[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120827-en":3,"doc-seo-120827-105":30,"detail-sidebar-cat-0-en-105":90},{"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},120827,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","AD-HOC SITUATIONAL AWARENESS DURING FLOODS - USING REMOTE SENSING DATA AND MACHINE LEARNING METHODS","Recent advances in machine learning and large-scale remote sensing datasets enable automation of disaster-oriented data analysis despite rapidly changing spatio-temporal conditions. The work presents machine learning methods developed by the German Aerospace Center (DLR) to support rapid mapping for the 2021 Western Germany flood response. It covers systematic Sentinel-1 flood monitoring, road-network extraction, object detection, and damage assessment using high-resolution optical satellite and aerial imagery. Data acquisition aspects and operational results used by first responders are discussed.","AD-HOC SITUATIONAL AWARENESS DURING FLOODS USING REMOTE SENSING  \nDATA AND MACHINE LEARNING METHODS  \nMarc Wieland1, Nina Merkle2, Anne Schneibel1, Corentin Henry2, Konstanze Lechner1, Xiangtian Yuan2  \nSeyed Majid Azimi2, Veronika Gstaiger2, Sandro Martinis1  \n1 German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Oberpfaffenhofen, Germany  \n2 German Aerospace Center (DLR), Remote Sensing Technology Institute (IMF), Oberpfaffenhofen, Germany  \nABSTRACT  \nRecent advances in machine learning and the rise of new large-scale remote sensing datasets have opened new possibilities for automation of remote sensing data analysis that make it possible to cope with the growing data volume and complexity and the inherent spatio-temporal dynamics of disaster situations. In this work, we provide insights into machine learning methods developed by the German Aerospace Center (DLR) for rapid mapping activities and used to support disaster response efforts during the 2021 flood in Western Germany. These include specifically methods related to systematic flood monitoring from Sentinel-1 as well as road-network extraction, object detection and damage assessment from very high-resolution optical satellite and aerial images. We discuss aspects of data acquisition and present results that were used by first responders during the flood disaster.  \nIndex Terms— Disaster response, flood monitoring, road network extraction, object detection, damage assessment  \n1. INTRODUCTION  \nRapid disaster response is critical for saving lives and minimizing the impact of natural disasters. Traditional methods of analyzing remote sensing data (satellite, aerial or drone imagery) for supporting an up-to-date situational awareness during disasters can be slow and labor-intensive, which might delay response efforts. Recent advances in machine learning and the rise of new large-scale remote sensing datasets have opened new possibilities for the automation of remote sensing data analysis to cope with the growing data volume and complexity and the inherent spatio-temporal dynamics of disaster situations.  \nIn this work, we provide insights into machine learning methods developed by the German Aerospace Center (DLR) for rapid mapping activities and used to support disaster response efforts during the 2021 floods in Western Germany. We discuss several aspects of the data acquisition  \nand present results that were used by first responders during the flood disaster. On the basis of the acquired data, we further show experimental results of research activities that have been conducted within the projects Drones4Good (safe, targeted and autonomous humanitarian transportation) [1], AIFER (artificial intelligence for analysis and fusion of earth observation and internet data to support situational awareness in emergency response) [2] and Data4Human (demand-driven data services for humanitarian aid) [3] .  \n2. DATA AND STUDY AREA  \nThe German districts of North Rhine-Westphalia and Rhineland-Palatinate were severely affected by rainfalltriggered floods on 14.07.2021 and 15.07.2021. The Center for Satellite based Crisis Information (ZKI) of the DLR supported the emergency and rescue teams with satellite data and aerial images that have been acquired, processed and analyzed within hours after notification. Flight campaigns were carried out on 15.07.2021, 16.07.2021 and 20.07.2021 using DLR's 3K [4], 4k [5] and MACS [6] camera systems from helicopters and aircraft platforms, obtaining data with aground sampling distance between 10cm and 20cm. Continuous surface water monitoring with Sentinel-1 Synthetic Aperture Radar (SAR) images over Germany provided further information about flood extent on a daily basis between 14.07.2021 and 20.07.2021. In the aftermath of the disaster, several drone-based surveys were conducted in the most severely affected areas on 23.10.2021 and 29.10.2022.  \n3. FLOOD MONITORING  \nWe deployed a modular processing chain for surface water ","cbCaisdFHwWpLGwd","https://ap.wps.com/l/cbCaisdFHwWpLGwd","pdf",520628,1,4,"English","en",105,"# Introduction\n# Data and Study Area\n# Flood Monitoring\n# Road Network Extraction","[{\"question\":\"Why is rapid disaster response critical during floods?\",\"answer\":\"Rapid response saves lives and reduces disaster impact. Delays caused by slow, labor-intensive remote sensing analysis can hinder up-to-date situational awareness.\"},{\"question\":\"How does the approach perform flood monitoring using Sentinel-1?\",\"answer\":\"It uses a pre-trained water segmentation model to extract water masks from Sentinel-1 SAR images, separating temporary flooded areas from permanent water using a two-year reference mask.\"},{\"question\":\"What methods support mapping the road network and assessing damage?\",\"answer\":\"By identifying flooded areas, the system compares pre-disaster and post-disaster road detections to locate potentially damaged road sections, and it uses object detection and damage assessment from high-resolution optical satellite and aerial images.\"}]","AD-HOC SITUATIONAL AWARENESS DURING FLOODS - USING REMOTE SENSING DATA AND MACHINE LEARNING METHODS | PDF",1785732212,10,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":28},"ad-hoc-situational-awareness-during-floods-using-remote-sensing-data-and-machine-learning-methods","",{"@graph":36,"@context":84},[37,53,67],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":21},"https://docshare.wps.com/document/ad-hoc-situational-awareness-during-floods-using-remote-sensing-data-and-machine-learning-methods/120827/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why is rapid disaster response critical during floods?","Question",{"text":74,"@type":75},"Rapid response saves lives and reduces disaster impact. Delays caused by slow, labor-intensive remote sensing analysis can hinder up-to-date situational awareness.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does the approach perform flood monitoring using Sentinel-1?",{"text":79,"@type":75},"It uses a pre-trained water segmentation model to extract water masks from Sentinel-1 SAR images, separating temporary flooded areas from permanent water using a two-year reference mask.",{"name":81,"@type":72,"acceptedAnswer":82},"What methods support mapping the road network and assessing damage?",{"text":83,"@type":75},"By identifying flooded areas, the system compares pre-disaster and post-disaster road detections to locate potentially damaged road sections, and it uses object detection and damage assessment from high-resolution optical satellite and aerial images.","https://schema.org",{"og:url":52,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,127,130,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":29,"slug":132},"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]