# The Synthinel-1 dataset: a collection of high resolution synthetic overhead imagery for building segmentation

arXiv: https://arxiv.org/abs/2001.05130 · HTML: https://mlsanity.com/paper/2001.05130/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2020.

## Built on by (most cited first, 19 of 19)

- [EarthNets: Empowering AI in Earth Observation](https://mlsanity.com/paper/2210.04936/) (2022)
- [Leveraging Synthetic Data in Object Detection on Unmanned Aerial Vehicles](https://mlsanity.com/paper/2112.12252/) (2021)
- [SyntheWorld: A Large-Scale Synthetic Dataset for Land Cover Mapping and Building Change Detection](https://mlsanity.com/paper/2309.01907/) (2023)
- [Learning to Extract Building Footprints from Off-Nadir Aerial Images](https://mlsanity.com/paper/2204.13637/) (2022)
- [SynRS3D: A Synthetic Dataset for Global 3D Semantic Understanding from Monocular Remote Sensing Imagery](https://mlsanity.com/paper/2406.18151/) (2024)
- [Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery](https://mlsanity.com/paper/2104.14032/) (2021)
- [SyntEO: Synthetic Data Set Generation for Earth Observation and Deep Learning -- Demonstrated for Offshore Wind Farm Detection](https://mlsanity.com/paper/2112.02829/) (2021)
- [Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis](https://mlsanity.com/paper/2202.12939/) (2022)
- [SIMPL: Generating Synthetic Overhead Imagery to Address Zero-shot and Few-Shot Detection Problems](https://mlsanity.com/paper/2106.15681/) (2021)
- [Enhancing Monocular Height Estimation via Sparse LiDAR-Guided Correction](https://mlsanity.com/paper/2505.06905/) (2025)
- [GridTracer: Automatic Mapping of Power Grids using Deep Learning and Overhead Imagery](https://mlsanity.com/paper/2101.06390/) (2021)
- [Centerpoints Are All You Need in Overhead Imagery](https://mlsanity.com/paper/2210.01857/) (2022)
- [Generating Synthetic Satellite Imagery for Rare Objects: An Empirical Comparison of Models and Metrics](https://mlsanity.com/paper/2409.01138/) (2024)
- [Synthetic Data Matters: Re-training with Geo-typical Synthetic Labels for Building Detection](https://mlsanity.com/paper/2507.16657/) (2025)
- [Meta-simulation for the Automated Design of Synthetic Overhead Imagery](https://mlsanity.com/paper/2209.08685/) (2022)
- [Mastering Spatial Graph Prediction of Road Networks](https://mlsanity.com/paper/2210.00828/) (2022)
- [Transformers For Recognition In Overhead Imagery: A Reality Check](https://mlsanity.com/paper/2210.12599/) (2022)
- [Using Game Engines and Machine Learning to Create Synthetic Satellite Imagery for a Tabletop Verification Exercise](https://mlsanity.com/paper/2404.11461/) (2024)
- [MultiScale Probability Map guided Index Pooling with Attention-based learning for Road and Building Segmentation](https://mlsanity.com/paper/2302.09411/) (2023)

## Abstract (arXiv)

Recently deep learning - namely convolutional neural networks (CNNs) - have yielded impressive performance for the task of building segmentation on large overhead (e.g., satellite) imagery benchmarks. However, these benchmark datasets only capture a small fraction of the variability present in real-world overhead imagery, limiting the ability to properly train, or evaluate, models for real-world application. Unfortunately, developing a dataset that captures even a small fraction of real-world variability is typically infeasible due to the cost of imagery, and manual pixel-wise labeling of the imagery. In this work we develop an approach to rapidly and cheaply generate large and diverse virtual environments from which we can capture synthetic overhead imagery for training segmentation CNNs. Using this approach, generate and publicly-release a collection of synthetic overhead imagery - termed Synthinel-1 with full pixel-wise building labels. We use several benchmark dataset to demonstrate that Synthinel-1 is consistently beneficial when used to augment real-world training imagery, especially when CNNs are tested on novel geographic locations or conditions.