# WakeupUrban: Unsupervised Semantic Segmentation of Mid-20th century Urban Landscapes with Satellite Imagery

arXiv: https://arxiv.org/abs/2506.09476 · HTML: https://mlsanity.com/paper/2506.09476/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 51 references that are in the map.
- First posted on arXiv 11 Jun 2025.

## Builds on (most cited first, 26 of 26)

- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Segment Everything Everywhere All at Once](https://mlsanity.com/paper/2304.06718/) (2023)
- [Invariant Information Clustering for Unsupervised Image Classification and Segmentation](https://mlsanity.com/paper/1807.06653/) (2018)
- [Generalized Decoding for Pixel, Image, and Language](https://mlsanity.com/paper/2212.11270/) (2022)
- [DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images](https://mlsanity.com/paper/1805.06561/) (2018)
- [Unsupervised Semantic Segmentation by Distilling Feature Correspondences](https://mlsanity.com/paper/2203.08414/) (2022)
- [A Simple Framework for Open-Vocabulary Segmentation and Detection](https://mlsanity.com/paper/2303.08131/) (2023)
- [UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagery](https://mlsanity.com/paper/2109.08937/) (2021)
- [Land-Cover Classification with High-Resolution Remote Sensing Images Using Transferable Deep Models](https://mlsanity.com/paper/1807.05713/) (2018)
- [OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping](https://mlsanity.com/paper/2210.10732/) (2022)
- [DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation](https://mlsanity.com/paper/2203.12560/) (2022)
- [Leveraging Hidden Positives for Unsupervised Semantic Segmentation](https://mlsanity.com/paper/2303.15014/) (2023)
- [SAM-Assisted Remote Sensing Imagery Semantic Segmentation with Object and Boundary Constraints](https://mlsanity.com/paper/2312.02464/) (2023)
- [Image Segmentation in Foundation Model Era: A Survey](https://mlsanity.com/paper/2408.12957/) (2024)
- [Hi-UCD: A Large-scale Dataset for Urban Semantic Change Detection in Remote Sensing Imagery](https://mlsanity.com/paper/2011.03247/) (2020)
- [UV-SAM: Adapting Segment Anything Model for Urban Village Identification](https://mlsanity.com/paper/2401.08083/) (2024)
- [EAGLE: Eigen Aggregation Learning for Object-Centric Unsupervised Semantic Segmentation](https://mlsanity.com/paper/2403.01482/) (2024)
- [AerialFormer: Multi-resolution Transformer for Aerial Image Segmentation](https://mlsanity.com/paper/2306.06842/) (2023)
- [FUSU: A Multi-temporal-source Land Use Change Segmentation Dataset for Fine-grained Urban Semantic Understanding](https://mlsanity.com/paper/2405.19055/) (2024)
- [OpenSatMap: A Fine-grained High-resolution Satellite Dataset for Large-scale Map Construction](https://mlsanity.com/paper/2410.23278/) (2024)
- [Boosting Unsupervised Semantic Segmentation with Principal Mask Proposals](https://mlsanity.com/paper/2404.16818/) (2024)
- [Cloud and Cloud Shadow Segmentation for Remote Sensing Imagery via Filtered Jaccard Loss Function and Parametric Augmentation](https://mlsanity.com/paper/2001.08768/) (2020)
- [Generalized Few-Shot Semantic Segmentation in Remote Sensing: Challenge and Benchmark](https://mlsanity.com/paper/2409.11227/) (2024)
- [GBSS:a global building semantic segmentation dataset for large-scale remote sensing building extraction](https://mlsanity.com/paper/2401.01178/) (2024)
- [SEN12-WATER: A New Dataset for Hydrological Applications and its Benchmarking](https://mlsanity.com/paper/2409.17087/) (2024)

## Built on by (most cited first, 1 of 1)

- [Generalized Referring Expression Segmentation on Aerial Photos](https://mlsanity.com/paper/2512.07338/) (2025)

## Abstract (arXiv)

Historical satellite imagery archive, such as Keyhole satellite data, offers rare insights into understanding early urban development and long-term transformation. However, severe quality degradation (textite.g., distortion, misalignment, and spectral scarcity) and the absence of annotations have long hindered its analysis. To bridge this gap and enhance understanding of urban development, we introduce WakeupUrbanBench, an annotated segmentation dataset based on historical satellite imagery with the earliest observation time among all existing remote sensing (RS) datasets, along with a framework for unsupervised segmentation tasks, WakeupUSM. First, WakeupUrbanBench serves as a pioneer, expertly annotated dataset built on mid-20th century RS imagery, involving four key urban classes and spanning 4 cities across 2 continents with nearly 1000 km2 area of diverse urban morphologies, and additionally introducing one present-day city. Second, WakeupUSM is a novel unsupervised semantic segmentation framework for historical RS imagery. It employs a confidence-aware alignment mechanism and focal-confidence loss based on a self-supervised learning architecture, which generates robust pseudo-labels and adaptively prioritizes prediction difficulty and label reliability to improve unsupervised segmentation on noisy historical data without manual supervision. Comprehensive experiments demonstrate WakeupUSM significantly outperforms existing unsupervised segmentation methods both WakeupUrbanBench and public dataset, promising to pave the way for quantitative studies of long-term urban change using modern computer vision. Our benchmark and codes will be released at https://github.com/Tianxiang-Hao/WakeupUrban.