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The automatic production of land use/land cover maps continues to be a challenging problem, with important impacts on the ability to promote sustainability and good resource management. The ability to build robust automatic classifiers and produce accurate maps can have a significant impact on the way we manage and optimize natural resources. The difficulty in achieving these results comes from many different factors, such as data quality and uncertainty. In this paper, we address the imbalanced learning problem, a common and difficult conundrum in remote sensing that affects the quality of classification results, by proposing Geometric-SMOTE, a novel oversampling method, as a tool for addressing the imbalanced learning problem in remote sensing. Geometric-SMOTE is a sophisticated oversampling algorithm which increases the quality of the instances generated in previous methods, such as the synthetic minority oversampling technique. The performance of Geometric- SMOTE, in the LUCAS (Land Use/Cover Area Frame Survey) dataset, is compared to other oversamplers using a variety of classifiers. The results show that Geometric-SMOTE significantly outperforms all the other oversamplers and improves the robustness of the classifiers. These results indicate that, when using imbalanced datasets, remote sensing researchers should consider the use of these new generation oversamplers to increase the quality of the classification results.

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In remote sensing, Active Learning (AL) has become an important technique to collect informative ground truth data “on-demand” for supervised classification tasks. Despite its effectiveness, it is still significantly reliant on user interaction, which makes it both expensive and time consuming to implement. Most of the current literature focuses on the optimization of AL by modifying the selection criteria and the classifiers used. Although improvements in these areas will result in more effective data collection, the use of artificial data sources to reduce human–computer interaction remains unexplored. In this paper, we introduce a new component to the typical AL framework, the data generator, a source of artificial data to reduce the amount of user-labeled data required in AL. The implementation of the proposed AL framework is done using Geometric SMOTE as the data generator. We compare the new AL framework to the original one using similar acquisition functions and classifiers over three AL-specific performance metrics in seven benchmark datasets. We show that this modification of the AL framework significantly reduces cost and time requirements for a successful AL implementation in all of the datasets used in the experiment.

Starting from a Yang‐Mills‐Dirac theory defined in ten dimensions we classify the semi‐realistic particle physics models resulting from their Forgacs‐Manton dimensional reduction. The higher‐dimensional gauge group is chosen to be E8. This choice as well as the dimensionality of the space‐time is suggested by the heterotic string theory. Furthermore, we assume that the space‐time on which the theory is defined can be written in the compactified form M4 × B, with M4 the ordinary Minkowski spacetime and B = S/R a 6 ‐ dim homogeneous coset space. We constrain our investigation in those cases where the dimensional reduction leads in four dimensions to phenomenologically interesting and anomaly‐free GUTs such as E6, SO(10) and SU(5). However the four‐dimensional surviving scalars transform in the fundamental of the resulting gauge group are not suitable for the superstrong symmetry breaking of the Standard Model. The main objective of our work is the investigation to which extent the latter can be achieved by employing the Wilson flux breaking mechanism.