# S2Looking: A Satellite Side-Looking Dataset for Building Change Detection

arXiv: https://arxiv.org/abs/2107.09244 · HTML: https://mlsanity.com/paper/2107.09244/

## Key facts

- Cited by 77 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 69 references that are in the map.
- First posted on arXiv 20 Jul 2021.

## Builds on (most cited first, 19 of 19)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Fully Convolutional Siamese Networks for Change Detection](https://mlsanity.com/paper/1810.08462/) (2018)
- [Remote Sensing Image Change Detection with Transformers](https://mlsanity.com/paper/2103.00208/) (2021)
- [DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images](https://mlsanity.com/paper/1805.06561/) (2018)
- [FineGym: A Hierarchical Video Dataset for Fine-grained Action Understanding](https://mlsanity.com/paper/2004.06704/) (2020)
- [WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection](https://mlsanity.com/paper/2101.01456/) (2021)
- [FAIR1M: A Benchmark Dataset for Fine-grained Object Recognition in High-Resolution Remote Sensing Imagery](https://mlsanity.com/paper/2103.05569/) (2021)
- [xBD: A Dataset for Assessing Building Damage from Satellite Imagery](https://mlsanity.com/paper/1911.09296/) (2019)
- [Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks](https://mlsanity.com/paper/1810.08468/) (2018)
- [Deep Learning vs. Traditional Computer Vision](https://mlsanity.com/paper/1910.13796/) (2019)
- [Building Change Detection for Remote Sensing Images Using a Dual Task Constrained Deep Siamese Convolutional Network Model](https://mlsanity.com/paper/1909.07726/) (2019)
- [Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges](https://mlsanity.com/paper/2009.03137/) (2020)
- [DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite images](https://mlsanity.com/paper/2003.03608/) (2020)
- [ISIA Food-500: A Dataset for Large-Scale Food Recognition via Stacked Global-Local Attention Network](https://mlsanity.com/paper/2008.05655/) (2020)
- [SpaceNet MVOI: a Multi-View Overhead Imagery Dataset](https://mlsanity.com/paper/1903.12239/) (2019)
- [A Novel Recurrent Encoder-Decoder Structure for Large-Scale Multi-view Stereo Reconstruction from An Open Aerial Dataset](https://mlsanity.com/paper/2003.00637/) (2020)
- [Vehicle Re-identification in Aerial Imagery: Dataset and Approach](https://mlsanity.com/paper/1904.01400/) (2019)
- [MOR-UAV: A Benchmark Dataset and Baselines for Moving Object Recognition in UAV Videos](https://mlsanity.com/paper/2008.01699/) (2020)
- [Benchmark Dataset for Automatic Damaged Building Detection from Post-Hurricane Remotely Sensed Imagery](https://mlsanity.com/paper/1812.05581/) (2018)

