[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118185-en":3,"doc-seo-118185-105":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},118185,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine Learning Approaches for Building Inventory Characterisation - Thesis Abstract","Accurate building inventories and associated information underpin sustainable urban governance and support Sustainable Development Goal 11 on sustainable cities and communities. Manual acquisition of building information in dense, complex urban environments is impractical and often cannot deliver the thematic and spatial detail required by real-world applications. This thesis leverages remote sensing advances and machine learning to automate building characterisation from VHR satellite/drone imagery and OpenStreetMap data, focusing on the comparative effectiveness of Random Forest and deep neural networks for multi-class modelling.","This is an excerpt from the thesis “Machine Learning Approaches for Building  \nInventory Characterisation”.  \nPlease contact Sunil Tamang for a full version of the thesis.  \nMachine Learning Approaches for Building Inventory Characterisation  \nA thesis submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies  \nby  \nSunil Tamang  \nSupervised by:  \nDr. Bakhtiar Feizizadeh  \nInstitute of Geoinformatics  \nUniversity of Münster  \nMünster, Germany  \nCo-supervised by:  \nDr. Marco Painho  \nNOVA Information Management School Universidade Nova de Lisboa Lisboa, Portugal  \nDr. Christian Geiss  \nGerman Remote Sensing Data Center German Aerospace Center (DLR) Oberpfaffenhofen, Germany  \n20 February 2024  \nAbstract  \nAccurate building inventories and relevant information are essential for sustainable urban governance, thereby contributing to achieve the Sustainable Development Goal 11, which focuses on sustainable cities and communities. Acquiring building information manually in complex-built environment with densely populated buildings is impractical and may not guarantee the high thematic and spatial detail necessary for real world applications.  \nRecent advancements in remote sensing technology, coupled with the availability of highresolution satellite and drone-based imagery through open access channels and as machine learning (ML) and deep learning (DL) continue to advance, showcasing their capability to recognise complex patterns, new opportunities for interpreting various surface features on the Earth are made possible. A growing body of literature emphasises the use of both traditional MLand DL for building footprint extraction, yet available and accessible literature reveal limited application of DL in urban building characterisation.  \nThis study involves exploring and implementing Random Forest (RF) as machine learning model and dense neural network (DNN) as deep learning model in the context of multi-class building characterisation encompassing six classes. A total of 35 geometric and distribution features calculated using VHR imagery and OpenStreetMap data are used to train the model. The experiments show that overall accuracy of RF (79 .9%) is higher than that of the DNN (71 .9%) . Upon closer examination and comparison of diagonal elements, representing the number of correctly classified samples for each class, it is found that the DNN outperforms RF in correctly classifying more instances for four classes. Further the recall rate using DNN is greater for four classes- ‘Building block in closed construction’, ‘Detached building block’, ‘Free standing individual building’, and ‘Garage’, in comparison to that of RF. Implementation of the DNN and comparison with traditional machine learning algorithm- RF provide additional scientific contribution, especially in situation where there is limited use of deep learning algorithms in the building characterisation.  \nKeywords: Building Footprints, Characterisation, Random Forest, Dense Neural Network  \nTable of Contents  \nDeclaration...................................................................................................................... ii  \nCopyright Statement....................................................................................................... iii  \nAcknowledgements ........................................................................................................ iv  \nAbstract...........................................................................................................................v  \nList of Tables.................................................................................................................. vii  \nList of Figures................................................................................................................ viii  \nList of Abbreviations ....................................................................................................... i","cbCaifNk9rSHrDRW","https://ap.wps.com/l/cbCaifNk9rSHrDRW","pdf",1107571,1,17,"English","en",105,"# Abstract\n## Research motivation and problem\n## Method and models\n## Experiments and results\n## Thesis contribution and keywords","[{\"question\":\"Why is building inventory characterisation important for urban governance?\",\"answer\":\"Accurate building inventories provide essential information for sustainable urban governance and help support Sustainable Development Goal 11 focused on sustainable cities and communities.\"},{\"question\":\"What machine learning models are evaluated in this study?\",\"answer\":\"The study implements Random Forest (RF) as a machine learning model and a dense neural network (DNN) as a deep learning model for multi-class building characterisation.\"},{\"question\":\"How do RF and DNN compare in overall accuracy and class-wise performance?\",\"answer\":\"RF achieves higher overall accuracy (79.9%) than DNN (71.9%), but DNN outperforms RF for correctly classifying more instances in four classes and shows higher recall for those same four classes.\"}]","Machine Learning Approaches for Building Inventory Characterisation - 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