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Dr. Takashi Fuse

Vice Chair, ISPRS Technical Commission II

Takashi Fuse received the B.S., M.S., and Ph.D. degrees in civil engineering from The University of Tokyo, Tokyo, Japan, in 1997, 1999, and 2002, respectively. From 2002 to 2003, he was a Research Fellow with the Japan Society for the Promotion of Science. From 2003 to 2005, he was a Research Associate with the Department of Civil Engineering, The University of Tokyo, and from 2005 to 2007 as an Assistant Professor. From 2007 to 2010, he was with the National Institute for Land and Infrastructure Management. In 2010, he joined the Department of Civil Engineering, The University of Tokyo, as an Associate Professor, and from 2017 as a Professor. He also has acted as ISPRS Com. II Vice-President (2016-) and Com.V WG V/4 Co-Chair (2012-2016), JSPRS Secretary General (2016-), and so on. His research interests include applications of photogrammetry for regional planning and transportation, data and simulation integration, statistical latent structure estimation, sequential image processing.

Abstract of Speech

Observation data from diverse sensors which are informative to understand various phenomena is accumulated these days thanks to the development of sensing technology. Meanwhile some simulation models in various field have made progress recently. The integration both of the observation data and simulation models is expected to contribute more precise estimation. This presentation introduces data assimilation process, widely used in many fields of geosciences. A data assimilation system consists of observations, forecasting (corresponds to simulation) and filtering (corresponds to error correction based on the observations). This data assimilation system can be described in a form of general state space model. The components of the general state space model can be defined according to the objectives. The presentation also includes some applications, such as self-localization of mobile device, target detection from full-waveform laser scanner, human tracking, anomaly detection in human dynamics.