Object Classification and Segmentation Based on Deep Learning Using Underwater Mapping Data

This paper presents a fast and accurate classification method for underwater objects using underwater mapping data obtained by a small Autonomous Underwater Vehicle (AUV) and autonomous surface vehicle (ASV). For the mapping data, in addition to underwater acoustic reflection intensity images, water depth data, point cloud data and backscattering reflection intensity data are employed. We propose the automatic classification and semantic segmentation method on deep learning using a convolutional neural network (CNN) and PointNet++. In order to verify the effectiveness of the present method, we applied it to the measured several underwater mapping data

Planet cartography with neural learned regularization

Finding potential life harboring exo-Earths is one of the aims of exoplanetary science. Detecting signatures of life in exoplanets will likely first be accomplished by determining the bulk composition of the planetary atmosphere via reflected/transmitted spectroscopy. However, a complete understanding of the habitability conditions will surely require mapping the presence of liquid water, continents and/or clouds. Spin-orbit tomography is a technique that allows us to obtain maps of the surface of exoplanets around other stars using the light scattered by the planetary surface. We leverage the potential of deep learning and propose a mapping technique for exo-Earths in which the regularization is learned from mock surfaces. The solution of the inverse mapping problem is posed as a deep neural network that can be trained end-to-end with suitable training data. We propose in this work to use methods based on the procedural generation of planets, inspired by what we found on Earth. We also consider mapping the recovery of surfaces and the presence of persistent cloud in cloudy planets. We show that the a reliable mapping can be carried out with our approach, producing very compact continents, even when using single passband observations. More importantly, if exoplanets are partially cloudy like the Earth is, we show that one can potentially map the distribution of persistent clouds that always occur on the same position on the surface (associated to orography and sea surface temperatures) together with non-persistent clouds that move across the surface. This will become the first test one can perform on an exoplanet for the detection of an active climate system. For small rocky planets in the habitable zone of their stars, this weather system will be driven by water, and the detection can be considered as a strong proxy for truly habitable conditions.Comment: 12 pages, 9 figures, accepted for publication in A&A, code on https://github.com/aasensio/neural_exocartograph

Multi-dimensional, multi-national, multi-faceted hydrographic training: the Nippon Foundation GEBCO training program at the University of New Hampshire

Hydrographic training entered a new era when students arrived at the University of New Hampshire in August of 2004 to form the first class of the Nippon Foundation GEBCO (General Bathymetric Chart of the Oceans) training program. Born out of the need to replenish GEBCO’s aging human material, and of the desire to spread deep ocean mapping capabilities more widely throughout the world, the program attracted applications from 57 students in over thirty countries. The seven selected each had post graduate training and several years experience, but differed in that three were hydrographers, two geologists and two oceanographers. Classes planned for the next two years will bring in a further fourteen students. The UNH program had been selected as the closest match to the general course requirements GEBCO considered that ocean bathymetrists should have. Subjects include all types of depth measurements, oceanography, acoustics, tides, plate tectonics, sea floor morphology, ocean basins, sedimentary processes, hydrothermal-thermal processes, gravity-magnetic relationships to seafloor fabrics, positioning and geodesy, maps and charts, IHO standards, GIS, data bases, gridding, contouring, spatial statistics, and the history of GEBCO and ocean mapping. These are taught at the graduate level as part of the graduate degree program at UNH. In this paper, the experiences that participants from the different backgrounds underwent are recounted with the overall goal of improving the general education required to map the floors of the deep ocean. Recommendations are made regarding the prior preparation of students entering the program, the content and intensity of courses comprising the program, and follow-up actions to solidify the learning experience. Intangibles such as the networking of professional contacts are also evaluated. Extrapolations to training in other areas of hydrography are made

The value of remote sensing techniques in supporting effective extrapolation across multiple marine spatial scales

The reporting of ecological phenomena and environmental status routinely required point observations, collected with traditional sampling approaches to be extrapolated to larger reporting scales. This process encompasses difficulties that can quickly entrain significant errors. Remote sensing techniques offer insights and exceptional spatial coverage for observing the marine environment. This review provides guidance on (i) the structures and discontinuities inherent within the extrapolative process, (ii) how to extrapolate effectively across multiple spatial scales, and (iii) remote sensing techniques and data sets that can facilitate this process. This evaluation illustrates that remote sensing techniques are a critical component in extrapolation and likely to underpin the production of high-quality assessments of ecological phenomena and the regional reporting of environmental status. Ultimately, is it hoped that this guidance will aid the production of robust and consistent extrapolations that also make full use of the techniques and data sets that expedite this process