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Recent developments and research in modern machine learning have led to substantial improvements in the geospatial field.
W. R. Tobler, “A computer movie simulating urban growth in the detroit region,” Economic Geography
1970
Earlier work this paper cites.
R. D. Cook, “Detection of influential observation in linear regression,” Technometrics
1977
Earlier work this paper cites.
D. Angluin and P. Laird, “Learning from noisy examples,” Machine Learning
1988
Earlier work this paper cites.
M. Ferecatu and N. Boujemaa, “Interactive remote-sensing image retrieval using active relevance feedback,” IEEE Transactions on Geoscience and Remote Sensing
2007
Earlier work this paper cites.
M. Sugiyama, S. Nakajima, H. Kashima, P. Buenau, and M. Kawanabe, “Direct importance estimation with model selection and its application to covariate shift adaptation,” Advances in neural information processing systems
2007
Earlier work this paper cites.
S. V. Stehman, “Sampling designs for accuracy assessment of land cover,” International Journal of Remote Sensing
2009
Earlier work this paper cites.
D. Tuia, F. Ratle, F. Pacifici, M. F. Kanevski, and W. J. Emery, “Active learning methods for remote sensing image classification,” IEEE Transactions on Geoscience and Remote Sensing
2009
Earlier work this paper cites.
G. M. Foody, “Sample size determination for image classification accuracy assessment and comparison,” International Journal of Remote Sensing
2009
Earlier work this paper cites.
B. Demir, C. Persello, and L. Bruzzone, “Batch-mode active-learning methods for the interactive classification of remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing
2010
Earlier work this paper cites.
S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Transactions on knowledge and data engineering
2010
Earlier work this paper cites.
D. Tuia, M. Volpi, L. Copa, M. Kanevski, and J. Munoz-Mari, “A survey of active learning algorithms for supervised remote sensing image classification,” IEEE Journal of Selected Topics in Signal Processing
2011
Earlier work this paper cites.
R. Roscher, B. Waske, and W. Förstner, “Incremental import vector machines for large area land cover classification,” in Proc. of the IEEE International Conference on Computer Vision Workshops (ICCV Workshops)
2011
Earlier work this paper cites.
R. Gomes, P. Welinder, A. Krause, and P. Perona, “Crowdclustering,” Advances in neural information processing systems
2011
Earlier work this paper cites.
R. G. Pontius Jr and M. Millones, “Death to kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment,” International Journal of Remote Sensing
2011
Earlier work this paper cites.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research
2011
Earlier work this paper cites.
K. L. Wagstaff, “Machine learning that matters,” in Proc. of the International Coference on International Conference on Machine Learning
2012
Earlier work this paper cites.
R. Roscher, W. Förstner, and B. Waske, “I2vm: Incremental import vector machines,” Image and Vision Computing
2012
Earlier work this paper cites.
F. Schomm, F. Stahl, and G. Vossen, “Marketplaces for data: an initial survey,” ACM SIGMOD Record
2013
Earlier work this paper cites.
P. Olofsson, G. M. Foody, M. Herold, S. V. Stehman, C. E. Woodcock, and M. A. Wulder, “Good practices for estimating area and assessing accuracy of land change,” Remote Sensing of Environment
2014
Earlier work this paper cites.
C. Persello and L. Bruzzone, “Active and semisupervised learning for the classification of remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing
2014
Earlier work this paper cites.
J. Wen, R. Greiner, and D. Schuurmans, “Correcting covariate shift with the frank-wolfe algorithm,” in Proc. of the International Joint Conference on Artificial Intelligence
2015
Earlier work this paper cites.
S. Shankar, Y. Halpern, E. Breck, J. Atwood, J. Wilson, and D. Sculley, “No classification without representation: Assessing geodiversity issues in open data sets for the developing world,” in Proc. of the NeurIPS Workshop on Machine Learning for the Developing World
2017
Earlier work this paper cites.
P. W. Koh and P. Liang, “Understanding black-box predictions via influence functions,” in Proc. of the International Conference on Machine Learning
2017
Earlier work this paper cites.
