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<p>During the last years, one could see a broad use of machine learning tools and applications. However, when we use these techniques for geophysical analyses, we must be sure that the obtained results are scientifically valid and allow us to derive quantitative outcomes that can be directly compared with other measurements.</p><p>Therefore, we set out to identify typical datasets that lend themselves well to geophysical data interpretation. To simplify this very general task, we concentrate in this contribution on multi-dimensional image data acquired by satellites with typical remote sensing instruments for Earth observation being used for the analysis for:</p><ul><li>Atmospheric phenomena (cloud cover, cloud characteristics, smoke and plumes, strong winds, etc.)</li> <li>Land cover and land use (open terrain, agriculture, forestry, settlements, buildings and streets, industrial and transportation facilities, mountains, etc.)</li> <li>Sea and ocean surfaces (waves, currents, ships, icebergs, coastlines, etc.)</li> <li>Ice and snow on land and water (ice fields, glaciers, etc.)</li> <li>Image time series (dynamical phenomena, their occurrence and magnitude, mapping techniques)</li> </ul><p>Then we analyze important data characteristics for each type of instrument. One can see that most selected images are characterized by their type of imaging instrument (e.g., radar or optical images), their typical signal-to-noise figures, their preferred pixel sizes, their various spectral bands, etc.</p><p>As a third step, we select a number of established machine learning algorithms, available tools, software packages, required environments, published experiences, and specific caveats. The comparisons cover traditional “flat” as well as advanced “deep” techniques that have to be compared in detail before making any decision about their usefulness for geophysical applications. They range from simple thresholding to k-means, from multi-scale approaches to convolutional networks (with visible or hidden layers) and auto-encoders with sub-components from rectified linear units to adversarial networks.</p><p>Finally, we summarize our findings in several instrument / machine learning algorithm matrices (e.g., for active or passive instruments). These matrices also contain important features of the input data and their consequences, computational effort, attainable figures-of-merit, and necessary testing and verification steps (positive and negative examples). Typical examples are statistical similarities, characteristic scales, rotation invariance, target groupings, topic bagging and targeting (hashing) capabilities as well as local compression behavior.</p>

In the last decades, the domain of spatial computing became more and more data-driven, especially when using remote sensing-based images. Furthermore, the satellites provide huge amounts of images, so the number of available datasets is increasing. This leads to the need for large storage requirements and high computational costs when estimating the label scene classification problem using deep learning. This consumes and blocks important hardware recourses, energy, and time. In this paper, the use of aggressive compression algorithms will be discussed to cut the wasted transmission and resources for selected land cover classification problems. To compare the different compression methods and the classification performance, the satellite image patches are compressed by two methods. The first method is the image quantization of the data to reduce the bit depth. Second is the lossy and lossless compression of images with the use of image file formats, such as JPEG and TIFF. The performance of the classification is evaluated with the use of convolutional neural networks like VGG16. The experiments indicated that not all remote sensing image classification problems improve their performance when taking the full available information into account. Moreover, compression can set the focus on specific image features, leading to fewer storage needs and a reduction of computing time with comparably small costs in terms of quality and accuracy. All in all, quantization and embedding into file formats do support convolutional neural networks to estimate the labels of images, by strengthening the features. The code is available at https://github.com/tum-bgd/compression-in-dl