The educational needs and interest of society in technological fields are rapidly changing. Basic higher education is commonly not sufficient to meet educational needs in the field of remote sensing. The SPatial LITeracy Remote Sensing (SPLITRS) Summer School is an international and interactive event between universities, research institutions and private firms that considers state-of-the-art remote sensing technology, sophisticated and comprehensive modeling approaches, data analysis, and sophisticated software capabilities – all of which is incorporated in the framework of building a well-designed strategy to protect natural resources and public well-being.
Dr. Claudia Notarnicola, Italy
Ms. Paola Winkler, Italy
Dr. Anita Simic Milas, USA
Dr. Nicolas Younan, USA
Dr. Konstantinos Ntouros, Greece
Dr. Ivan Balenovic, Croatia
Dr. Mateo Gasparovic, Croatia
Mr. Luka Jurjevic, Croatia
Dr. Claudia Notarnicola received the Degree in Physics, summa cum laude, and the PhD in Physics from the University of Bari (Italy) in 1995 and 2002 respectively. She is presently the vice-head of the Eurac Research-Institute of Applied Remote Sensing (Bolzano, Italy). Within the same institute she is leader of a group dealing with remote sensing applications in SAR and optical domain for soil and vegetation monitoring as well as integration of remotely sensed observations with models and ground measurements. Her main research interest includes biophysical parameters (soil moisture, vegetation, snow) retrieval by using optical images and SAR images, optical and SAR data processing, data fusion and electromagnetic models. She conducts research on these topics within the frameworks of several national and international projects. Among the others, she is involved in the Cassini-Huygens Project for the application of inversion procedure to the estimation of Titan surface parameters. She is a referee for IEEE and other international journals and since 2006, she serves as Conference Chairs for SPIE International Conference on “SAR Image Analysis, Modeling and Techniques”.
Dr. Martin Isenburg received his MSc in 1999 from the University of British Columbia in Vancouver, Canada and his PhD in 2004 from the University of North Carolina at Chapel Hill, USA – both in Computer Science. Currently, he is an independent scientist, lecturer, and research consultant. Dr. Isenburg has created a popular suite of LiDAR processing software modules called LAStools that is the flagship product of rapidlasso GmbH, the company he founded in 2012. The LAStools software suite has deep market penetration and is heavily used in industry, government agencies, research labs, and educational institutions. These highly efficient LiDAR processing tools are known for their high productivity. They combine robust algorithms with efficient I/O and clever memory management to achieve high throughput for data sets containing billions of points. See http://rapidlasso.com for more information.
Dr. Ribana Roscher received her Diploma and PhD in Geodesy from the University of Bonn, Germany in 2008 and 2012, respectively. During 2012 and 2015 she was a Postdoctoral Researcher at Julius Kühn-Insitut (Institute for Grapevine Breeding) in Siebeldingen, Humboldt Innovation in Berlin and Freie Universität Berlin. In 2015 she was a Visiting Researcher at The Fields Institute for Research in Mathematical Sciences, Toronto, Canada. Since 2015 she is a Junior Professor for Remote Sensing at the University of Bonn. In her research, she aims at the development of pattern recognition methods, which are particularly designed for the analysis of large scale remote sensing data. She specifically focusses on efficient classification methods, techniques for sophisticated feature learning and the integration of prior knowledge such as spatial and temporal information. A central idea in her research is to develop methods which ensure a high discrimination power and at the same time model the underlying structure of the data, since such methods are a prerequisite for the automatic analysis of Earth observation data.