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Multi-task learning from fixed-wing UAV images for 2D/3D city modelling

Bayanlou, M. R ; Sharif University of Technology | 2021

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  1. Type of Document: Article
  2. DOI: 10.5194/isprs-archives-XLIV-M-3-2021-1-2021
  3. Publisher: International Society for Photogrammetry and Remote Sensing , 2021
  4. Abstract:
  5. Single-task learning in artificial neural networks will be able to learn the model very well, and the benefits brought by transferring knowledge thus become limited. In this regard, when the number of tasks increases (e.g., semantic segmentation, panoptic segmentation, monocular depth estimation, and 3D point cloud), duplicate information may exist across tasks, and the improvement becomes less significant. Multi-task learning has emerged as a solution to knowledge-transfer issues and is an approach to scene understanding which involves multiple related tasks each with potentially limited training data. Multi-task learning improves generalization by leveraging the domain-specific information contained in the training data of related tasks. In urban management applications such as infrastructure development, traffic monitoring, smart 3D cities, and change detection, automated multi-task data analysis for scene understanding based on the semantic, instance, and panoptic annotation, as well as monocular depth estimation, is required to generate precise urban models. In this study, a common framework for the performance assessment of multi-task learning methods from fixed-wing UAV images for 2D/3D city modelling is presented. © 2021 International Society for Photogrammetry and Remote Sensing. All rights reserved
  6. Keywords:
  7. Fixed wings ; Knowledge management ; Learning systems ; Neural networks ; Semantics ; Three dimensional computer graphics ; 2d/3d city modeling ; 3D city modeling ; Depth Estimation ; Fixed-wing UAV ; Learn+ ; Multitask learning ; SAMA-VTOL ; Scene understanding ; Semantic segmentation ; Single task learning ; Unmanned aerial vehicles (UAV)
  8. Source: American Society for Photogrammetry and Remote Sensing, ASPRS 2021 Annual Conference, 29 March 2021 through 2 April 2021 ; Volume 44, Issue M-3 , 2021 , Pages 1-5 ; 16821750 (ISSN)
  9. URL: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIV-M-3-2021/1/2021