Home Workshops - 01 - Azimi, Bahmanyar, Henry


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"Object Segmentation and Detection in Remote Sensing Images Based on Deep Learning Methods: Theory and Practice Using PyTorch Framework"


High resolution remote sensing images contain detailed information, making it possible to recognize objects within them. This interactive tutorial will begin by introducing some of the state-of-the-art detection and segmentation methods based on deep learning and explaining their building blocks. The focus will be on road, building, and vehicle classes as well as crowd detection and density estimation, which will be highlighted with visual demonstrations. This will be followed by a quick hands-on experiment involving the audience. During the tutorial, some networks will be processed live, and their training process as well as their results will be analyzed and discussed jointly with the audience. Concluding this interactive tutorial will be a live demonstration of real-time object detection on an embedded board.

The PyTorch framework will be used throughout the tutorial.

                                                                                                                                                         
                                                                       

Workshop date: 15th Oct. 2019   8:30-12:30

                   

                            


Presenters Information


             


Seyed Majid Azimi

Seyed Majid Azimi is a researcher with the Department of Photogrammetry and Image Analysis, Remote Sensing Technology Institute, German Aerospace Center (DLR), Weßling, Bayern, Germany. His research interests include (embedded) computer vision, artificial intelligence (AI) and machine learning for object detection, segmentation, and tracking from remote sensing data using Deep Learning methods in traffic/infrastructure monitoring and disaster/catastrophe management applications.

         


Dr. Reza Bahmanyar

Reza Bahmanyar is a research associate at the Photogrammetry and Image Analysis department of the German Aerospace Center (DLR), holding M.Sc. and Ph.D. degrees in Computer Sciences from Saarland University (Saarbrücken) in 2012 and Technical University of Munich in 2016, respectively. His main research interests include Machine Learning, Computer Vision, Image Processing, Data Mining, and Artificial Intelligence with the application in the Remote Sensing domain.

    

         

   


Corentin Henry

Corentin Henry is pursuing a Ph.D. degree at the Department of Photogrammetry and Image Analysis in the Remote Sensing Technology Institute of the German Aerospace Center (DLR-IMF). He received a M.Sc. degree in Computer Science from ISEN-Lille Engineering School, Lille, France, in 2017. His main research topic is Computer Vision and focuses on the application of Deep Learning to Remote Sensing tasks, especially to the extraction of roads topology in aerial imagery.