# Multi-Task Learning of Height and Semantics from Aerial Images

arXiv: https://arxiv.org/abs/1911.07543 · HTML: https://mlsanity.com/paper/1911.07543/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2019.

## Built on by (most cited first, 14 of 14)

- [Shadow Neural Radiance Fields for Multi-view Satellite Photogrammetry](https://mlsanity.com/paper/2104.09877/) (2021)
- [Sub-Meter Tree Height Mapping of California using Aerial Images and LiDAR-Informed U-Net Model](https://mlsanity.com/paper/2306.01936/) (2023)
- [Remote Sensing Novel View Synthesis with Implicit Multiplane Representations](https://mlsanity.com/paper/2205.08908/) (2022)
- [HTC-DC Net: Monocular Height Estimation from Single Remote Sensing Images](https://mlsanity.com/paper/2309.16486/) (2023)
- [Co-Training Vision Language Models for Remote Sensing Multi-task Learning](https://mlsanity.com/paper/2511.21272/) (2025)
- [Estimating optical vegetation indices and biophysical variables for temperate forests with Sentinel-1 SAR data using machine learning techniques: A case study for Czechia](https://mlsanity.com/paper/2311.07537/) (2023)
- [Seabed-Net: A multi-task network for joint bathymetry estimation and seabed classification from remote sensing imagery in shallow waters](https://mlsanity.com/paper/2510.19329/) (2025)
- [TSE-Net: Semi-supervised Monocular Height Estimation from Single Remote Sensing Images](https://mlsanity.com/paper/2511.13552/) (2025)
- [Focusing on Shadows for Predicting Heightmaps from Single Remotely Sensed RGB Images with Deep Learning](https://mlsanity.com/paper/2104.10874/) (2021)
- [Mapping Vulnerable Populations with AI](https://mlsanity.com/paper/2107.14123/) (2021)
- [Automatic satellite building construction monitoring](https://mlsanity.com/paper/2209.15084/) (2022)
- [Virtual Accessory Try-On via Keypoint Hallucination](https://mlsanity.com/paper/2310.17131/) (2023)
- [Multitask Learning for Earth Observation Data Classification with Hybrid Quantum Network](https://mlsanity.com/paper/2601.22195/) (2026)
- [Estimating Individual Tree Height and Species from UAV Imagery](https://mlsanity.com/paper/2603.23669/) (2026)

## Abstract (arXiv)

Aerial or satellite imagery is a great source for land surface analysis, which might yield land use maps or elevation models. In this investigation, we present a neural network framework for learning semantics and local height together. We show how this joint multi-task learning benefits to each task on the large dataset of the 2018 Data Fusion Contest. Moreover, our framework also yields an uncertainty map which allows assessing the prediction of the model. Code is available at https://github.com/marcelampc/mtl_aerial_images .