Remote-sensing image scene classification can provide significant value, ranging from forest fire monitoring to land-use and land-cover classification. Beginning with the first aerial photographs of the early 20th century to the satellite imagery of today, the amount of remote-sensing data has increased geometrically with a higher resolution. The need to analyze these modern digital data motivated research to accelerate remote-sensing image classification. Fortunately, great advances have been made by the computer vision community to classify natural images or photographs taken with an ordinary camera. Natural image datasets can range up to millions of samples and are, therefore, amenable to deep-learning techniques. Many fields of science, remote sensing included, were able to exploit the success of natural image classification by convolutional neural network models using a technique commonly called transfer learning. We provide a systematic review of transfer learning application for scene classification using different datasets and different deep-learning models. We evaluate how the specialization of convolutional neural network models affects the transfer learning process by splitting original models in different points. As expected, we find the choice of hyperparameters used to train the model has a significant influence on the final performance of the models. Curiously, we find transfer learning from models trained on larger, more generic natural images datasets outperformed transfer learning from models trained directly on smaller remotely sensed datasets. Nonetheless, results show that transfer learning provides a powerful tool for remote-sensing scene classification.

Accurate taxonomic classification of microfossils in thin-sections is an important biostratigraphic procedure. As paleontological expertise is typically restricted to specific taxonomic groups and experts are not present in all institutions, geoscience researchers often suffer from lack of quick access to critical taxonomic knowledge for biostratigraphic analyses. Moreover, diminishing emphasis on education and training in systematics poses a major challenge for the future of biostratigraphy, and on associated endeavors reliant on systematics. Here we present a machine learning approach to classify and organize fusulinids—microscopic index fossils for the late Paleozoic. The technique we employ has the potential to use such important taxonomic knowledge in models that can be applied to recognize and categorize fossil specimens. Our results demonstrate that, given adequate images and training, convolutional neural network models can correctly identify fusulinids with high levels of accuracy. Continued efforts in digitization of biological and paleontological collections at numerous museums and adoption of machine learning by paleontologists can enable the development of highly accurate and easy-to-use classification tools and, thus, facilitate biostratigraphic analyses by non-experts as well as allow for cross-validation of disparate collections around the world. Automation of classification work would also enable expert paleontologists and others to focus efforts on exploration of more complex interpretations and concepts.

We present the recent progress in upgrading a predictive model for megaelectron‐volt (MeV) electrons inside the Earth's outer Van Allen belt. This updated model, called PreMevE 2.0, provides improved forecasts, particularly at outer L‐shells, by adding upstream solar wind speeds to the model's input parameter list that originally includes precipitating electrons observed at low Earth orbits and MeV electron fluxes in situ measured by a geosynchronous satellite. Furthermore, based on several kinds of linear and artificial neural networks algorithms, a list of models was constructed, trained, validated, and tested with 42‐month MeV electron observations from Van Allen Probes. Out‐of‐sample test results from these models show that, with optimized model hyperparameters and input parameter combinations, the top performer from each category of models has the similar capability of making reliable 1‐day (2‐day) forecasts of 1‐MeV electron flux distributions with performance efficiency values ~0.87 (~0.82) averaged over the L‐shell range of 2.8–6.6, significantly outperforming the previous version of PreMevE particularly at L‐shells > ~4.5. Interestingly, the linear regression model is often the most successful when compared to other models, which suggests the relationship between dynamics of trapped 1‐MeV electrons and precipitating electrons is dominated by linear components. Results also show that PreMevE 2.0 can reasonably well predict the onsets of MeV electron events in 2‐day forecasts. PreMevE 2.0 is designed to be driven by observations from longstanding space infrastructure to make high‐fidelity forecasts for MeV electrons, thus can be an invaluable space weather forecasting tool for the future.