SyntheWorld: A Large-Scale Synthetic Dataset for Land Cover Mapping and Building Change Detection
This addresses data scarcity and high annotation costs in remote sensing image processing, though it is incremental as it builds on existing synthetic data approaches.
The authors tackled the challenge of limited and costly real remote sensing datasets by creating SyntheWorld, a large-scale synthetic dataset with 40,000 images and fine-grained annotations, which showed effectiveness in experiments on benchmark datasets.
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.