Deep artificial neural networks require a large corpus of training data in order to effectively learn, where collection of such training data is often expensive and laborious. Data augmentation overcomes this issue by artificially inflating the training set with label preserving transformations. Recently there has been extensive use of generic data augmentation to improve Convolutional Neural Network (CNN) task performance. This study benchmarks various popular data augmentation schemes to allow researchers to make informed decisions as to which training methods are most appropriate for their data sets. Various geometric and photometric schemes are evaluated on a coarse-grained data set using a relatively simple CNN. Experimental results, run using 4-fold cross-validation and reported in terms of Top-1 and Top-5 accuracy, indicate that cropping in geometric augmentation significantly increases CNN task performance.
Extreme events such as storms, floods, landslides, and volcanic eruptions can redistribute huge volumes of sediment in landscape systems. These geomorphic impacts tend to be studied in landscapes with minimal human presence, infrastructure, or intervention, to reduce confounding factors on sediment transport. However, human domination of natural environments means that unbuilt conditions now represent exceptional circumstances (
Key points:1) We measure agreement among coastal scientists labeling the same sets of post-storm images.2) Coastal scientists agree more when rating landforms, less when labeling inferred processes.3) Iterating on questions, providing documentation, and using smaller image sizes all increase agreement.
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