[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126754-en":3,"doc-seo-126754-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126754,962084928432,"Emma Wilson","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","A Multimodal Supervised Machine Learning Approach for Satellite-based Wildfire Identification in Europe - read online free","The document proposes a rapid, automated wildfire identification solution that improves the accuracy of satellite-based hotspot detection by fusing multiple information sources. Thermal anomalies from MODIS and VIIRS hotspot services are cross-referenced with the EFFIS database to build a large-scale Europe-wide hotspot dataset. A multimodal supervised machine learning approach then disambiguates hotspot detections by separating wildfires from other events using data such as ERSI annual LULC and Copernicus Sentinel-3, with experiments confirming effectiveness.","POLITECNICO DI TORINO  \nRepository ISTITUZIONALE  \nA Multimodal Supervised Machine Learning Approach for Satellite-based Wildfire Identification in Europe  \nOriginal  \nA Multimodal Supervised Machine Learning Approach for Satellite-based Wildfire Identification in Europe / Urbanelli, Angelica; Barco, Luca; Arnaudo, Edoardo; Rossi, Claudio. - (2023), pp. 608-611. (Intervento presentato al convegno 2023 IEEE International Symposium on Geoscience and Remote Sensing (IGARSS 2023) tenutosi a Pasadena (USA) nel 16-21 July, 2023) [10 . 1109/IGARSS52108 .2023. 10282227] .  \nAvailability:  \nThis version is available at: 11583/2981340 since: 2023-08-29T09:06:13Z  \nPublisher: IEEE  \nPublished  \nDOI:10.1109/IGARSS52108.2023.10282227  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \nIEEE postprint/Author's Accepted Manuscript  \n©2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collecting works, for resale or lists, or reuse of any copyrighted component of this work in other works.  \n(Article begins on next page)  \n07 November 2024  \nA MULTIMODAL SUPERVISED MACHINE LEARNING APPROACH FOR SATELLITE-BASED WILDFIRE IDENTIFICATION IN EUROPE  \nAngelica Urbanelli 1∗, Luca Barco 1∗, Edoardo Arnaudo 1 ,2, Claudio Rossi 1  \n1. LINKS Foundation, AI, Data & Space (ADS), Torino (TO), Italy  \n2. Politecnico di Torino, Dipartimento di Automatica e Informatica (DAUIN) , Torino (TO), Italy  \nABSTRACT  \nThe increasing frequency of catastrophic natural events, such as wildfires, calls for the development of rapid and automated wildfire detection systems. In this paper, we propose a wildfire identification solution to improve the accuracy of automated satellite-based hotspot detection systems by leveraging multiple information sources. We cross-reference the thermal anomalies detected by the Moderate-resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) hotspot services with the European Forest Fire Information System (EFFIS) database to construct a large-scale hotspot dataset for wildfire-related studies in Europe. Then, we propose a novel multimodal supervised machine learning approach to disambiguate hotspot detections, distinguishing between wildfires and other events. Our methodology includes the use of multimodal data sources, such as the ERSI annual Land Use Land Cover (LULC) and the Copernicus Sentinel-3 data. Experimental results demonstrate the effectiveness of our approach in the task of wildfire identification.  \nIndex Terms— machine learning, computer vision, earth observation, hotspot disambiguation.  \n1. INTRODUCTION  \nThe world is experiencing a surge in catastrophic natural events, including wildfires, floods, and storms, the frequency and magnitude of which are exacerbated by climate change effects. The rising global average temperature is expected to increase the risk of fires across a wide range of latitudes, leading to more frequent and impacting events and consequently to the release of a significant amount of smoke, carbon dioxide and heat, which further drives up climate change [1] . Automated wildfire detection systems are crucial to identify fires in their early stage, allowing a prompt response and a consequent reduction of the impacts on human ecosystems, especially in remote areas that are sparsely populated and urbanized.  \nThis work was carried out in the context ofthe H2020 projects: SAFERS (GA n.869353) and OVERWATCH (GA n.101082320)  \n* Equal contribution.  \nIn this context, the Moderate-resolution Imaging Spectroradiometer (MODIS), and the Visible Infrared Imaging Radiometer Suite (VIIRS) provide operational services aimed at detecting thermal anomalies. On ","cbCait3YzJC8tlxp","https://ap.wps.com/l/cbCait3YzJC8tlxp","pdf",1685366,1,5,"English","en",105,"# Introduction\n## Problem context: automated wildfire detection and false positives\n## Proposed solution: hotspot disambiguation with multimodal data\n# Related Works\n## Satellite-based fire detection and hotspot extraction\n# Method Overview\n## Large-scale hotspot dataset construction using EFFIS\n## Multimodal supervised learning to distinguish wildfire events","[{\"question\":\"What is the main goal of the proposed approach?\",\"answer\":\"To improve the accuracy of satellite-based wildfire detection by disambiguating hotspot detections and distinguishing wildfires from other events.\"},{\"question\":\"How is the hotspot dataset constructed?\",\"answer\":\"Thermal anomalies from MODIS and VIIRS are cross-referenced with the EFFIS database to build a large-scale hotspot dataset for studies in Europe.\"},{\"question\":\"Which multimodal data sources are used in the method?\",\"answer\":\"The approach uses multimodal inputs such as ERSI annual LULC and Copernicus Sentinel-3 data, alongside hotspot services from MODIS and VIIRS.\"}]","A Multimodal Supervised Machine Learning Approach for Satellite-based Wildfire Identification in Europe - 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