{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/cross-directional-feature-fusion-network-for","title":"Cross-directional Feature Fusion Network for Building Damage Assessment from Satellite Imagery","arxiv_id":"2010.14014","date":"2020-10-27","proceeding":null,"authors":["Yu Shen","Sijie Zhu","Taojiannan Yang","Chen Chen"],"abstract":"Fast and effective responses are required when a natural disaster (e.g., earthquake, hurricane, etc.) strikes. Building damage assessment from satellite imagery is critical before an effective response is conducted. High-resolution satellite images provide rich information with pre- and post-disaster scenes for analysis. However, most existing works simply use pre- and post-disaster images as input without considering their correlations. In this paper, we propose a novel cross-directional fusion strategy to better explore the correlations between pre- and post-disaster images. Moreover, the data augmentation method CutMix is exploited to tackle the challenge of hard classes. The proposed method achieves state-of-the-art performance on a large-scale building damage assessment dataset -- xBD.","url_abs":"https://arxiv.org/abs/2010.14014v2","url_pdf":"https://arxiv.org/pdf/2010.14014v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"2d-semantic-segmentation","task_name":"2D Semantic Segmentation"},{"task_slug":"building-damage-assessment","task_name":"Building Damage Assessment"},{"task_slug":"data-augmentation","task_name":"Data Augmentation"}],"methods":[{"method_slug":"cutmix","method_name":"CutMix"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/2d-semantic-segmentation-on-xbd","task":"2D Semantic Segmentation","dataset":"xBD","model":"Double branch U-Net","rank_in_archive_order":3,"of":5,"metrics":{"Weighted Average F1-score":"0.804"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2010.14014","atlas_url":"https://app.syntology.ai/?focus=2010.14014","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}