L. Zhang, Y. Zhao, Z. Zhu, D. Shen, and S. Ji, “Multi-view missing data completion,” IEEE Transactions on Knowledge and Data Engineering
2018
Earlier work this paper cites.
M. B. Lyons, D. A. Keith, S. R. Phinn, T. J. Mason, and J. Elith, “A comparison of resampling methods for remote sensing classification and accuracy assessment,” Remote Sensing of Environment
2018
Earlier work this paper cites.
N. Yokoya, P. Ghamisi, J. Xia, S. Sukhanov, R. Heremans, I. Tankoyeu, B. Bechtel, B. Le Saux, G. Moser, and D. Tuia, “Open data for global multimodal land use classification: Outcome of the 2017 ieee grss data fusion contest,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
2018
Earlier work this paper cites.
Y. Luo, X. Cai, Y. Zhang, J. Xu, et al
2018
Earlier work this paper cites.
J. Dong, R. Yin, X. Sun, Q. Li, Y. Yang, and X. Qin, “Inpainting of remote sensing sst images with deep convolutional generative adversarial network,” IEEE Geoscience and Remote Sensing Letters
2018
Earlier work this paper cites.
S. Georganos, T. Grippa, S. Vanhuysse, M. Lennert, M. Shimoni, S. Kalogirou, and E. Wolff, “Less is more: Optimizing classification performance through feature selection in a very-high-resolution remote sensing object-based urban application,” GIScience & remote sensing
2018
Earlier work this paper cites.
Z. Wang, L. Du, J. Mao, B. Liu, and D. Yang, “Sar target detection based on ssd with data augmentation and transfer learning,” IEEE Geoscience and Remote Sensing Letters
2018
Earlier work this paper cites.
J. Inglada, “Machine learning for land cover map production - follow-up on the tiselac challenge,” 2018
2018
Earlier work this paper cites.
M. Schmitt, L. H. Hughes, C. Qiu, and X. X. Zhu, “SEN12MS – A curated dataset of georeferenced multi-spectral Sentinel-1/2 imagery for deep learning and data fusion,” in ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci
2019
Earlier work this paper cites.
C. M. Gevaert, D. Kohli, and M. Kuffer, “Challenges of mapping the missing spaces,” in Proc. of the Joint Urban Remote Sensing Event (JURSE)
2019
Earlier work this paper cites.
A. Ghorbani and J. Zou, “Data shapley: Equitable valuation of data for machine learning,” in Proc. of the International Conference on Machine Learning
2019
Earlier work this paper cites.
Y. Zhang, F. Wen, Z. Gao, and X. Ling, “A coarse-to-fine framework for cloud removal in remote sensing image sequence,” IEEE Transactions on Geoscience and Remote Sensing
2019
Earlier work this paper cites.
A. Kirsch, J. Van Amersfoort, and Y. Gal, “Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning,” Advances in neural information processing systems
2019
Earlier work this paper cites.
J. M. Johnson and T. M. Khoshgoftaar, “Survey on deep learning with class imbalance,” Journal of Big Data
2019
Earlier work this paper cites.
H. Meyer, C. Reudenbach, S. Wöllauer, and T. Nauss, “Importance of spatial predictor variable selection in machine learning applications–moving from data reproduction to spatial prediction,” Ecological Modelling
2019
Earlier work this paper cites.
R. Hadsell, D. Rao, A. A. Rusu, and R. Pascanu, “Embracing change: Continual learning in deep neural networks,” Trends in Cognitive Sciences
2020
Earlier work this paper cites.
A. Elmes, H. Alemohammad, R. Avery, K. Caylor, J. R. Eastman, L. Fishgold, M. A. Friedl, M. Jain, D. Kohli, J. C. Laso Bayas, et al
2020
Earlier work this paper cites.
J. E. Vargas-Munoz, S. Srivastava, D. Tuia, and A. X. Falcao, “Openstreetmap: Challenges and opportunities in machine learning and remote sensing,” IEEE Geoscience and Remote Sensing Magazine
2020
Earlier work this paper cites.
