{"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/robust-burned-area-delineation-through","title":"Robust Burned Area Delineation through Multitask Learning","arxiv_id":"2309.08368","date":"2023-09-15","proceeding":null,"authors":["Edoardo Arnaudo","Luca Barco","Matteo Merlo","Claudio Rossi"],"abstract":"In recent years, wildfires have posed a significant challenge due to their increasing frequency and severity. For this reason, accurate delineation of burned areas is crucial for environmental monitoring and post-fire assessment. However, traditional approaches relying on binary segmentation models often struggle to achieve robust and accurate results, especially when trained from scratch, due to limited resources and the inherent imbalance of this segmentation task. We propose to address these limitations in two ways: first, we construct an ad-hoc dataset to cope with the limited resources, combining information from Sentinel-2 feeds with Copernicus activations and other data sources. In this dataset, we provide annotations for multiple tasks, including burned area delineation and land cover segmentation. Second, we propose a multitask learning framework that incorporates land cover classification as an auxiliary task to enhance the robustness and performance of the burned area segmentation models. We compare the performance of different models, including UPerNet and SegFormer, demonstrating the effectiveness of our approach in comparison to standard binary segmentation.","url_abs":"https://arxiv.org/abs/2309.08368v1","url_pdf":"https://arxiv.org/pdf/2309.08368v1.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":[{"paper_slug":"robust-burned-area-delineation-through","repo_url":"https://github.com/links-ads/maclean-burned-area-segmentation","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"robust-burned-area-delineation-through","repo_url":"https://github.com/links-ads/burned-area-seg","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"burned-area-delineation","task_name":"Burned Area Delineation"},{"task_slug":"land-cover-classification","task_name":"Land Cover Classification"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"mix-ffn","method_name":"Mix-FFN"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"segformer","method_name":"SegFormer"}],"datasets_introduced":[{"slug":"cems-w","name":"CEMS-W","full_name":"CEMS Wildires"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/semantic-segmentation-on-cems-w","task":"Semantic Segmentation","dataset":"CEMS-W","model":"UPerNet (RN50)","rank_in_archive_order":1,"of":3,"metrics":{"mIoU":"84.94"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-cems-w","task":"Semantic Segmentation","dataset":"CEMS-W","model":"SegFormer (MiT-B3)","rank_in_archive_order":2,"of":3,"metrics":{"mIoU":"83.34"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-cems-w","task":"Semantic Segmentation","dataset":"CEMS-W","model":"UPerNet (ViT-S)","rank_in_archive_order":3,"of":3,"metrics":{"mIoU":"82.98"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}