# High-Resolution Semantic Labeling with Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1611.01962 · HTML: https://mlsanity.com/paper/1611.01962/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2016.

## Built on by (most cited first, 11 of 11)

- [Semantic Segmentation of Remote Sensing Images with Sparse Annotations](https://mlsanity.com/paper/2101.03492/) (2021)
- [Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images](https://mlsanity.com/paper/2106.12413/) (2021)
- [Accurate Polygonal Mapping of Buildings in Satellite Imagery](https://mlsanity.com/paper/2208.00609/) (2022)
- [Self-Correlation and Cross-Correlation Learning for Few-Shot Remote Sensing Image Semantic Segmentation](https://mlsanity.com/paper/2309.05840/) (2023)
- [A hierarchical deep learning framework for the consistent classification of land use objects in geospatial databases](https://mlsanity.com/paper/2104.06991/) (2021)
- [A2-FPN for Semantic Segmentation of Fine-Resolution Remotely Sensed Images](https://mlsanity.com/paper/2102.07997/) (2021)
- [Segmentation of VHR EO Images using Unsupervised Learning](https://mlsanity.com/paper/2108.04222/) (2021)
- [Deep Learning and Earth Observation to Support the Sustainable Development Goals](https://mlsanity.com/paper/2112.11367/) (2021)
- [Segment Using Just One Example](https://mlsanity.com/paper/2408.07393/) (2024)
- [FLNet: Flood-Induced Agriculture Damage Assessment using Super Resolution of Satellite Images](https://mlsanity.com/paper/2601.03884/) (2026)
- [Parameter-Efficient Modality-Balanced Symmetric Fusion for Multimodal Remote Sensing Semantic Segmentation](https://mlsanity.com/paper/2603.17705/) (2026)

## Abstract (arXiv)

Convolutional neural networks (CNNs) have received increasing attention over the last few years. They were initially conceived for image categorization, i.e., the problem of assigning a semantic label to an entire input image. In this paper we address the problem of dense semantic labeling, which consists in assigning a semantic label to every pixel in an image. Since this requires a high spatial accuracy to determine where labels are assigned, categorization CNNs, intended to be highly robust to local deformations, are not directly applicable. By adapting categorization networks, many semantic labeling CNNs have been recently proposed. Our first contribution is an in-depth analysis of these architectures. We establish the desired properties of an ideal semantic labeling CNN, and assess how those methods stand with regard to these properties. We observe that even though they provide competitive results, these CNNs often underexploit properties of semantic labeling that could lead to more effective and efficient architectures. Out of these observations, we then derive a CNN framework specifically adapted to the semantic labeling problem. In addition to learning features at different resolutions, it learns how to combine these features. By integrating local and global information in an efficient and flexible manner, it outperforms previous techniques. We evaluate the proposed framework and compare it with state-of-the-art architectures on public benchmarks of high-resolution aerial image labeling.