  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.

Computer Vision and Pattern Recognition;Artificial Intelligence;Human-Computer Interaction