{"type":"ScholarlyArticle","title":"Beyond the Clouds: Reliable and Cloud-Aware Spatiotemporal Fusion via Adversarial Regression Wavelets","authors":[{"name":"Sichen Lu","slug":"sichen-lu","given":"Sichen","family":"Lu","affiliation":""},{"name":"Mingfei Li","slug":"mingfei-li","given":"Mingfei","family":"Li","affiliation":""},{"name":"Juanjuan Jing","slug":"juanjuan-jing","given":"Juanjuan","family":"Jing","affiliation":""},{"name":"Junhua Yu","slug":"junhua-yu","given":"Junhua","family":"Yu","affiliation":""},{"name":"Lei Yang","slug":"lei-yang","given":"Lei","family":"Yang","affiliation":""},{"name":"Boyang Nie","slug":"boyang-nie","given":"Boyang","family":"Nie","affiliation":""},{"name":"Jinsong Zhou","slug":"jinsong-zhou","given":"Jinsong","family":"Zhou","affiliation":""}],"abstract":"Spatiotemporal fusion (STF) bridges the gap between temporal and spatial resolutions in satellite imagery, enabling effective monitoring of Earth's surface dynamics. However, existing methods rely on cloud-free reference images, a constraint that fails in realistic, cloud-prone scenarios. To overcome this, we propose the Cloud-Aware Wavelet Generative Adversarial Network (CLAW-GAN), a novel framework for high-fidelity reconstruction under cloud-contaminated conditions. CLAW-GAN introduces Regression Wavelet Analysis (RWA) to decouple spectral backgrounds from structural details. While a change-aware gated mechanism accounts for land-cover changes, the Frequency-Separated Fusion (FSF) module then independently integrates these components. To ensure visual realism, a multi-scale discriminator operates in the wavelet domain, enforcing consistency across high-frequency subbands to minimize artifacts. Evaluated on the newly introduced Global Cloud-shrouded Agricultural Regions (GCAR) benchmark and the simulated Daxing dataset, CLAW-GAN achieves state-of-the-art performance and demonstrates superior robustness across varying cloud coverage.","doi":"10.24963/ijcai.2026/163","url":"https://ijcai.org/proceedings/2026/163","pdf":"https://ijcai.org/proceedings/2026/0163.pdf","markdown":"https://ijcai.org/proceedings/2026/163.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/163","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Computer Vision","track":"Main Track","number":163,"pages":{"first":1460,"last":1468},"keywords":["Computer Vision: Image and video synthesis and generation","Computer Vision: Low-level Vision","Computer Vision: Adversarial learning, adversarial attack and defense methods","Humans and AI: Computational sustainability and human wellbeing"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}