TY - CONF A1 - Winkler, Nicolas P. A1 - Lilienthal, A. J. A1 - Schaffernicht, E. A1 - Neumann, Patrick P. T1 - DeepGDM: Deep Learning for Gas Distribution Mapping N2 - Gas distribution mapping (GDM) is a valuable tool for monitoring the distribution of gases in various applications, including environmental monitoring, emergency response, and industrial safety. While GDM is actively researched in the scope of gas-sensitive mobile robots (Mobile Robot Olfaction), there is a potential for broader applications utilizing sensor networks. This presentation gives an overview of the different approaches to GDM and motivate the use of a deep-neural network-based approach. As access to ground truth representations of gas distributions remains a challenge in GDM research, an approach for the simulation of realistic-shaped synthetic gas plumes is described, which was used for training Gas Distribution Decoder, a deep neural network for spatial interpolation of spatially sparse gas measurements. T2 - AASS Seminar CY - Örebro, Sweden DA - 08.06.2023 KW - Deep learning KW - Mobile robot olfaction KW - Environmental monitoring PY - 2023 AN - OPUS4-57635 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -