# Fully Convolutional Networks for Dense Semantic Labelling of High-Resolution Aerial Imagery

arXiv: https://arxiv.org/abs/1606.02585 · HTML: https://mlsanity.com/paper/1606.02585/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2016.

## Built on by (most cited first, 30 of 36)

- [SkySense: A Multi-Modal Remote Sensing Foundation Model Towards Universal Interpretation for Earth Observation Imagery](https://mlsanity.com/paper/2312.10115/) (2023)
- [UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagery](https://mlsanity.com/paper/2109.08937/) (2021)
- [A Novel Transformer Based Semantic Segmentation Scheme for Fine-Resolution Remote Sensing Images](https://mlsanity.com/paper/2104.12137/) (2021)
- [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)
- [SkySense V2: A Unified Foundation Model for Multi-modal Remote Sensing](https://mlsanity.com/paper/2507.13812/) (2025)
- [ABCNet: Attentive Bilateral Contextual Network for Efficient Semantic Segmentation of Fine-Resolution Remote Sensing Images](https://mlsanity.com/paper/2102.02531/) (2021)
- [Representation Learning for Remote Sensing: An Unsupervised Sensor Fusion Approach](https://mlsanity.com/paper/2108.05094/) (2021)
- [RS-vHeat: Heat Conduction Guided Efficient Remote Sensing Foundation Model](https://mlsanity.com/paper/2411.17984/) (2024)
- [RemoteAgent: Bridging Vague Human Intents and Earth Observation with RL-based Agentic MLLMs](https://mlsanity.com/paper/2604.07765/) (2026)
- [An Attention-Fused Network for Semantic Segmentation of Very-High-Resolution Remote Sensing Imagery](https://mlsanity.com/paper/2105.04132/) (2021)
- [ChessMix: Spatial Context Data Augmentation for Remote Sensing Semantic Segmentation](https://mlsanity.com/paper/2108.11535/) (2021)
- [Scale-aware Neural Network for Semantic Segmentation of Multi-resolution Remote Sensing Images](https://mlsanity.com/paper/2103.07935/) (2021)
- [IRSAMap:Towards Large-Scale, High-Resolution Land Cover Map Vectorization](https://mlsanity.com/paper/2508.16272/) (2025)
- [Stochastic Coherence Over Attention Trajectory For Continuous Learning In Video Streams](https://mlsanity.com/paper/2204.12193/) (2022)
- [A Genealogy of Foundation Models in Remote Sensing](https://mlsanity.com/paper/2504.17177/) (2025)
- [Towards Efficient Benchmarking of Foundation Models in Remote Sensing: A Capabilities Encoding Approach](https://mlsanity.com/paper/2505.03299/) (2025)
- [Survey of Multimodal Geospatial Foundation Models: Techniques, Applications, and Challenges](https://mlsanity.com/paper/2510.22964/) (2025)
- [Pattern Integration and Enhancement Vision Transformer for Self-Supervised Learning in Remote Sensing](https://mlsanity.com/paper/2411.06091/) (2024)
- [Multi-modal land cover mapping of remote sensing images using pyramid attention and gated fusion networks](https://mlsanity.com/paper/2111.03845/) (2021)
- [Semantic Labeling of High Resolution Images Using EfficientUNets and Transformers](https://mlsanity.com/paper/2206.09731/) (2022)
- [Exploration of Convolutional Neural Network Architectures for Large Region Map Automation](https://mlsanity.com/paper/2211.03854/) (2022)
- [A Contrastive Learning Foundation Model Based on Perfectly Aligned Sample Pairs for Remote Sensing Images](https://mlsanity.com/paper/2505.19447/) (2025)
- [Learning with less: label-efficient land cover classification at very high spatial resolution using self-supervised deep learning](https://mlsanity.com/paper/2511.03004/) (2025)
- [A2-FPN for Semantic Segmentation of Fine-Resolution Remotely Sensed Images](https://mlsanity.com/paper/2102.07997/) (2021)
- [Rapid Detection of Aircrafts in Satellite Imagery based on Deep Neural Networks](https://mlsanity.com/paper/2104.11677/) (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)
- [Global and Local Features through Gaussian Mixture Models on Image Semantic Segmentation](https://mlsanity.com/paper/2207.09162/) (2022)
- [Habitat classification from satellite observations with sparse annotations](https://mlsanity.com/paper/2209.12995/) (2022)

## Abstract (arXiv)

The trend towards higher resolution remote sensing imagery facilitates a transition from land-use classification to object-level scene understanding. Rather than relying purely on spectral content, appearance-based image features come into play. In this work, deep convolutional neural networks (CNNs) are applied to semantic labelling of high-resolution remote sensing data. Recent advances in fully convolutional networks (FCNs) are adapted to overhead data and shown to be as effective as in other domains. A full-resolution labelling is inferred using a deep FCN with no downsampling, obviating the need for deconvolution or interpolation. To make better use of image features, a pre-trained CNN is fine-tuned on remote sensing data in a hybrid network context, resulting in superior results compared to a network trained from scratch. The proposed approach is applied to the problem of labelling high-resolution aerial imagery, where fine boundary detail is important. The dense labelling yields state-of-the-art accuracy for the ISPRS Vaihingen and Potsdam benchmark data sets.