# Multi-Task Learning for Segmentation of Building Footprints with Deep Neural Networks

arXiv: https://arxiv.org/abs/1709.05932 · HTML: https://mlsanity.com/paper/1709.05932/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2017.

## Built on by (most cited first, 24 of 24)

- [Every Annotation Counts: Multi-label Deep Supervision for Medical Image Segmentation](https://mlsanity.com/paper/2104.13243/) (2021)
- [OmniCity: Omnipotent City Understanding with Multi-level and Multi-view Images](https://mlsanity.com/paper/2208.00928/) (2022)
- [Self-Supervised Learning for Invariant Representations from Multi-Spectral and SAR Images](https://mlsanity.com/paper/2205.02049/) (2022)
- [BuildMapper: A Fully Learnable Framework for Vectorized Building Contour Extraction](https://mlsanity.com/paper/2211.03373/) (2022)
- [HiT: Building Mapping with Hierarchical Transformers](https://mlsanity.com/paper/2309.09643/) (2023)
- [Semi-Supervised Building Footprint Generation with Feature and Output Consistency Training](https://mlsanity.com/paper/2205.08416/) (2022)
- [P2PFormer: A Primitive-to-polygon Method for Regular Building Contour Extraction from Remote Sensing Images](https://mlsanity.com/paper/2406.02930/) (2024)
- [RAG for Geoscience: What We Expect, Gaps and Opportunities](https://mlsanity.com/paper/2508.11246/) (2025)
- [Objective-Dependent Uncertainty Driven Retinal Vessel Segmentation](https://mlsanity.com/paper/2104.08554/) (2021)
- [VectorLLM: Human-like Extraction of Structured Building Contours vis Multimodal LLMs](https://mlsanity.com/paper/2507.04664/) (2025)
- [A Multitask Deep Learning Model for Parsing Bridge Elements and Segmenting Defect in Bridge Inspection Images](https://mlsanity.com/paper/2209.02190/) (2022)
- [TPE-Net: Track Point Extraction and Association Network for Rail Path Proposal Generation](https://mlsanity.com/paper/2302.05803/) (2023)
- [GlobalBuildingMap -- Unveiling the Mystery of Global Buildings](https://mlsanity.com/paper/2404.13911/) (2024)
- [A Multi-Task Deep Learning Framework for Building Footprint Segmentation](https://mlsanity.com/paper/2104.09375/) (2021)
- [Empirical Study of Multi-Task Hourglass Model for Semantic Segmentation Task](https://mlsanity.com/paper/2105.13531/) (2021)
- [Uncertainty, Edge, and Reverse-Attention Guided Generative Adversarial Network for Automatic Building Detection in Remotely Sensed Images](https://mlsanity.com/paper/2112.05335/) (2021)
- [Body Segmentation Using Multi-task Learning](https://mlsanity.com/paper/2212.06550/) (2022)
- [A Novel Adaptive Deep Network for Building Footprint Segmentation](https://mlsanity.com/paper/2103.00286/) (2021)
- [Multi-task fully convolutional network for tree species mapping in dense forests using small training hyperspectral data](https://mlsanity.com/paper/2106.00799/) (2021)
- [An End-to-End Two-Phase Deep Learning-Based workflow to Segment Man-made Objects Around Reservoirs](https://mlsanity.com/paper/2302.03282/) (2023)
- [Dual skip connections in U-Net, ResUnet and U-Net3+ for remote extraction of buildings](https://mlsanity.com/paper/2303.09064/) (2023)
- [RAPTOR-AI for Disaster OODA Loop: Hierarchical Multimodal RAG with Experience-Driven Agentic Decision-Making](https://mlsanity.com/paper/2602.00030/) (2026)
- [Meta-Learning and Meta-Reinforcement Learning -- Tracing the Path towards DeepMind's Adaptive Agent](https://mlsanity.com/paper/2602.19837/) (2026)
- [Fields of the Planet: Field Boundary Mapping Beyond 10m](https://mlsanity.com/paper/2607.04449/) (2026)

## Abstract (arXiv)

The increased availability of high resolution satellite imagery allows to sense very detailed structures on the surface of our planet. Access to such information opens up new directions in the analysis of remote sensing imagery. However, at the same time this raises a set of new challenges for existing pixel-based prediction methods, such as semantic segmentation approaches. While deep neural networks have achieved significant advances in the semantic segmentation of high resolution images in the past, most of the existing approaches tend to produce predictions with poor boundaries. In this paper, we address the problem of preserving semantic segmentation boundaries in high resolution satellite imagery by introducing a new cascaded multi-task loss. We evaluate our approach on Inria Aerial Image Labeling Dataset which contains large-scale and high resolution images. Our results show that we are able to outperform state-of-the-art methods by 8.3% without any additional post-processing step.