Automated classification of remote sensing data is an integral tool for
earth scientists, and deep learning has proven very successful at
solving such problems. However, building deep learning models to process
the data requires expert knowledge of machine learning. We introduce
DELTA, a software toolkit to bridge this technical gap and make deep
learning easily accessible to earth scientists. Visual feature
engineering is a critical part of the machine learning lifecycle, and
hence is a key area that will be automated by DELTA. Hand-engineered
features can perform well, but require a cross functional team with
expertise in both machine learning and the specific problem domain,
which is costly in both researcher time and labor. The problem is more
acute with multispectral satellite imagery, which requires considerable
computational resources to process. In order to automate the feature
learning process, a neural architecture search samples the space of
asymmetric and symmetric autoencoders using evolutionary algorithms.
Since denoising autoencoders have been shown to perform well for feature
learning, the autoencoders are trained on various levels of noise and
the features generated by the best performing autoencoders evaluated
according to their performance on image classification tasks. The
resulting features are demonstrated to be effective for Landsat-8 flood
mapping, as well as benchmark datasets CIFAR10 and SVHN.