Cross Domain Adaptation for Online Map Generation from Optical Remote Sensing Image via Diffusion Model

Online map plays a significant role in modern society. Especially with the rapid expansion of urbanization, obtaining updated maps in a timely manner has become a necessary task. Recently, the rise of generative models has provided an alternative tool for efficient map generation. However, existing methods focus on single-domain translation from remote sensing (RS) images to online maps, which may suffer from poor generalization. For remote sensing images acquired from different regional domains with significant difference, a model trained on one domain can hardly be employed for images in other domains. To address this issue, we propose a novel cross-domain adaptation framework for online map generation from different areas. Our framework comprises two stages. In the first stage, we propose a cross-domain generative adversarial network for source-to-target domain RS images translation, applying the feature-consistency and geometric-consistency constraints. We acquire source-domain RS images with the style of the target domain. In the second stage, we design a conditional diffusion model for online maps generation from translated RS images. To guide the denoising process to a promising direction, we design a content prompt encoder to facilitate the semantic transfer of RS images to the generated online maps. Extensive experiments are conducted in cross-domain datasets. The results demonstrate the superior performance of our model over the state-of-the-art methods on the cross-domain online map generation.