The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIV-M-3-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-M-3-2021, 1–5, 2021
https://doi.org/10.5194/isprs-archives-XLIV-M-3-2021-1-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-M-3-2021, 1–5, 2021
https://doi.org/10.5194/isprs-archives-XLIV-M-3-2021-1-2021

  10 Aug 2021

10 Aug 2021

MULTI-TASK LEARNING FROM FIXED-WING UAV IMAGES FOR 2D/3D CITY MODELLING

M. R. Bayanlou1 and M. Khoshboresh-Masouleh2 M. R. Bayanlou and M. Khoshboresh-Masouleh
  • 1Aerospace Engineering Department, Sharif University of Technology, Tehran, Iran
  • 2School of Surveying and Geospatial Eng., College of Eng., University of Tehran, Tehran, Iran

Keywords: Multi-Task Learning, 2D/3D City Modelling, Fixed-Wing UAV, SAMA-VTOL

Abstract. 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.