Y. Li, Y. Zhang, and Z. Zhu, “Error-tolerant deep learning for remote sensing image scene classification,” IEEE Transactions on Cybernetics
2020
Earlier work this paper cites.
J. Li, Z. Wu, Z. Hu, J. Zhang, M. Li, L. Mo, and M. Molinier, “Thin cloud removal in optical remote sensing images based on generative adversarial networks and physical model of cloud distortion,” ISPRS Journal of Photogrammetry and Remote Sensing
2020
Earlier work this paper cites.
X.-Y. Tong, G.-S. Xia, Q. Lu, H. Shen, S. Li, S. You, and L. Zhang, “Land-cover classification with high-resolution remote sensing images using transferable deep models,” Remote Sensing of Environment
2020
Earlier work this paper cites.
J. Fowler, F. Waldner, and Z. Hochman, “All pixels are useful, but some are more useful: Efficient in situ data collection for crop-type mapping using sequential exploration methods,” International Journal of Applied Earth Observation and Geoinformation
2020
Earlier work this paper cites.
D. Y. Zhang, Y. Huang, Y. Zhang, and D. Wang, “Crowd-assisted disaster scene assessment with human-ai interactive attention,” in Proc. of the AAAI Conference on Artificial Intelligence
2020
Earlier work this paper cites.
E. Saralioglu and O. Gungor, “Crowdsourcing in remote sensing: A review of applications and future directions,” IEEE Geoscience and Remote Sensing Magazine
2020
Cited alongside, same era.
A. Ghorbani, M. Kim, and J. Zou, “A distributional framework for data valuation,” in Proc. of the International Conference on Machine Learning
2020
Cited alongside, same era.
X. X. Zhu, J. Hu, C. Qiu, Y. Shi, J. Kang, L. Mou, H. Bagheri, M. Haberle, Y. Hua, R. Huang, et al
2020
Cited alongside, same era.
R. Roscher, B. Bohn, M. Duarte, and J. Garcke, “Explain it to me–facing remote sensing challenges in the bio-and geosciences with explainable machine learning,” in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
2020
Cited alongside, same era.
G. M. Foody, “Explaining the unsuitability of the kappa coefficient in the assessment and comparison of the accuracy of thematic maps obtained by image classification,” Remote Sensing of Environment
2022
Later among the works it cites.
L. Scheibenreif, J. Hanna, M. Mommert, and D. Borth, “Self-supervised vision transformers for land-cover segmentation and classification,” in Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
2022
Later among the works it cites.
P. Ebel, Y. Xu, M. Schmitt, and X. X. Zhu, “Sen12ms-cr-ts: A remote-sensing data set for multimodal multitemporal cloud removal,” IEEE Transactions on Geoscience and Remote Sensing
2022
Later among the works it cites.
P. Wang, B. Bayram, and E. Sertel, “A comprehensive review on deep learning based remote sensing image super-resolution methods,” Earth-Science Reviews
2022
Later among the works it cites.
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2020
Cited alongside, same era.
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “Simclr: A simple framework for contrastive learning of visual representations,” in Proc. of the International Conference on Learning Representations
2020
Cited alongside, same era.
N. Polyzotis and M. Zaharia, “What can data-centric ai learn from data and ml engineering?,” in Proc. of the Conference on Neural Information Processing Systems
2021
Cited alongside, same era.
V. S. F. Garnot and L. Landrieu, “Panoptic segmentation of satellite image time series with convolutional temporal attention networks,” in Proc. of the IEEE/CVF International Conference on Computer Vision
2021
Cited alongside, same era.
N. Sambasivan, S. Kapania, H. Highfill, D. Akrong, P. Paritosh, and L. M. Aroyo, ““everyone wants to do the model work, not the data work”: Data cascades in high-stakes ai,” in Proc. of the CHI Conference on Human Factors in Computing Systems
2021
Cited alongside, same era.
