---
license: cc-by-nc-4.0
tags:
- synthetic
- geospatial
- segmentation
size_categories:
- 10K<n<100K
---

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

**Accepted by WACV 2024**
[[paper](https://openaccess.thecvf.com/content/WACV2024/papers/Song_SyntheWorld_A_Large-Scale_Synthetic_Dataset_for_Land_Cover_Mapping_and_WACV_2024_paper.pdf), [supp](https://openaccess.thecvf.com/content/WACV2024/supplemental/Song_SyntheWorld_A_Large-Scale_WACV_2024_supplemental.pdf)] [[arXiv](https://arxiv.org/abs/2309.01907)]


## Overview

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.

### Key Features

  * **Scale:** 40,000 images with submeter-level pixels.
  * **Annotations:** Fine-grained land cover annotations of eight categories.
  * **Bitemporal Data:** 40,000 pairs of bitemporal image pairs with building change annotations for building change detection tasks.
  * **Validation:** Experiments conducted on multiple benchmark remote sensing datasets to verify effectiveness and investigate advantages.

## Description

This dataset has been designed for **land cover mapping** and **building change detection** tasks.

## File Structure and Content

### 1. 1024.zip (split into multiple parts for hosting)

⚠️ **Important note**

Due to file size limitations on Hugging Face, the original archive
`1024.zip` is split into multiple parts:

- `1024_split.zip`
- `1024_split.z01`
- `1024_split.z02`
- ...

All split files should be downloaded into the same directory.

#### How to reconstruct the archive

After downloading all split files, run the following command:

```bash
zip -s 0 1024_split.zip --out 1024.zip
```

  * **Specs:** Image size 1024x1024 | GSD (Ground Sampling Distance) 0.6-1m
  * **Contents:**
      * `images` & `ss_mask`: Used for the **land cover mapping** task.
      * `images`: Post-event images for building change detection.
      * `small-pre-images`: Images with a **minor** off-nadir angle difference compared to post-event images.
      * `big-pre-images`: Images with a **large** off-nadir angle difference compared to post-event images.
      * `cd_mask`: Ground truth for the **building change detection** task.

### 2\. `512-1.zip`, `512-2.zip`, `512-3.zip`

  * **Specs:** Image size 512x512 | GSD 0.3-0.6m
  * **Contents:**
      * `images` & `ss_mask`: Used for the **land cover mapping** task.
      * `images`: Post-event images for building change detection.
      * `pre-event`: Images for the pre-event phase.
      * `cd-mask`: Ground truth for **building change detection**.

## Land Cover Mapping Class Grep Map

The following mapping corresponds to the pixel values in the segmentation masks:

```python
class_grey = {
    "Bareland": 1,
    "Rangeland": 2,
    "Developed Space": 3,
    "Road": 4,
    "Tree": 5,
    "Water": 6,
    "Agriculture land": 7,
    "Building": 8,
}
```

## Reference

If you use this dataset, please cite the following paper:

```bibtex
@misc{song2023syntheworld,
      title={SyntheWorld: A Large-Scale Synthetic Dataset for Land Cover Mapping and Building Change Detection}, 
      author={Jian Song and Hongruixuan Chen and Naoto Yokoya},
      year={2023},
      eprint={2309.01907},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```