# The Multi-Temporal Urban Development SpaceNet Dataset

arXiv: https://arxiv.org/abs/2102.04420 · HTML: https://mlsanity.com/paper/2102.04420/

## Key facts

- Cited by 62 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 42 references that are in the map.
- First posted on arXiv 8 Feb 2021.

## Builds on (most cited first, 22 of 22)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark](https://mlsanity.com/paper/1809.03327/) (2018)
- [MOT20: A benchmark for multi object tracking in crowded scenes](https://mlsanity.com/paper/2003.09003/) (2020)
- [Towards Real-Time Multi-Object Tracking](https://mlsanity.com/paper/1909.12605/) (2019)
- [DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images](https://mlsanity.com/paper/1805.06561/) (2018)
- [Fast Online Object Tracking and Segmentation: A Unifying Approach](https://mlsanity.com/paper/1812.05050/) (2018)
- [MOTS: Multi-Object Tracking and Segmentation](https://mlsanity.com/paper/1902.03604/) (2019)
- [SpaceNet: A Remote Sensing Dataset and Challenge Series](https://mlsanity.com/paper/1807.01232/) (2018)
- [xView: Objects in Context in Overhead Imagery](https://mlsanity.com/paper/1802.07856/) (2018)
- [A Novel Performance Evaluation Methodology for Single-Target Trackers](https://mlsanity.com/paper/1503.01313/) (2015)
- [The 2019 DAVIS Challenge on VOS: Unsupervised Multi-Object Segmentation](https://mlsanity.com/paper/1905.00737/) (2019)
- [The Effects of Super-Resolution on Object Detection Performance in Satellite Imagery](https://mlsanity.com/paper/1812.04098/) (2018)
- [SpaceNet 6: Multi-Sensor All Weather Mapping Dataset](https://mlsanity.com/paper/2004.06500/) (2020)
- [SpaceNet MVOI: a Multi-View Overhead Imagery Dataset](https://mlsanity.com/paper/1903.12239/) (2019)
- [Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking](https://mlsanity.com/paper/1704.02781/) (2017)
- [Satellite Imagery Multiscale Rapid Detection with Windowed Networks](https://mlsanity.com/paper/1809.09978/) (2018)