## Built on by (most cited first, 30 of 77)

- [Changer: Feature Interaction is What You Need for Change Detection](https://mlsanity.com/paper/2209.08290/) (2022)
- [TorchGeo: Deep Learning With Geospatial Data](https://mlsanity.com/paper/2111.08872/) (2021)
- [TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Data](https://mlsanity.com/paper/2410.06234/) (2024)
- [Transformers in Remote Sensing: A Survey](https://mlsanity.com/paper/2209.01206/) (2022)
- [Adapting Segment Anything Model for Change Detection in HR Remote Sensing Images](https://mlsanity.com/paper/2309.01429/) (2023)
- [A New Learning Paradigm for Foundation Model-based Remote Sensing Change Detection](https://mlsanity.com/paper/2312.01163/) (2023)
- [LSKNet: A Foundation Lightweight Backbone for Remote Sensing](https://mlsanity.com/paper/2403.11735/) (2024)
- [EarthNets: Empowering AI in Earth Observation](https://mlsanity.com/paper/2210.04936/) (2022)
- [Changen2: Multi-Temporal Remote Sensing Generative Change Foundation Model](https://mlsanity.com/paper/2406.17998/) (2024)
- [Segment Any Change](https://mlsanity.com/paper/2402.01188/) (2024)
- [Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images](https://mlsanity.com/paper/2204.08454/) (2022)
- [Change Guiding Network: Incorporating Change Prior to Guide Change Detection in Remote Sensing Imagery](https://mlsanity.com/paper/2404.09179/) (2024)
- [Scalable Multi-Temporal Remote Sensing Change Data Generation via Simulating Stochastic Change Process](https://mlsanity.com/paper/2309.17031/) (2023)
- [DynamicEarth: How Far are We from Open-Vocabulary Change Detection?](https://mlsanity.com/paper/2501.12931/) (2025)
- [Self-Pair: Synthesizing Changes from Single Source for Object Change Detection in Remote Sensing Imagery](https://mlsanity.com/paper/2212.10236/) (2022)
- [Vision-Language Modeling Meets Remote Sensing: Models, Datasets and Perspectives](https://mlsanity.com/paper/2505.14361/) (2025)
- [Detecting Building Changes with Off-Nadir Aerial Images](https://mlsanity.com/paper/2301.10922/) (2023)
- [SegEarth-OV3: Exploring SAM 3 for Open-Vocabulary Semantic Segmentation in Remote Sensing Images](https://mlsanity.com/paper/2512.08730/) (2025)
- [RSCC: A Large-Scale Remote Sensing Change Caption Dataset for Disaster Events](https://mlsanity.com/paper/2509.01907/) (2025)
- [A Change Detection Reality Check](https://mlsanity.com/paper/2402.06994/) (2024)
- [MapFormer: Boosting Change Detection by Using Pre-change Information](https://mlsanity.com/paper/2303.17859/) (2023)
- [RSBuilding: Towards General Remote Sensing Image Building Extraction and Change Detection with Foundation Model](https://mlsanity.com/paper/2403.07564/) (2024)
- [The Change You Want To Detect: Semantic Change Detection In Earth Observation With Hybrid Data Generation](https://mlsanity.com/paper/2503.15683/) (2025)
- [Open-CD: A Comprehensive Toolbox for Change Detection](https://mlsanity.com/paper/2407.15317/) (2024)
- [Inferring 3D change detection from bitemporal optical images](https://mlsanity.com/paper/2205.15903/) (2022)
- [Geospatial Foundation Models to Enable Progress on Sustainable Development Goals](https://mlsanity.com/paper/2505.24528/) (2025)
- [Robot Navigation Using Physically Grounded Vision-Language Models in Outdoor Environments](https://mlsanity.com/paper/2409.20445/) (2024)
- [A Survey of Sample-Efficient Deep Learning for Change Detection in Remote Sensing: Tasks, Strategies, and Challenges](https://mlsanity.com/paper/2502.02835/) (2025)
- [Visual Instruction Pretraining for Domain-Specific Foundation Models](https://mlsanity.com/paper/2509.17562/) (2025)
- [Single-Temporal Supervised Learning for Universal Remote Sensing Change Detection](https://mlsanity.com/paper/2406.15694/) (2024)

## Abstract (arXiv)

Building-change detection underpins many important applications, especially in the military and crisis-management domains. Recent methods used for change detection have shifted towards deep learning, which depends on the quality of its training data. The assembly of large-scale annotated satellite imagery datasets is therefore essential for global building-change surveillance. Existing datasets almost exclusively offer near-nadir viewing angles. This limits the range of changes that can be detected. By offering larger observation ranges, the scroll imaging mode of optical satellites presents an opportunity to overcome this restriction. This paper therefore introduces S2Looking, a building-change-detection dataset that contains large-scale side-looking satellite images captured at various off-nadir angles. The dataset consists of 5000 bitemporal image pairs of rural areas and more than 65,920 annotated instances of changes throughout the world. The dataset can be used to train deep-learning-based change-detection algorithms. It expands upon existing datasets by providing (1) larger viewing angles; (2) large illumination variances; and (3) the added complexity of rural images. To facilitate {the} use of the dataset, a benchmark task has been established, and preliminary tests suggest that deep-learning algorithms find the dataset significantly more challenging than the closest-competing near-nadir dataset, LEVIR-CD+. S2Looking may therefore promote important advances in existing building-change-detection algorithms. The dataset is available at https://github.com/S2Looking/.