# Feature-Augmented Deep Networks for Multiscale Building Segmentation in High-Resolution UAV and Satellite Imagery

arXiv: https://arxiv.org/abs/2505.05321 · HTML: https://mlsanity.com/paper/2505.05321/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 93 references that are in the map.
- First posted on arXiv 8 May 2025.

## Builds on (most cited first, 17 of 17)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [Cyclical Learning Rates for Training Neural Networks](https://mlsanity.com/paper/1506.01186/) (2015)
- [Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations](https://mlsanity.com/paper/1707.03237/) (2017)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [Learning Deconvolution Network for Semantic Segmentation](https://mlsanity.com/paper/1505.04366/) (2015)
- [SpaceNet: A Remote Sensing Dataset and Challenge Series](https://mlsanity.com/paper/1807.01232/) (2018)
- [Deep learning in remote sensing: a review](https://mlsanity.com/paper/1710.03959/) (2017)
- [A Survey on Object Detection in Optical Remote Sensing Images](https://mlsanity.com/paper/1603.06201/) (2016)
- [Fully Convolutional Networks for Dense Semantic Labelling of High-Resolution Aerial Imagery](https://mlsanity.com/paper/1606.02585/) (2016)
- [Semantic Labeling in Very High Resolution Images via a Self-Cascaded Convolutional Neural Network](https://mlsanity.com/paper/1807.11236/) (2018)
- [Rapid building detection using machine learning](https://mlsanity.com/paper/1603.04392/) (2016)
- [A Multi-Stage Multi-Task Neural Network for Aerial Scene Interpretation and Geolocalization](https://mlsanity.com/paper/1804.01322/) (2018)

## Abstract (arXiv)

Accurate building segmentation from high-resolution RGB imagery remains challenging due to spectral similarity with non-building features, shadows, and irregular building geometries. In this study, we present a comprehensive deep learning framework for multiscale building segmentation using RGB aerial and satellite imagery with spatial resolutions ranging from 0.4m to 2.7m. We curate a diverse, multi-sensor dataset and introduce feature-augmented inputs by deriving secondary representations including Principal Component Analysis (PCA), Visible Difference Vegetation Index (VDVI), Morphological Building Index (MBI), and Sobel edge filters from RGB channels. These features guide a Res-U-Net architecture in learning complex spatial patterns more effectively. We also propose training policies incorporating layer freezing, cyclical learning rates, and SuperConvergence to reduce training time and resource usage. Evaluated on a held-out WorldView-3 image, our model achieves an overall accuracy of 96.5%, an F1-score of 0.86, and an Intersection over Union (IoU) of 0.80, outperforming existing RGB-based benchmarks. This study demonstrates the effectiveness of combining multi-resolution imagery, feature augmentation, and optimized training strategies for robust building segmentation in remote sensing applications.