# End-to-End Trainable Deep Active Contour Models for Automated Image Segmentation: Delineating Buildings in Aerial Imagery

arXiv: https://arxiv.org/abs/2007.11691 · HTML: https://mlsanity.com/paper/2007.11691/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2020.

## Built on by (most cited first, 16 of 16)

- [SegDiff: Image Segmentation with Diffusion Probabilistic Models](https://mlsanity.com/paper/2112.00390/) (2021)
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- [On the in vivo recognition of kidney stones using machine learning](https://mlsanity.com/paper/2201.08865/) (2022)
- [DeFormer: Integrating Transformers with Deformable Models for 3D Shape Abstraction from a Single Image](https://mlsanity.com/paper/2309.12594/) (2023)
- [Multi-Class Segmentation from Aerial Views using Recursive Noise Diffusion](https://mlsanity.com/paper/2212.00787/) (2022)
- [IRSAMap:Towards Large-Scale, High-Resolution Land Cover Map Vectorization](https://mlsanity.com/paper/2508.16272/) (2025)
- [HoliTracer: Holistic Vectorization of Geographic Objects from Large-Size Remote Sensing Imagery](https://mlsanity.com/paper/2507.16251/) (2025)
- [Deep Active Contours Using Locally Controlled Distance Vector Flow](https://mlsanity.com/paper/2105.08447/) (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)
- [End-to-End Segmentation via Patch-wise Polygons Prediction](https://mlsanity.com/paper/2112.02535/) (2021)
- [P-MSDiff: Parallel Multi-Scale Diffusion for Remote Sensing Image Segmentation](https://mlsanity.com/paper/2405.20443/) (2024)
- [Nacala-Roof-Material: Drone Imagery for Roof Detection, Classification, and Segmentation to Support Mosquito-borne Disease Risk Assessment](https://mlsanity.com/paper/2406.04949/) (2024)
- [Deep ContourFlow: Advancing Active Contours with Deep Learning](https://mlsanity.com/paper/2407.10696/) (2024)
- [Mask Proposal Voting Based on Geodesic Framework for Robust Image Segmentation](https://mlsanity.com/paper/2606.14912/) (2026)

## Abstract (arXiv)

The automated segmentation of buildings in remote sensing imagery is a challenging task that requires the accurate delineation of multiple building instances over typically large image areas. Manual methods are often laborious and current deep-learning-based approaches fail to delineate all building instances and do so with adequate accuracy. As a solution, we present Trainable Deep Active Contours (TDACs), an automatic image segmentation framework that intimately unites Convolutional Neural Networks (CNNs) and Active Contour Models (ACMs). The Eulerian energy functional of the ACM component includes per-pixel parameter maps that are predicted by the backbone CNN, which also initializes the ACM. Importantly, both the ACM and CNN components are fully implemented in TensorFlow and the entire TDAC architecture is end-to-end automatically differentiable and backpropagation trainable without user intervention. TDAC yields fast, accurate, and fully automatic simultaneous delineation of arbitrarily many buildings in the image. We validate the model on two publicly available aerial image datasets for building segmentation, and our results demonstrate that TDAC establishes a new state-of-the-art performance.