  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 modeling
is presented.
