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

arXiv: https://arxiv.org/abs/2109.00918 · HTML: https://mlsanity.com/paper/2109.00918/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 25 Aug 2021.

## Builds on (most cited first, 5 of 5)

- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [SAMA-VTOL: A new unmanned aircraft system for remotely sensed data collection](https://mlsanity.com/paper/2011.11007/) (2020)
- [Robust building footprint extraction from big multi-sensor data using deep competition network](https://mlsanity.com/paper/2011.02879/) (2020)

## Built on by (most cited first, 2 of 2)

- [UniGeoSeg: Towards Unified Open-World Segmentation for Geospatial Scenes](https://mlsanity.com/paper/2511.23332/) (2025)
- [SA-NET.v2: Real-time vehicle detection from oblique UAV images with use of uncertainty estimation in deep meta-learning](https://mlsanity.com/paper/2208.04190/) (2022)

## Abstract (arXiv)

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.