# LIGHT: Joint Individual Building Extraction and Height Estimation from Satellite Images through a Unified Multitask Learning Network

arXiv: https://arxiv.org/abs/2304.01090 · HTML: https://mlsanity.com/paper/2304.01090/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 9 references that are in the map.
- First posted on arXiv 3 Apr 2023.

## Builds on (most cited first, 3 of 3)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Elevation Estimation-Driven Building 3D Reconstruction from Single-View Remote Sensing Imagery](https://mlsanity.com/paper/2301.04581/) (2023)

## Built on by (most cited first, 5 of 5)

- [Not Just Learning from Others but Relying on Yourself: A New Perspective on Few-Shot Segmentation in Remote Sensing](https://mlsanity.com/paper/2310.12452/) (2023)
- [Enhancing Monocular Height Estimation via Sparse LiDAR-Guided Correction](https://mlsanity.com/paper/2505.06905/) (2025)
- [Self-guided Few-shot Semantic Segmentation for Remote Sensing Imagery Based on Large Vision Models](https://mlsanity.com/paper/2311.13200/) (2023)
- [Mask-to-Height: A YOLOv11-Based Architecture for Joint Building Instance Segmentation and Height Classification from Satellite Imagery](https://mlsanity.com/paper/2510.27224/) (2025)
- [BuildMamba: A Visual State-Space Based Model for Multi-Task Building Segmentation and Height Estimation from Satellite Images](https://mlsanity.com/paper/2603.08523/) (2026)

## Abstract (arXiv)

Building extraction and height estimation are two important basic tasks in remote sensing image interpretation, which are widely used in urban planning, real-world 3D construction, and other fields. Most of the existing research regards the two tasks as independent studies. Therefore the height information cannot be fully used to improve the accuracy of building extraction and vice versa. In this work, we combine the individuaL buIlding extraction and heiGHt estimation through a unified multiTask learning network (LIGHT) for the first time, which simultaneously outputs a height map, bounding boxes, and a segmentation mask map of buildings. Specifically, LIGHT consists of an instance segmentation branch and a height estimation branch. In particular, so as to effectively unify multi-scale feature branches and alleviate feature spans between branches, we propose a Gated Cross Task Interaction (GCTI) module that can efficiently perform feature interaction between branches. Experiments on the DFC2023 dataset show that our LIGHT can achieve superior performance, and our GCTI module with ResNet101 as the backbone can significantly improve the performance of multitask learning by 2.8% AP50 and 6.5% delta1, respectively.