DiffusionUavLoc: Visually Prompted Diffusion for Cross-View UAV Localization

TL;DR

DiffusionUavLoc introduces a novel diffusion-based, image-prompted framework for UAV localization that effectively bridges view gaps without relying on text prompts or extensive annotations, demonstrating strong performance on benchmark datasets.

Contribution

The paper presents a diffusion-centric, text-free cross-view localization method using pseudo-satellite images and a VAE for robust feature learning, addressing geometric and appearance domain gaps.

Findings

Competitive performance on University-1652 and SUES-200 datasets.

Effective for satellite-to-drone localization tasks.

No reliance on text prompts or large annotations.

Abstract

With the rapid growth of the low-altitude economy, unmanned aerial vehicles (UAVs) have become key platforms for measurement and tracking in intelligent patrol systems. However, in GNSS-denied environments, localization schemes that rely solely on satellite signals are prone to failure. Cross-view image retrieval-based localization is a promising alternative, yet substantial geometric and appearance domain gaps exist between oblique UAV views and nadir satellite orthophotos. Moreover, conventional approaches often depend on complex network architectures, text prompts, or large amounts of annotation, which hinders generalization. To address these issues, we propose DiffusionUavLoc, a cross-view localization framework that is image-prompted, text-free, diffusion-centric, and employs a VAE for unified representation. We first use training-free geometric rendering to synthesize pseudo-satellite images from UAV imagery as structural prompts. We then design a text-free conditional diffusion model that fuses multimodal structural cues to learn features robust to viewpoint changes. At inference, descriptors are computed at a fixed time step t and compared using cosine similarity. On University-1652 and SUES-200, the method performs competitively for cross-view localization, especially for satellite-to-drone in University-1652.Our data and code will be published at the following URL: https://github.com/liutao23/DiffusionUavLoc.git.