IEEE GRSS-YP & ISPRS Student Consortium Summer School 2018

by Charles Jjuuko (comments: 0)

IEEE/GRSS-Young Professionals & ISPRS Student Consortium Summer School in Brazil

This is the fourth edition of the event so called “IEEE/GRSS-Young Professionals & ISPRS WG V/5 and SC Summer School”, which will be held on October 29 - November 1 in Campo Grande, MS, Brazil, with the theme: UAV Photogrammetry and Machine Learning Applications: Emerging Trends and Challenges for Earth Observation.

Remote Sensing and Photogrammetry make it possible to map and monitor natural resources through orbital images or through images collected with UAV. They also support precision agriculture, what leads to a greater productive efficiency, and reduce the need on use of pesticides. In this sense, it becomes of great importance to acknowledge what exists in the most recent researches (state of the art) both in the technological aspect and in the methods/techniques of Photogrammetry, Remote Sensing and Machine Learning. The event has the support of ISPRS (International Society for Photogrammetry and Remote Sensing) and IEEE GRSS (Geoscience & Remote Sensing Society).

The event will be organized in two blocks. The first, lasting one day, follows the model of GRSS Young Professionals. It is intended to guide the careers of young professionals who have been graduated up to 15 years and consists of lectures and interactive sessions delivered and moderated by prominent professionals active in business, education/research institutions and academia.

The second block, lasting three days, will follow the model of the ISPRS Summer School, aiming to transmit technical/scientific knowledge on the selected topic. In this block, presentations will be performed by three renowned speakers with great technical and scientific experience in the field: Dr. Farid Melgani (University of Trento), Dr. Franz Rottensteiner (Leibniz University Hannover) and Dra. Anette Eltner (TU Dresden).

The following topics will be addressed: UAV Photogrammetry, Generative classifiers and Artificial Neural Networks.


Further details are available in

Go back

Add a comment