# Learning deep structured active contours end-to-end

arXiv: https://arxiv.org/abs/1803.06329 · HTML: https://mlsanity.com/paper/1803.06329/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2018.

## Built on by (most cited first, 30 of 38)

- [SegDiff: Image Segmentation with Diffusion Probabilistic Models](https://mlsanity.com/paper/2112.00390/) (2021)
- [PolyWorld: Polygonal Building Extraction with Graph Neural Networks in Satellite Images](https://mlsanity.com/paper/2111.15491/) (2021)
- [Contour Proposal Networks for Biomedical Instance Segmentation](https://mlsanity.com/paper/2104.03393/) (2021)
- [Learning to Extract Building Footprints from Off-Nadir Aerial Images](https://mlsanity.com/paper/2204.13637/) (2022)
- [SharpContour: A Contour-based Boundary Refinement Approach for Efficient and Accurate Instance Segmentation](https://mlsanity.com/paper/2203.13312/) (2022)
- [BuildMapper: A Fully Learnable Framework for Vectorized Building Contour Extraction](https://mlsanity.com/paper/2211.03373/) (2022)
- [Instance segmentation of fallen trees in aerial color infrared imagery using active multi-contour evolution with fully convolutional network-based intensity priors](https://mlsanity.com/paper/2105.01998/) (2021)
- [Multi-Class Segmentation from Aerial Views using Recursive Noise Diffusion](https://mlsanity.com/paper/2212.00787/) (2022)
- [Pix2Poly: A Sequence Prediction Method for End-to-end Polygonal Building Footprint Extraction from Remote Sensing Imagery](https://mlsanity.com/paper/2412.07899/) (2024)
- [PolygoNet: Leveraging Simplified Polygonal Representation for Effective Image Classification](https://mlsanity.com/paper/2504.01214/) (2025)
- [IRSAMap:Towards Large-Scale, High-Resolution Land Cover Map Vectorization](https://mlsanity.com/paper/2508.16272/) (2025)
- [A Comprehensive Review of Agricultural Parcel and Boundary Delineation from Remote Sensing Images: Recent Progress and Future Perspectives](https://mlsanity.com/paper/2508.14558/) (2025)
- [Cell Instance Segmentation: The Devil Is in the Boundaries](https://mlsanity.com/paper/2510.09848/) (2025)
- [Deep Active Contours Using Locally Controlled Distance Vector Flow](https://mlsanity.com/paper/2105.08447/) (2021)
- [Split GCN: Effective Interactive Annotation for Segmentation of Disconnected Instance](https://mlsanity.com/paper/2112.06454/) (2021)
- [ContourRender: Detecting Arbitrary Contour Shape For Instance Segmentation In One Pass](https://mlsanity.com/paper/2106.03382/) (2021)
- [Adjusting the Ground Truth Annotations for Connectivity-Based Learning to Delineate](https://mlsanity.com/paper/2112.02781/) (2021)
- [Deep Active Latent Surfaces for Medical Geometries](https://mlsanity.com/paper/2206.10241/) (2022)
- [Building Footprint Extraction with Graph Convolutional Network](https://mlsanity.com/paper/2305.04499/) (2023)
- [GeoFormer: A Multi-Polygon Segmentation Transformer](https://mlsanity.com/paper/2411.16616/) (2024)
- [Image Segmentation via Variational Model Based Tailored UNet: A Deep Variational Framework](https://mlsanity.com/paper/2505.05806/) (2025)
- [Mapping Vulnerable Populations with AI](https://mlsanity.com/paper/2107.14123/) (2021)
- [Neural TMDlayer: Modeling Instantaneous flow of features via SDE Generators](https://mlsanity.com/paper/2108.08891/) (2021)
- [BoundaryNet: An Attentive Deep Network with Fast Marching Distance Maps for Semi-automatic Layout Annotation](https://mlsanity.com/paper/2108.09433/) (2021)
- [Subpixel object segmentation using wavelets and multi resolution analysis](https://mlsanity.com/paper/2110.15233/) (2021)
- [Polyline Generative Navigable Space Segmentation for Autonomous Visual Navigation](https://mlsanity.com/paper/2111.00063/) (2021)
- [End-to-End Segmentation via Patch-wise Polygons Prediction](https://mlsanity.com/paper/2112.02535/) (2021)
- [Deep Learning and Earth Observation to Support the Sustainable Development Goals](https://mlsanity.com/paper/2112.11367/) (2021)
- [An Object Aware Hybrid U-Net for Breast Tumour Annotation](https://mlsanity.com/paper/2202.10691/) (2022)
- [Primitive Graph Learning for Unified Vector Mapping](https://mlsanity.com/paper/2206.13963/) (2022)

## Abstract (arXiv)

The world is covered with millions of buildings, and precisely knowing each instance's position and extents is vital to a multitude of applications. Recently, automated building footprint segmentation models have shown superior detection accuracy thanks to the usage of Convolutional Neural Networks (CNN). However, even the latest evolutions struggle to precisely delineating borders, which often leads to geometric distortions and inadvertent fusion of adjacent building instances. We propose to overcome this issue by exploiting the distinct geometric properties of buildings. To this end, we present Deep Structured Active Contours (DSAC), a novel framework that integrates priors and constraints into the segmentation process, such as continuous boundaries, smooth edges, and sharp corners. To do so, DSAC employs Active Contour Models (ACM), a family of constraint- and prior-based polygonal models. We learn ACM parameterizations per instance using a CNN, and show how to incorporate all components in a structured output model, making DSAC trainable end-to-end. We evaluate DSAC on three challenging building instance segmentation datasets, where it compares favorably against state-of-the-art. Code will be made available.