# UV-SAM: Adapting Segment Anything Model for Urban Village Identification

arXiv: https://arxiv.org/abs/2401.08083 · HTML: https://mlsanity.com/paper/2401.08083/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 31 references that are in the map.
- First posted on arXiv 16 Jan 2024.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [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)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Track Anything: Segment Anything Meets Videos](https://mlsanity.com/paper/2304.11968/) (2023)
- [Inpaint Anything: Segment Anything Meets Image Inpainting](https://mlsanity.com/paper/2304.06790/) (2023)
- [RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model](https://mlsanity.com/paper/2306.16269/) (2023)
- [A Contextual Master-Slave Framework on Urban Region Graph for Urban Village Detection](https://mlsanity.com/paper/2211.14633/) (2022)

## Built on by (most cited first, 29 of 29)

- [TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis](https://mlsanity.com/paper/2506.20380/) (2025)
- [Diving into Underwater: Segment Anything Model Guided Underwater Salient Instance Segmentation and A Large-scale Dataset](https://mlsanity.com/paper/2406.06039/) (2024)
- [FlexiMo: A Flexible Remote Sensing Foundation Model](https://mlsanity.com/paper/2503.23844/) (2025)
- [Segment Anything with Multiple Modalities](https://mlsanity.com/paper/2408.09085/) (2024)
- [Foundation Models in Remote Sensing: Evolving from Unimodality to Multimodality](https://mlsanity.com/paper/2603.00988/) (2026)
- [De-LightSAM: Modality-Decoupled Lightweight SAM for Generalizable Medical Segmentation](https://mlsanity.com/paper/2407.14153/) (2024)
- [UrbanCross: Enhancing Satellite Image-Text Retrieval with Cross-Domain Adaptation](https://mlsanity.com/paper/2404.14241/) (2024)
- [UrbanSAM: Learning Invariance-Inspired Adapters for Segment Anything Models in Urban Construction](https://mlsanity.com/paper/2502.15199/) (2025)
- [Dynamic Dictionary Learning for Remote Sensing Image Segmentation](https://mlsanity.com/paper/2503.06683/) (2025)
- [CityLens: Evaluating Large Vision-Language Models for Urban Socioeconomic Sensing](https://mlsanity.com/paper/2506.00530/) (2025)
- [Adapting Segment Anything Model to Multi-modal Salient Object Detection with Semantic Feature Fusion Guidance](https://mlsanity.com/paper/2408.15063/) (2024)
- [Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery](https://mlsanity.com/paper/2506.04970/) (2025)
- [Generalizable Slum Detection from Satellite Imagery with Mixture-of-Experts](https://mlsanity.com/paper/2511.10300/) (2025)
- [SAMCT: Segment Any CT Allowing Labor-Free Task-Indicator Prompts](https://mlsanity.com/paper/2403.13258/) (2024)
- [Deep Instruction Tuning for Segment Anything Model](https://mlsanity.com/paper/2404.00650/) (2024)
- [NuSegDG: Integration of Heterogeneous Space and Gaussian Kernel for Domain-Generalized Nuclei Segmentation](https://mlsanity.com/paper/2408.11787/) (2024)
- [From Pixels to Progress: Generating Road Network from Satellite Imagery for Socioeconomic Insights in Impoverished Areas](https://mlsanity.com/paper/2406.11282/) (2024)
- [KAN-SAM: Kolmogorov-Arnold Network Guided Segment Anything Model for RGB-T Salient Object Detection](https://mlsanity.com/paper/2504.05878/) (2025)
- [Towards Collective Intelligence: Uncertainty-aware SAM Adaptation for Ambiguous Medical Image Segmentation](https://mlsanity.com/paper/2403.10931/) (2024)
- [UV-Mamba: A DCN-Enhanced State Space Model for Urban Village Boundary Identification in High-Resolution Remote Sensing Images](https://mlsanity.com/paper/2409.03431/) (2024)
- [Mapping Urban Villages in China: Progress and Challenges](https://mlsanity.com/paper/2503.14195/) (2025)
- [Satellites Reveal Mobility: A Commuting Origin-destination Flow Generator for Global Cities](https://mlsanity.com/paper/2505.15870/) (2025)
- [Expose Camouflage in the Water: Underwater Camouflaged Instance Segmentation and Dataset](https://mlsanity.com/paper/2510.17585/) (2025)
- [Pro2SAM: Mask Prompt to SAM with Grid Points for Weakly Supervised Object Localization](https://mlsanity.com/paper/2505.04905/) (2025)
- [WakeupUrban: Unsupervised Semantic Segmentation of Mid-20th century Urban Landscapes with Satellite Imagery](https://mlsanity.com/paper/2506.09476/) (2025)
- [TNet: Terrace Convolutional Decoder Network for Remote Sensing Image Semantic Segmentation](https://mlsanity.com/paper/2508.04061/) (2025)
- [LithoSeg: A Coarse-to-Fine Framework for High-Precision Lithography Segmentation](https://mlsanity.com/paper/2511.12005/) (2025)
- [SPDA-SAM: A Self-prompted Depth-Aware Segment Anything Model for Instance Segmentation](https://mlsanity.com/paper/2602.06335/) (2026)
- [Building and Road Recognition in Dense Urban Informal Settlements: A Dataset and Benchmark](https://mlsanity.com/paper/2605.29856/) (2026)

## Abstract (arXiv)

Urban villages, defined as informal residential areas in or around urban centers, are characterized by inadequate infrastructures and poor living conditions, closely related to the Sustainable Development Goals (SDGs) on poverty, adequate housing, and sustainable cities. Traditionally, governments heavily depend on field survey methods to monitor the urban villages, which however are time-consuming, labor-intensive, and possibly delayed. Thanks to widely available and timely updated satellite images, recent studies develop computer vision techniques to detect urban villages efficiently. However, existing studies either focus on simple urban village image classification or fail to provide accurate boundary information. To accurately identify urban village boundaries from satellite images, we harness the power of the vision foundation model and adapt the Segment Anything Model (SAM) to urban village segmentation, named UV-SAM. Specifically, UV-SAM first leverages a small-sized semantic segmentation model to produce mixed prompts for urban villages, including mask, bounding box, and image representations, which are then fed into SAM for fine-grained boundary identification. Extensive experimental results on two datasets in China demonstrate that UV-SAM outperforms existing baselines, and identification results over multiple years show that both the number and area of urban villages are decreasing over time, providing deeper insights into the development trends of urban villages and sheds light on the vision foundation models for sustainable cities. The dataset and codes of this study are available at https://github.com/tsinghua-fib-lab/UV-SAM.