{"type":"ScholarlyArticle","title":"CrossRefine: A Microservice for Cross-Domain Spatial Super-Resolution","authors":[{"name":"Daniil Sukhorukov","slug":"daniil-sukhorukov","given":"Daniil","family":"Sukhorukov","affiliation":""},{"name":"Andrei Zakharov","slug":"andrei-zakharov","given":"Andrei","family":"Zakharov","affiliation":""},{"name":"Ilya Makarov","slug":"ilya-makarov","given":"Ilya","family":"Makarov","affiliation":""}],"abstract":"High-resolution spatial fields are critical for local decision-making, yet many operational and scientific workflows produce coarse outputs due to computational limits. We present CrossRefine, a deployable microservice for cross-domain spatial super-resolution that enhances multi-channel spatial tiles without modifying upstream models. It is built around a unified, topography-conditioned adversarial UNet trained across geographically diverse regions to ensure robustness to heterogeneous terrains and domain shifts. Unlike region-specific enhancement models, the system generalizes across domains within a single architecture, balancing numerical fidelity and structural realism through a hybrid regression–adversarial objective. The service provides REST API endpoints for batch and streaming inference, supports mixed-precision, and offers per-tile diagnostics and confidence maps to promote safe deployment.\r\nIn our demo, we show interactive refinement of coarse spatial inputs, side-by-side comparison with interpolation and non-adversarial baselines, and real-time profiling of latency and throughput on commodity hardware. CrossRefine illustrates how spatial super-resolution can be delivered as a practical AI microservice, enabling scalable refinement of existing computational workflows without requiring higher-resolution upstream simulations.\r\n\r\nDemonstration video: https://shorturl.at/lz2un","doi":"10.24963/ijcai.2026/996","url":"https://ijcai.org/proceedings/2026/996","pdf":"https://ijcai.org/proceedings/2026/0996.pdf","markdown":"https://ijcai.org/proceedings/2026/996.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/996","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":"Demo Track","track":"Demo Track","number":996,"pages":{"first":8530,"last":8533},"keywords":["AI: Multidisciplinary Topics and Applications","AI: Computer Vision"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}