Deep Neural Network Regression for Normalized Digital Surface Model Generation With Sentinel-2 Imagery  Reference: Print ISSN: 1939-1404 Electronic ISSN: 2151-1535   ... View more

Utilizing Flood Inundation Observations to Obtain Floodplain Topography in Data-Scarce Regions    Keywords: digital elevation model, flood modeling, data assimilation, remote sensing, floodplains Citation: Shastry A and Durand M (2019) Utilizing Flood Inundation Observations to Obtain Floodplain Topography in Data-Scarce Regions. Front. Earth Sci. 6:243. doi: 10.3389/feart.2018.00243 Received: 06 October 2018; Accepted: 17 December 2018; Published: 11 January 2019. Edited by: Paul Bates, University of Bristol, United Kingdom Reviewed by: Andrew Mark Ireson, University of Saskatchewan, Canada Juan Pablo Rodríguez Sánchez, University of Los Andes, Colombia Copyright © 2019 Shastry and Durand. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. *Correspondence: Apoorva Shastry, shastry.7@osu.edu   Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher. ... View more

Neural state space alignment for magnitude generalization in humans and recurrent networks  Reference: Hannah Sheahan, Fabrice Luyckx, Stephanie Nelli, Clemens Teupe, Christopher Summerfield, Neural state space alignment for magnitude generalization in humans and recurrent networks, Neuron, Volume 109, Issue 7, 2021, Pages 1214-1226.e8, ISSN 0896-6273, https://doi.org/10.1016/j.neuron.2021.02.004. (https://www.sciencedirect.com/science/article/pii/S0896627321000787) Abstract: Summary A prerequisite for intelligent behavior is to understand how stimuli are related and to generalize this knowledge across contexts. Generalization can be challenging when relational patterns are shared across contexts but exist on different physical scales. Here, we studied neural representations in humans and recurrent neural networks performing a magnitude comparison task, for which it was advantageous to generalize concepts of “more” or “less” between contexts. Using multivariate analysis of human brain signals and of neural network hidden unit activity, we observed that both systems developed parallel neural “number lines” for each context. In both model systems, these number state spaces were aligned in a way that explicitly facilitated generalization of relational concepts (more and less). These findings suggest a previously overlooked role for neural normalization in supporting transfer of a simple form of abstract relational knowledge (magnitude) in humans and machine learning systems. Keywords: magnitude; alignment; representation; number; parietal cortex; generalization; normalization; neural network   ... View more

nDSMs: How digital surface models and digital terrain models elevate your insights  https://www.esri.com/partners/up42-gmbh-a2T5x000008Dm9vEAC @AndresHernandezUp42  @rosenjambi  ... View more

Everything you need to know about Digital Elevation Models (DEMs), Digital Surface Models (DSMs), and Digital Terrain Models (DTMs)  @AndresHernandezUp42  https://www.esri.com/partners/up42-gmbh-a2T5x000008Dm9vEAC   ... View more