P. Li, X. Rao, J. Blase, Y. Zhang, X. Chu, and C. Zhang, “Cleanml: A study for evaluating the impact of data cleaning on ml classification tasks,” in Proc. of the IEEE International Conference on Data Engineering (ICDE)
2021
Cited alongside, same era.
K. Fatras, B. B. Damodaran, S. Lobry, R. Flamary, D. Tuia, and N. Courty, “Wasserstein adversarial regularization for learning with label noise,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2021
Cited alongside, same era.
Y. Chen, R. Cao, J. Chen, L. Liu, and B. Matsushita, “A practical approach to reconstruct high-quality landsat ndvi time-series data by gap filling and the savitzky–golay filter,” ISPRS Journal of Photogrammetry and Remote Sensing
2021
Cited alongside, same era.
S. Stadtler, C. Betancourt, and R. Roscher, “Explainable machine learning reveals capabilities, redundancy, and limitations of a geospatial air quality benchmark dataset,” Machine Learning and Knowledge Extraction
2022
Later among the works it cites.
2022
Later among the works it cites.
Y. Chen, L. Ma, D. Yu, H. Zhang, K. Feng, X. Wang, and J. Song, “Comparison of feature selection methods for mapping soil organic matter in subtropical restored forests,” Ecological Indicators
2022
Later among the works it cites.
A. Mumuni and F. Mumuni, “Data augmentation: A comprehensive survey of modern approaches,” Array
2022
Later among the works it cites.
H. Mansourifar, A. Moskowitz, B. Klingensmith, D. Mintas, and S. J. Simske, “Gan-based satellite imaging: A survey on techniques and applications,” IEEE Access
2022
Later among the works it cites.
J. Li, D. Hong, L. Gao, J. Yao, K. Zheng, B. Zhang, and J. Chanussot, “Deep learning in multimodal remote sensing data fusion: A comprehensive review,” International Journal of Applied Earth Observation and Geoinformation
2022
Later among the works it cites.
C. Geiß, A. Rabuske, P. Aravena Pelizari, S. Bauer, and H. Taubenböck, “Selection of unlabeled source domains for domain adaptation in remote sensing,” Array
2022
Later among the works it cites.
J. Castillo-Navarro, B. Le Saux, A. Boulch, N. Audebert, and S. Lefèvre, “Semi-supervised semantic segmentation in earth observation: the minifrance suite, dataset analysis and multi-task network study,” Machine Learning
2022
Later among the works it cites.
C. M. Gevaert and M. Belgiu, “Assessing the generalization capability of deep learning networks for aerial image classification using landscape metrics,” International Journal of Applied Earth Observation and Geoinformation
2022
Later among the works it cites.
X. Zhu, W. Zhan, J. Zhou, X. Chen, Z. Liang, S. Xu, and J. Chen, “A novel framework to assess all-round performances of spatiotemporal fusion models,” Remote Sensing of Environment
2022
Later among the works it cites.
Z. Fang, Y. Yang, Z. Li, W. Li, Y. Chen, L. Ma, and Q. Du, “Confident learning-based domain adaptation for hyperspectral image classification,” IEEE Transactions on Geoscience and Remote Sensing
2022
Later among the works it cites.
I. F. Ilyas and T. Rekatsinas, “Machine learning and data cleaning: Which serves the other?,” ACM Journal of Data and Information Quality (JDIQ)
2022
Later among the works it cites.
S. Eyuboglu, M. Varma, K. K. Saab, J.-B. Delbrouck, C. Lee-Messer, J. Dunnmon, J. Zou, and C. Re, “Domino: Discovering systematic errors with cross-modal embeddings,” in Proc. of the International Conference on Learning Representations
2022
Later among the works it cites.
I. Kraljevski, C. Tschöpe, and M. Wolff, “Limits and prospects of big data and small data approaches in ai applications,” KI-Kritik/AI Critique Volume 4
2023
Closest in time.
