and projection (lcmap) collection 1.0 annual land cover products 1985–2017
Stephen V Stehman, Bruce W Pengra, Josephine A Horton, and Danika F Wellington · 2021
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Spatial cross-validation is not the right way to evaluate map accuracy
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Statistical approaches for spatial sample survey: Persistent misconceptions and new developments
Dick J Brus · 2021
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Using satellite imagery to understand and promote sustainable development
Marshall Burke, Anne Driscoll, David B Lobell, and Stefano Ermon · 2021
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Geostatistical learning: Challenges and opportunities
Júlio Hoffimann, Maciel Zortea, Breno De Carvalho, and Bianca Zadrozny · 2021
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Auxiliary-task learning for geographic data with autoregressive embeddings
Konstantin Klemmer and Daniel B Neill · 2021
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Wilds: A benchmark of in-the-wild distribution shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, et al · 2021
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Sampling: design and analysis
Sharon L Lohr · 2021
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Accuracy assessment in convolutional neural network-based deep learning remote sensing studies—part 1: Literature review
Aaron E Maxwell, Timothy A Warner, and Luis Andrés Guillén · 2021
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Alexandre MJ-C Wadoux, Gerard BM Heuvelink, Sytze De Bruin, and Dick J Brus · 2021
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Spatial machine-learning model diagnostics: A model-agnostic distance-based approach
Alexander Brenning · 2022
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Microestimates of wealth for all low-and middle-income countries
Guanghua Chi, Han Fang, Sourav Chatterjee, and Joshua E Blumenstock · 2022
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Spatial statistical machine learning models to assess the relationship between development vulnerabilities and educational factors in children in queensland, australia
Wala Draidi Areed, Aiden Price, Kathryn Arnett, and Kerrie Mengersen · 2022
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Machine learning-based global maps of ecological variables and the challenge of assessing them
Hanna Meyer and Edzer Pebesma · 2022
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Nearest neighbour distance matching leave-one-out cross-validation for map validation
Carles Milà, Jorge Mateu, Edzer Pebesma, and Hanna Meyer · 2022
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Fast building segmentation from satellite imagery and few local labels
Caleb Robinson, Anthony Ortiz, Hogeun Park, Nancy Lozano, Jon Kher Kaw, Tina Sederholm, Rahul Dodhia, and Juan M Lavista Ferres · 2022
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Three common machine learning algorithms neither enhance prediction accuracy nor reduce spatial autocorrelation in residuals: An analysis of twenty-five socioeconomic data sets
Insang Song and Daehyun Kim · 2022
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Embedding scale: New thinking of scale in machine learning and geographic representation
May Yuan and Arlo McKee · 2022
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Parameter recovery using remotely sensed variables
Jonathan Proctor, Tamma Carleton, and Sandy Sum · 2023
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