mlsanityData to 7 Oct 2026

SyntheWorld: A Large-Scale Synthetic Dataset for Land Cover Mapping and Building Change Detection

arXiv 2309.01907 ↗First posted 5 Sep 2023cs.CV cs.AI cs.HCcited by 19

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Abstract (arXiv)

Synthetic datasets, recognized for their cost effectiveness, play a pivotal role in advancing computer vision tasks and techniques. However, when it comes to remote sensing image processing, the creation of synthetic datasets becomes challenging due to the demand for larger-scale and more diverse 3D models. This complexity is compounded by the difficulties associated with real remote sensing datasets, including limited data acquisition and high annotation costs, which amplifies the need for high-quality synthetic alternatives. To address this, we present SyntheWorld, a synthetic dataset unparalleled in quality, diversity, and scale. It includes 40,000 images with submeter-level pixels and fine-grained land cover annotations of eight categories, and it also provides 40,000 pairs of bitemporal image pairs with building change annotations for building change detection task. We conduct experiments on multiple benchmark remote sensing datasets to verify the effectiveness of SyntheWorld and to investigate the conditions under which our synthetic data yield advantages. We will release SyntheWorld to facilitate remote sensing image processing research.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 26 of its 52 references that are in the map. First posted on arXiv 5 Sep 2023.

Builds on

26 of 52 references.

References · 26

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  4. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  5. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  6. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  7. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  8. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,427
  9. 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,131
  10. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,210
  11. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,923
  12. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
14 more
  1. 2015A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimationcited by 878
  2. 2016Playing for Data: Ground Truth from Computer Gamescited by 816
  3. 2018Fully Convolutional Siamese Networks for Change Detectioncited by 305
  4. 2018DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Imagescited by 276
  5. 2021LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentationcited by 259
  6. 2022A Transformer-Based Siamese Network for Change Detectioncited by 243
  7. 2020Geometric Dataset Distances via Optimal Transportcited by 157
  8. 2018Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsingcited by 105
  9. 2019Building Change Detection for Remote Sensing Images Using a Dual Task Constrained Deep Siamese Convolutional Network Modelcited by 84
  10. 2022OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mappingcited by 80
  11. 2020RarePlanes: Synthetic Data Takes Flightcited by 48
  12. 2020Semantic Change Detection with Asymmetric Siamese Networkscited by 31
  13. 2020The Synthinel-1 dataset: a collection of high resolution synthetic overhead imagery for building segmentationcited by 19
  14. 2023Detecting Building Changes with Off-Nadir Aerial Imagescited by 16

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 7; 2026: 7.

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  1. 2024ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Modelcited by 118
  2. 2024Changen2: Multi-Temporal Remote Sensing Generative Change Foundation Modelcited by 37
  3. 2025The Change You Want To Detect: Semantic Change Detection In Earth Observation With Hybrid Data Generationcited by 12
  4. 2024SynRS3D: A Synthetic Dataset for Global 3D Semantic Understanding from Monocular Remote Sensing Imagerycited by 9
  5. 2024ChangeAnywhere: Sample Generation for Remote Sensing Change Detection via Semantic Latent Diffusion Modelcited by 8
  6. 2025Referring Change Detection in Remote Sensing Imagerycited by 6
  7. 2025Enhancing Monocular Height Estimation via Sparse LiDAR-Guided Correctioncited by 4
  8. 2025Generalizable Multispectral Land Cover Classification via Frequency-Aware Mixture of Low-Rank Token Expertscited by 3
  9. 2024Learning Zero-Shot Material States Segmentation, by Implanting Natural Image Patterns in Synthetic Datacited by 2
  10. 2025Synthetic Data Matters: Re-training with Geo-typical Synthetic Labels for Building Detectioncited by 2
  11. 2026TerraDiT-Omega: Unified Spatial Control for Satellite Image Synthesis with Any Geospatial Primitivecited by 2
  12. 2025FOD-S2R: A FOD Dataset for Sim2Real Transfer Learning based Object Detectioncited by 1
7 more
  1. 2026Remote Sensing Change Detection via Weak Temporal Supervisioncited by 1
  2. 2025Operational Change Detection for Geographical Information: Overview and Challengescited by 0
  3. 2026Make Some Noise: Unsupervised Remote Sensing Change Detection Using Latent Space Perturbationscited by 0
  4. 2026ChangeFlow -- Latent Rectified Flow for Change Detection in Remote Sensingcited by 0
  5. 2026Learning Semantic-Robust Change Detection via Semantic-Invariant Self-Distillationcited by 0
  6. 2026Frequency and Edge-Guided Segment Anything Model for Remote Sensing Image Semantic Segmentationcited by 0
  7. 2026Real-World Knowledge-Guided Change Data Synthesis for Remote Sensingcited by 0