[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123625-en":3,"doc-seo-123625-105":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},123625,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","MACHINE LEARNING FOR THE DOCUMENTATION, PREDICTION, AND AUGMENTATION OF HERITAGE STRUCTURE DATA - Paper","The paper develops learning models from large-scale digital documentation of heritage structures collected over decades, with emphasis on disaster zones and earthquake damage scenarios. An ontology is proposed to represent heritage buildings, sites, and hazard events, enabling structured data for machine learning systems that detect damage patterns from image records. For seismic regions, earthquake and damage information are analyzed to create linked relationships, such as damage extent versus magnitude and epicentral distance, while supporting image-grounded sub-model selection.","MACHINE LEARNING FOR THE DOCUMENTATION, PREDICTION, AND AUGMENTATION OF HERITAGE STRUCTURE DATA  \nSatwant Rihal*, Hisham Assal  \nCal Poly State University, San Luis Obispo, California, USA –(srihal, hhassal)@[calpoly.edu](calpoly.edu)  \nKEY WORDS: Heritage structures, Survey and Documentation, Heritage Data, Machine Learning, Natural Hazards, Ontology  \nABSTRACT:  \nThe paper presents an effort to develop learning models based on the massive amounts of data that has been accumulated over the past decades during the process of digital documentation of heritage structures around the globe especially those in disaster zones. The development of an ontology is proposed that describes heritage buildings, their sites, and major hazard events that may cause damage to them. This ontology can serve as a repository for documenting heritage structures and provide highly structured data for developing machine learning systems that can identify patterns of damage from recorded image data. For heritage structures in seismic zones, the first step in ontology development is analyzing available earthquake information about the event and the damage information. The resulting model will create links between information items, for example relating the extent of the damage of an element to the earthquake magnitude and its distance from the epicenter. The ontology may also include collected images from previous earthquake events, with links to the objects in each image. Special tools will focus on selecting sub-models to be included in a machine learning model. For example, if the learning objective is to identify the damage and its extent from an image, then the rules will select the features in the model that relate to structural damage and identify each type of damage. It is hoped that this work will help develop learning systems that speed up processing of large volumes of image damage data collected from heritage sites.  \n1. INTRODUCTION  \nDuring the past thirty years the world has experienced unprecedented levels of devastation, loss of innocent lives and staggering losses caused by catastrophic earthquakes as presented in Table 1.  \n\n| Location | year | Magnitude | Fatalities | Economic Losses B$ |\n| --- | --- | --- | --- | --- |\n| Kobe, Japan | 1995 | 6.9 | > 6000 | 197 |\n| Afghanistan | 1998 | 6.6 | 4000 |  |\n| Izmit, Turkey | 1999 | 7.6 | 20000 | 19 |\n| Gujarat, India | 2001 | 7.6 | 20000 | 5 |\n| Indonesia | 2004 | 9.1 | 230,000 | 15 |\n| Sichuan, China | 2008 | 7.9 | 87,500 | 148 |\n| Haiti | 2010 | 7.0 | 316,000 | 14 |\n| Chile | 2010 | 8.8 | 521 | 30 |\n| Tohoku, Japan | 2011 | 9.0 | 18,400 | 360 |\n| Christchurch, New Zealand | 2010 | 7.1 |  | 40 |\n| Emilia-Romagna Italy | 2012 | 6.1 | 27 | 16 |\n| Nepal | 2015 | 7.8 | 8,800 | 10 |\n| Central Italy | 2016 | 6.2 | 247 | 0.208 Euros (insured) |\n| Central Mexico | 2017 | 7.1 | >370 | 4 |\n| Turkey/Syria | 2023 | 7.8 | 57,658 | 104 |\n\nTable 1. Major earthquakes over the last 30 years The resulting aftermath of human toll of hundreds of thousands of lives lost, and economic losses amounting to billions of dollars seriously affected countries’ economies in different  \n* Corresponding author  \nsectors, such as housing, infrastructure, health, education, industry, cultural heritage and tourism.  \nWith the recent advancements in digital technologies and tools available for the documentation of heritage structures, (e.g. satellites, UAV, TLS, drones, GPS, and GIS), massive volumes of data have been collected and accumulated over the recent decades during the survey and documentation process of field reconnaissance and observed damage following devastating earthquakes, (Hutchinson, 2017), (Hadick, 2022) . The data covers a wide range of heritage structures in many regions around the world. The sheer volume of data makes it difficult to comprehend the extent of coverage and utilize it in a useful way.  \nFigure 1. Typical damage of domes – Historical Churches September 2017 Puebla, Mexico earthquake  \nFigur","cbCaivVSKsdj3nNb","https://ap.wps.com/l/cbCaivVSKsdj3nNb","pdf",1072643,1,7,"English","en",105,"# Introduction\n## Motivation and impact of major earthquakes\n## Digital documentation data and challenges\n## Ontology-based environment for heritage data\n## Building information models and deep learning workflow","[{\"question\":\"What problem does the paper address in heritage documentation after earthquakes?\",\"answer\":\"It addresses the difficulty of handling massive volumes of collected heritage survey and damage data so that useful, structured insights can be extracted for analysis and learning.\"},{\"question\":\"How does the proposed ontology contribute to machine learning for heritage structures?\",\"answer\":\"The ontology provides a common representational model linking heritage buildings, their sites, hazard events, and related damage concepts, producing structured data that machine learning systems can use.\"},{\"question\":\"What relationships does the paper expect the model to learn for seismic zones?\",\"answer\":\"It focuses on creating links between information items—for example, relating the extent of damage of a structural element to earthquake magnitude and distance from the epicenter.\"}]","MACHINE LEARNING FOR THE DOCUMENTATION, PREDICTION, AND AUGMENTATION OF HERITAGE STRUCTURE DATA - 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