Deep learning for geometric and semantic tasks in photogrammetry and remote sensing. Issue 1 (2nd January 2020)

• Record Type: Journal Article
• Authors: Heipke, Christian; Rottensteiner, Franz
• Is Part Of: Geo-spatial information science. Volume 23:Issue 1(2020)
• Page Start: 10
• Page End: 19
• Publication Date: 2020-01-02
• Subjects: Deep learning -- machine learning -- convolutional neural networks(CNN) -- example project from IPI; Geographic information systems -- Periodicals; Cartography -- Data processing -- Periodicals; Surveying -- Data processing -- Periodicals; Remote sensing -- Periodicals; 526.0285
• DOI: 10.1080/10095020.2020.1718003
• Languages: English
• ISSNs: 1009-5020

Abstract

ABSTRACT: During the last few years, artificial intelligence based on deep learning, and particularly based on convolutional neural networks, has acted as a game changer in just about all tasks related to photogrammetry and remote sensing. Results have shown partly significant improvements in many projects all across the photogrammetric processing chain from image orientation to surface reconstruction, scene classification as well as change detection, object extraction and object tracking and recognition in image sequences. This paper summarizes the foundations of deep learning for photogrammetry and remote sensing before illustrating, by way of example, different projects being carried out at the Institute of Photogrammetry and GeoInformation, Leibniz University Hannover, in this exciting and fast moving field of research and development.