## Built on by (most cited first, 30 of 62)

- [DiffusionSat: A Generative Foundation Model for Satellite Imagery](https://mlsanity.com/paper/2312.03606/) (2023)
- [DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation](https://mlsanity.com/paper/2203.12560/) (2022)
- [PANGAEA: A Global and Inclusive Benchmark for Geospatial Foundation Models](https://mlsanity.com/paper/2412.04204/) (2024)
- [EarthNets: Empowering AI in Earth Observation](https://mlsanity.com/paper/2210.04936/) (2022)
- [On Calibrating Semantic Segmentation Models: Analyses and An Algorithm](https://mlsanity.com/paper/2212.12053/) (2022)
- [OmniCity: Omnipotent City Understanding with Multi-level and Multi-view Images](https://mlsanity.com/paper/2208.00928/) (2022)
- [GEO-Bench-2: From Performance to Capability, Rethinking Evaluation in Geospatial AI](https://mlsanity.com/paper/2511.15658/) (2025)
- [Multi-Spectral Multi-Image Super-Resolution of Sentinel-2 with Radiometric Consistency Losses and Its Effect on Building Delineation](https://mlsanity.com/paper/2111.03231/) (2021)
- [Detecting Building Changes with Off-Nadir Aerial Images](https://mlsanity.com/paper/2301.10922/) (2023)
- [RSCC: A Large-Scale Remote Sensing Change Caption Dataset for Disaster Events](https://mlsanity.com/paper/2509.01907/) (2025)
- [The Change You Want To Detect: Semantic Change Detection In Earth Observation With Hybrid Data Generation](https://mlsanity.com/paper/2503.15683/) (2025)
- [OpenSatMap: A Fine-grained High-resolution Satellite Dataset for Large-scale Map Construction](https://mlsanity.com/paper/2410.23278/) (2024)
- [The SpaceNet Multi-Temporal Urban Development Challenge](https://mlsanity.com/paper/2102.11958/) (2021)
- [Urban Change Detection Using a Dual-Task Siamese Network and Semi-Supervised Learning](https://mlsanity.com/paper/2204.12202/) (2022)
- [Multitemporal and multispectral data fusion for super-resolution of Sentinel-2 images](https://mlsanity.com/paper/2301.11154/) (2023)
- [Robot Navigation Using Physically Grounded Vision-Language Models in Outdoor Environments](https://mlsanity.com/paper/2409.20445/) (2024)
- [SMARTIES: Spectrum-Aware Multi-Sensor Auto-Encoder for Remote Sensing Images](https://mlsanity.com/paper/2506.19585/) (2025)
- [Applications of Knowledge Distillation in Remote Sensing: A Survey](https://mlsanity.com/paper/2409.12111/) (2024)
- [Temporal Cluster Matching for Change Detection of Structures from Satellite Imagery](https://mlsanity.com/paper/2103.09787/) (2021)
- [UrbanFeel: A Comprehensive Benchmark for Temporal and Perceptual Understanding of City Scenes through Human Perspective](https://mlsanity.com/paper/2509.22228/) (2025)
- [Urban Change Forecasting from Satellite Images](https://mlsanity.com/paper/2204.12875/) (2022)
- [Satellite Image Time Series Semantic Change Detection: Novel Architecture and Analysis of Domain Shift](https://mlsanity.com/paper/2407.07616/) (2024)
- [Can LLM Agents Respond to Disasters? Benchmarking Heterogeneous Geospatial Reasoning in Emergency Operations](https://mlsanity.com/paper/2605.11633/) (2026)
- [Made to Order: Discovering monotonic temporal changes via self-supervised video ordering](https://mlsanity.com/paper/2404.16828/) (2024)
- [Continuous Urban Change Detection from Satellite Image Time Series with Temporal Feature Refinement and Multi-Task Integration](https://mlsanity.com/paper/2406.17458/) (2024)
- [RAMEN: Resolution-Adjustable Multimodal Encoder for Earth Observation](https://mlsanity.com/paper/2512.05025/) (2025)
- [FPCD: An Open Aerial VHR Dataset for Farm Pond Change Detection](https://mlsanity.com/paper/2302.14554/) (2023)
- [Unsupervised CD in satellite image time series by contrastive learning and feature tracking](https://mlsanity.com/paper/2304.11375/) (2023)
- [TEMPO: Global Temporal Building Density and Height Estimation from Satellite Imagery](https://mlsanity.com/paper/2511.12104/) (2025)
- [Sci-Net: Scale Invariant Model for Buildings Segmentation from Aerial Imagery](https://mlsanity.com/paper/2111.06812/) (2021)

## Abstract (arXiv)

Satellite imagery analytics have numerous human development and disaster response applications, particularly when time series methods are involved. For example, quantifying population statistics is fundamental to 67 of the 231 United Nations Sustainable Development Goals Indicators, but the World Bank estimates that over 100 countries currently lack effective Civil Registration systems. To help address this deficit and develop novel computer vision methods for time series data, we present the Multi-Temporal Urban Development SpaceNet (MUDS, also known as SpaceNet 7) dataset. This open source dataset consists of medium resolution (4.0m) satellite imagery mosaics, which includes 24 images (one per month) covering >100 unique geographies, and comprises >40,000 km2 of imagery and exhaustive polygon labels of building footprints therein, totaling over 11M individual annotations. Each building is assigned a unique identifier (i.e. address), which permits tracking of individual objects over time. Label fidelity exceeds image resolution; this "omniscient labeling" is a unique feature of the dataset, and enables surprisingly precise algorithmic models to be crafted. We demonstrate methods to track building footprint construction (or demolition) over time, thereby directly assessing urbanization. Performance is measured with the newly developed SpaceNet Change and Object Tracking (SCOT) metric, which quantifies both object tracking as well as change detection. We demonstrate that despite the moderate resolution of the data, we are able to track individual building identifiers over time. This task has broad implications for disaster preparedness, the environment, infrastructure development, and epidemic prevention.