# A Survey on Deep Learning-based Architectures for Semantic Segmentation on 2D images

arXiv: https://arxiv.org/abs/1912.10230 · HTML: https://mlsanity.com/paper/1912.10230/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2019.

## Built on by (most cited first, 29 of 29)

- [A Spacecraft Dataset for Detection, Segmentation and Parts Recognition](https://mlsanity.com/paper/2106.08186/) (2021)
- [Object Detection with Transformers: A Review](https://mlsanity.com/paper/2306.04670/) (2023)
- [Semantic Scene Segmentation for Robotics Applications](https://mlsanity.com/paper/2108.11128/) (2021)
- [Deep Learning for Robust Motion Segmentation with Non-Static Cameras](https://mlsanity.com/paper/2102.10929/) (2021)
- [ClusteringSDF: Self-Organized Neural Implicit Surfaces for 3D Decomposition](https://mlsanity.com/paper/2403.14619/) (2024)
- [Real-Time Semantic Segmentation: A Brief Survey & Comparative Study in Remote Sensing](https://mlsanity.com/paper/2309.06047/) (2023)
- [Surround-View Cameras based Holistic Visual Perception for Automated Driving](https://mlsanity.com/paper/2206.05542/) (2022)
- [Transparency Distortion Robustness for SOTA Image Segmentation Tasks](https://mlsanity.com/paper/2405.12864/) (2024)
- [Cityscape-Adverse: Benchmarking Robustness of Semantic Segmentation with Realistic Scene Modifications via Diffusion-Based Image Editing](https://mlsanity.com/paper/2411.00425/) (2024)
- [GeloVec: Higher Dimensional Geometric Smoothing for Coherent Visual Feature Extraction in Image Segmentation](https://mlsanity.com/paper/2505.01057/) (2025)
- [TriangleNet: Edge Prior Augmented Network for Semantic Segmentation through Cross-Task Consistency](https://mlsanity.com/paper/2210.05152/) (2022)
- [Synthetic-to-real Composite Semantic Segmentation in Additive Manufacturing](https://mlsanity.com/paper/2210.07466/) (2022)
- [Efficient Semantic Segmentation on Edge Devices](https://mlsanity.com/paper/2212.13691/) (2022)
- [Deep Learning for Human Parsing: A Survey](https://mlsanity.com/paper/2301.12416/) (2023)
- [Hadamard Layer to Improve Semantic Segmentation](https://mlsanity.com/paper/2302.10318/) (2023)
- [DenseMP: Unsupervised Dense Pre-training for Few-shot Medical Image Segmentation](https://mlsanity.com/paper/2307.09604/) (2023)
- [TransNet: A Transfer Learning-Based Network for Human Action Recognition](https://mlsanity.com/paper/2309.06951/) (2023)
- [Toward Flare-Free Images: A Survey](https://mlsanity.com/paper/2310.14354/) (2023)
- [Enhancing Rock Image Segmentation in Digital Rock Physics: A Fusion of Generative AI and State-of-the-Art Neural Networks](https://mlsanity.com/paper/2311.06079/) (2023)
- [Influence of Water Droplet Contamination for Transparency Segmentation](https://mlsanity.com/paper/2405.12861/) (2024)
- [Leveraging Segment Anything Model in Identifying Buildings within Refugee Camps (SAM4Refugee) from Satellite Imagery for Humanitarian Operations](https://mlsanity.com/paper/2407.11381/) (2024)
- [Diffusion Based Ambiguous Image Segmentation](https://mlsanity.com/paper/2504.05977/) (2025)
- [Real-Time Wildfire Localization on the NASA Autonomous Modular Sensor using Deep Learning](https://mlsanity.com/paper/2601.14475/) (2026)
- [Scalable 3D Semantic Segmentation for Gun Detection in CT Scans](https://mlsanity.com/paper/2112.03917/) (2021)
- [Skyline variations allow estimating distance to trees on landscape photos using semantic segmentation](https://mlsanity.com/paper/2201.08816/) (2022)
- [FuSS: Fusing Superpixels for Improved Segmentation Consistency](https://mlsanity.com/paper/2206.02714/) (2022)
- [Harmonizing output imbalance for defect segmentation on extremely-imbalanced photovoltaic module cells images](https://mlsanity.com/paper/2211.05295/) (2022)
- [Surpassing state of the art on AMD area estimation from RGB fundus images through careful selection of U-Net architectures and loss functions for class imbalance](https://mlsanity.com/paper/2510.26778/) (2025)
- [AD-SAM: Adapting the Segment Anything Model for Semantic Segmentation in Autonomous Driving](https://mlsanity.com/paper/2510.27047/) (2025)

## Abstract (arXiv)

Semantic segmentation is the pixel-wise labelling of an image. Since the problem is defined at the pixel level, determining image class labels only is not acceptable, but localising them at the original image pixel resolution is necessary. Boosted by the extraordinary ability of convolutional neural networks (CNN) in creating semantic, high level and hierarchical image features; several deep learning-based 2D semantic segmentation approaches have been proposed within the last decade. In this survey, we mainly focus on the recent scientific developments in semantic segmentation, specifically on deep learning-based methods using 2D images. We started with an analysis of the public image sets and leaderboards for 2D semantic segmentation, with an overview of the techniques employed in performance evaluation. In examining the evolution of the field, we chronologically categorised the approaches into three main periods, namely pre-and early deep learning era, the fully convolutional era, and the post-FCN era. We technically analysed the solutions put forward in terms of solving the fundamental problems of the field, such as fine-grained localisation and scale invariance. Before drawing our conclusions, we present a table of methods from all mentioned eras, with a summary of each approach that explains their contribution to the field. We conclude the survey by discussing the current challenges of the field and to what extent they have been solved.