# Segment Any Building

arXiv: https://arxiv.org/abs/2310.01164 · HTML: https://mlsanity.com/paper/2310.01164/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 32 references that are in the map.
- First posted on arXiv 2 Oct 2023.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [Boundary IoU: Improving Object-Centric Image Segmentation Evaluation](https://mlsanity.com/paper/2103.16562/) (2021)
- [FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding](https://mlsanity.com/paper/2012.02951/) (2020)
- [LandCover.ai: Dataset for Automatic Mapping of Buildings, Woodlands, Water and Roads from Aerial Imagery](https://mlsanity.com/paper/2005.02264/) (2020)
- [Mask-FPAN: Semi-Supervised Face Parsing in the Wild With De-Occlusion and UV GAN](https://mlsanity.com/paper/2212.09098/) (2022)
- [Deep Learning Based 3D Point Cloud Regression for Estimating Forest Biomass](https://mlsanity.com/paper/2112.11335/) (2021)
- [LR-CSNet: Low-Rank Deep Unfolding Network for Image Compressive Sensing](https://mlsanity.com/paper/2212.09088/) (2022)
- [BuildSeg: A General Framework for the Segmentation of Buildings](https://mlsanity.com/paper/2301.06190/) (2023)
- [Edge Aware Learning for 3D Point Cloud](https://mlsanity.com/paper/2309.13472/) (2023)

## Built on by (most cited first, 9 of 9)

- [Exploring Diverse Methods in Visual Question Answering](https://mlsanity.com/paper/2404.13565/) (2024)
- [Regional Style and Color Transfer](https://mlsanity.com/paper/2404.13880/) (2024)
- [Human Motion Instruction Tuning](https://mlsanity.com/paper/2411.16805/) (2024)
- [TD3 Based Collision Free Motion Planning for Robot Navigation](https://mlsanity.com/paper/2405.15460/) (2024)
- [Rumor Detection with a novel graph neural network approach](https://mlsanity.com/paper/2403.16206/) (2024)
- [A Multispectral Automated Transfer Technique (MATT) for machine-driven image labeling utilizing the Segment Anything Model (SAM)](https://mlsanity.com/paper/2402.11413/) (2024)
- [Tree Counting by Bridging 3D Point Clouds with Imagery](https://mlsanity.com/paper/2403.01932/) (2024)
- [SAM2-ELNet: Label Enhancement and Automatic Annotation for Remote Sensing Segmentation](https://mlsanity.com/paper/2503.12404/) (2025)
- [MedFLIP: Medical Vision-and-Language Self-supervised Fast Pre-Training with Masked Autoencoder](https://mlsanity.com/paper/2403.04626/) (2024)

## Abstract (arXiv)

The task of identifying and segmenting buildings within remote sensing imagery has perennially stood at the forefront of scholarly investigations. This manuscript accentuates the potency of harnessing diversified datasets in tandem with cutting-edge representation learning paradigms for building segmentation in such images. Through the strategic amalgamation of disparate datasets, we have not only expanded the informational horizon accessible for model training but also manifested unparalleled performance metrics across multiple datasets. Our avant-garde joint training regimen underscores the merit of our approach, bearing significant implications in pivotal domains such as urban infrastructural development, disaster mitigation strategies, and ecological surveillance. Our methodology, predicated upon the fusion of datasets and gleaning insights from pre-trained models, carves a new benchmark in the annals of building segmentation endeavors. The outcomes of this research both fortify the foundations for ensuing scholarly pursuits and presage a horizon replete with innovative applications in the discipline of building segmentation.