# GBSS:a global building semantic segmentation dataset for large-scale remote sensing building extraction

arXiv: https://arxiv.org/abs/2401.01178 · HTML: https://mlsanity.com/paper/2401.01178/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 2 Jan 2024.

## Builds on (most cited first, 4 of 4)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [SpaceNet: A Remote Sensing Dataset and Challenge Series](https://mlsanity.com/paper/1807.01232/) (2018)

## Built on by (most cited first, 2 of 2)

- [WakeupUrban: Unsupervised Semantic Segmentation of Mid-20th century Urban Landscapes with Satellite Imagery](https://mlsanity.com/paper/2506.09476/) (2025)
- [Transport-Related Surface Detection with Machine Learning: Analyzing Temporal Trends in Madrid and Vienna](https://mlsanity.com/paper/2503.15653/) (2025)

## Abstract (arXiv)

Semantic segmentation techniques for extracting building footprints from high-resolution remote sensing images have been widely used in many fields such as urban planning. However, large-scale building extraction demands higher diversity in training samples. In this paper, we construct a Global Building Semantic Segmentation (GBSS) dataset (The dataset will be released), which comprises 116.9k pairs of samples (about 742k buildings) from six continents. There are significant variations of building samples in terms of size and style, so the dataset can be a more challenging benchmark for evaluating the generalization and robustness of building semantic segmentation models. We validated through quantitative and qualitative comparisons between different datasets, and further confirmed the potential application in the field of transfer learning by conducting experiments on subsets.