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arXiv · 2604.14707

Geo2Sound: A Scalable Geo-Aligned Framework for Soundscape Generation from Satellite Imagery

Kunlin Wu·Yanning Wang·Haofeng Tan·Boyi Chen·Teng Fei·Xianping Ma·Yang Yue·Zan Zhou·Xiaofeng Liu
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Abstract

Recent image-to-audio models have shown impressive performance on object-centric visual scenes. However, their application to satellite imagery remains limited by the complex, wide-area semantic ambiguity of top-down views. While satellite imagery provides a uniquely scalable source for global soundscape generation, matching these views to real acoustic environments with unique spatial structures is inherently difficult. To address this challenge, we introduce Geo2Sound, a novel task and framework for generating geographically realistic soundscapes from satellite imagery. Specifically, Geo2Sound combines structural geospatial attributes modeling, semantic hypothesis expansion, and geo-acoustic alignment in a unified framework. A lightweight classifier summarizes overhead scenes into compact geographic attributes, multiple sound-oriented semantic hypotheses are used to generate diverse acoustically plausible candidates, and a geo-acoustic alignment module projects geographic attributes into the acoustic embedding space and identifies the candidate most consistent with the candidate sets. Moreover, we establish SatSound-Bench, the first benchmark comprising over 20k high-quality paired satellite images, text descriptions, and real-world audio recordings, collected from the field across more than 10 countries and complemented by three public datasets. Experiments show that Geo2Sound achieves a SOTA FAD of 1.765, outperforming the strongest baseline by 50.0%. Human evaluations further confirm substantial gains in both realism (26.5%) and semantic alignment, validating our high-fidelity synthesis on scale. Project page and source code: https://github.com/Blanketzzz/Geo2Sound

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Kunlin Wu, Yanning Wang, Haofeng Tan, Boyi Chen, Teng Fei, Xianping Ma, Yang Yue, Zan Zhou, Xiaofeng Liu. 2026-04-16. Geo2Sound: A Scalable Geo-Aligned Framework for Soundscape Generation from Satellite Imagery. https://arxiv.org/abs/2604.14707

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