PSP-MapDiff: Progressively Semantic Prior Information-Guided Online Map Generation From Remote Sensing Imagery via Diffusion Model

Online map generation from remote sensing imagery represents a critical yet challenging task in the geographic information system. With the development of deep learning, automatic generation has become a popular research focus. Current mainstream approaches involve two paradigms: domain mapping utilizing generative models and semantic segmentation based on discriminative models. Although these methods provide an effective tool for automatic online map generation, they still face a fundamental dilemma of suffering from poor inferential creativity or low semantic consistency. Therefore, we argue that lacking proper guidance may result in some semantic distortion in the generated maps. To address this issue, we propose PSP-MapDiff, a novel end-to-end network based on a generative denoising diffusion model incorporating semantic prior information provided by pretrained semantic segmentation networks. Specifically, we design a conditional diffusion framework with a denoising starting point from source domain images, effectively driving the denoising process in a promising direction. Additionally, the proposed Semantic Consistency Gradient (SCG) and Boundary Preservation Gradient (BPG) modules progressively utilize semantic prior information to enhance cross-domain translation accuracy. Geometric consistency loss is further designed to mitigate distortion and improve robustness. Extensive experiments demonstrate that PSP-MapDiff achieves improvements in visual quality (e.g., +27.8% in LPIPS) and accuracy (e.g., +2.3% in ACC) compared to other state-of-the-art methods, indicating the superiority of the proposed model in maintaining the semantic details of the source domain images.