M. Schmitt, S. A. Ahmadi, Y. Xu, G. Taşkın, U. Verma, F. Sica, and R. Hänsch, “There are no data like more data: Datasets for deep learning in earth observation,” IEEE Geoscience and Remote Sensing Magazine
2023
Closest in time.
2023
Closest in time.
G. Mai, W. Huang, J. Sun, S. Song, D. Mishra, N. Liu, S. Gao, T. Liu, G. Cong, Y. Hu, et al
2023
Closest in time.
M. H. Jarrahi, A. Memariani, and S. Guha, “The principles of data-centric ai,” Communications of the ACM
2023
Closest in time.
S. Falk and A. van Wynsberghe, “Challenging ai for sustainability: what ought it mean?,” AI and Ethics
2023
Closest in time.
G. Machado, M. B. Pereira, K. Nogueira, and J. A. dos Santos, “Facing the void: Overcoming missing data in multi-view imagery,” IEEE Access
2023
Closest in time.
J. Gawlikowski, C. R. N. Tassi, M. Ali, J. Lee, M. Humt, J. Feng, A. Kruspe, R. Triebel, P. Jung, R. Roscher, et al
2023
Closest in time.
B. Ekim, T. T. Stomberg, R. Roscher, and M. Schmitt, “Mapinwild: A remote sensing dataset to address the question of what makes nature wild [software and data sets],” IEEE Geoscience and Remote Sensing Magazine
2023
Closest in time.
T. T. Stomberg, J. Leonhardt, I. Weber, and R. Roscher, “Recognizing protected and anthropogenic patterns in landscapes using interpretable machine learning and satellite imagery,” Frontiers in Artificial Intelligence
2023
Closest in time.
J. T. Wang and R. Jia, “Data banzhaf: A robust data valuation framework for machine learning,” in Proc. of the International Conference on Artificial Intelligence and Statistics
2023
Closest in time.
2023
Closest in time.
J. Kierdorf and R. Roscher, “Reliability scores from saliency map clusters for improved image-based harvest-readiness prediction in cauliflower,” IEEE Geoscience and Remote Sensing Letters
2023
Closest in time.
Z. Xi, X. He, Y. Meng, A. Yue, J. Chen, Y. Deng, and J. Chen, “A multilevel-guided curriculum domain adaptation approach to semantic segmentation for high-resolution remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing
2023
Closest in time.
J. Prexl and M. Schmitt, “Multi-modal multi-objective contrastive learning for sentinel-1/2 imagery,” in Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
Y. Zhang, F. Shen, X. Sun, and K. Tan, “Marine big data-driven ensemble learning for estimating global phytoplankton group composition over two decades (1997–2020),” Remote Sensing of Environment
2023
Closest in time.
M. Rußwurm, L. H. Hughes, G. Pasquali, C. O. Dumitru, and D. Tuia, “Detection of settlements in tanzania and mozambique by many regional few-shot models,” in Proc. of the IEEE International Geoscience and Remote Sensing Symposium
2023
Closest in time.
F. Bastani, P. Wolters, R. Gupta, J. Ferdinando, and A. Kembhavi, “Satlaspretrain: A large-scale dataset for remote sensing image understanding,” in Proc. of the IEEE/CVF International Conference on Computer Vision
2023
Closest in time.
M. Rußwurm, S. J. Venkatesa, and D. Tuia, “Large-scale detection of marine debris in coastal areas with sentinel-2,” iScience
2023
Closest in time.
G. Varoquaux and O. Colliot, “Evaluating machine learning models and their diagnostic value,” in Machine Learning for Brain Disorders
2023
Closest in time.
E. Rolf, “Evaluation challenges for geospatial ml,” in Proc. of the ICLR Workshop on Machine Learning for Remote Sensing
2023
Closest in time.
D. Hong, B. Zhang, X. Li, Y. Li, C. Li, J. Yao, N. Yokoya, H. Li, P. Ghamisi, X. Jia, et al
2024
Closest in time.
D. Wang, M. Hu, Y. Jin, Y. Miao, J. Yang, Y. Xu, X. Qin, J. Ma, L. Sun, C. Li, et al
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.