The increasing use of deep learning techniques has reduced interpretation time and, ideally, reduced interpreter bias by automatically deriving geological maps from digital outcrop models. However, accurate validation of these automated mapping approaches is a significant challenge due to the subjective nature of geological mapping and the difficulty in collecting quantitative validation data. Additionally, many state-of-the-art deep learning methods are limited to 2D image data, which is insufficient for 3D digital outcrops, such as hyperclouds. To address these challenges, we present Tinto, a multi-sensor benchmark digital outcrop dataset designed to facilitate the development and validation of deep learning approaches for geological mapping, especially for non-structured 3D data like point clouds. Tinto comprises two complementary sets: 1) a real digital outcrop model from Corta Atalaya (Spain), with spectral attributes and ground-truth data, and 2) a synthetic twin that uses latent features in the original datasets to reconstruct realistic spectral data (including sensor noise and processing artifacts) from the ground-truth. The point cloud is dense and contains 3,242,964 labeled points. We used these datasets to explore the abilities of different deep learning approaches for automated geological mapping. By making Tinto publicly available, we hope to foster the development and adaptation of new deep learning tools for 3D applications in Earth sciences. The dataset can be accessed through this link: https://doi.org/10.14278/rodare.2256.

Deepfake images and videos pose a significant threat to biometric systems used for remote identity verification, and existing liveness technologies can detect them, making an attack vector for non-deepfakes a vulnerability businesses need to be aware of. 'Why Deepfakes aren't the Real Challenge for Remote Biometrics' was presented by Ann-Kathrin Freiberg of BioID in the latest lunch talk presented by the European Association for Biometrics (EAB). More than 250 attendees from more than 40 countries around the world pre-registered for the presentation, many of whom were highly engaged in discussion throughout. The origin of the term based on the use of deep learning to manipulate or fake an image, video or audio file was reviewed, and Freiberg shared several examples of deepfakes, including a morph fake created by a BioID employee from a free app and a single image found on the internet. Some basic tips for spotting deepfake videos were shared, such as observing the transition between different areas of the face and head, and frequency or lack of blinking.

In this episode, Audrow Nash interviews Robert Lösch, Ali Marjovi, and Sophia Sakr about the work they presented at the 2018 International Conference on Intelligent Robots and Systems (IROS) in Madrid, Spain. Robert Lösch is a PhD Student at Technische Universität Bergakademie Freiberg (TU Freiberg) in Germany, and he speaks on an approach to have robots navigate mining environments. Ali Marjovi is a Post doc at the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland, and he speaks about on how robots could be used to localize odors, which could be useful for finding explosives or for search-and-rescue. Marjovi discusses how odor localization works, his experimental setup, the challenges of odor localization, and on giving robots a sense of smell. Sophia Sakr, from Institut des Systèmes Intelligents et de Robotique (ISIR) in France, speaks about a haptic pair of tweezers (designed by Thomas Daunizeau).