[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127479-en":3,"doc-seo-127479-105":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},127479,962084925290,"Ophelia","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Enhancing Interpretable Machine Learning for Earth Observation - Doctoral Thesis","Remote sensing enables Earth Observation applications through rich, diverse measurements. This thesis addresses a core tension in machine learning for EO: deep models often achieve high performance at the cost of reduced interpretability, which is essential for sensitive human activity monitoring and natural-disaster response. The work advances explainable AI methods for remote sensing, targeting justification of predictions and improvement of modeling via feature analysis, vegetation-index design, and multi-modal, multi-task evaluation.","ENHANCING INTERPRETABLE MACHINE LEARNING FOR EARTH OBSERVATION  \nThesis approved by the Department of Computer Science University of Kaiserslautern-Landau for the award of the Doctoral Degree Doctor of Engineering (Dr.-Ing.)  \nto  \nHiba Najjar  \nDate of Defense: 16 September 2025  \nDean: Prof. Dr. Christoph Garth Reviewers: Prof. Dr. Prof. h.c. Andreas Dengel  \nProf. Dr. Sebastian Vollmer  \nDE-386  \nHiba Najjar:  \nENHANCING INTERPRETABLE MACHINE LEARNING FOR EARTH OBSERVA TION  \nCONTACT INFORMATION:  \n[Email:](Email: najjar@rptu.de)[ najjar@rptu.de](Email: najjar@rptu.de), [hibanajjar998@gmail.com](hibanajjar998@gmail.com).  \nTo my beloved mother, Sanaa Zahidi, my late father, Mohammed Najjar, and all my brothers and sisters in Gaza.  \nAbstract  \nRemote sensing (RS) provides abundant and diverse data for Earth Observation (EO) applications. Machine learning leverages the available data through deep neural networks and specialized architectures. However, increasing model complexity often compromises its interpretability, which is crucial for many EO applications that monitor sensitive human activities or support natural disaster response efforts. This thesis contributes to advancing the interpretability and explainability of complex AI models for various RS applications, with a specific focus on agricultural activities.  \nOur work employs eXplainable AI (XAI) methods to address two main objective for understanding and improving the model predictions within EO applications. First, we focus on XAI for Justification where the model behavior is justified by analyzing how different input features contribute to the outputs. The explanation of individual predictions are leveraged and aggregated to provide a broader understanding of the model’s behavior. We apply and evaluate existing model-agnostic and model-specific methods, while also developing new techniques when necessary. We further explore how multi-task learning can further enhance the explainability of predictions.  \nSecond, we apply XAI for Improvement based on insights from our prior model justification results. On one hand, we identify the features that are necessary and sufficient for accurate modeling across different contexts. On the other hand, we focus on optimizing the selection and design of vegetation indices, a key component in EO analysis and modeling.  \nWe benchmark our explainability objectives across multiple datasets, covering a range of tasks in EO. We particularly focus on multi-modal datasets, commonly used in EO, to mitigate the research gap regarding the explanation of complex multi-modal networks. The results demonstrate that our approach effectively explains the models by verifying that the model reasoning aligns with expert knowledge. Additionally, our experiments on vegetation indices and the optimization of models through feature reduction yielded promising results, and contributed to enhanced overall model performance and interpretability.  \nOverall, this thesis provides a thorough examination of the interpretability of ML models under complex modeling scenarios. It leverages various explainability tools and objectives to justify model predictions and improve the modeling strategy and performance. This work contributes an important building block towards more transparent and better performing ML models designed for EO applications.  \nv  \nAcknowledgement  \nAll praise is for Allah, without Whom this thesis would not have come to be, Whose guidance has been constant, and Whose mercy has encompassed me throughout this journey. I am grateful for the clarity of heart He bestowed upon me, allowing me to witness His support and strengthen my faith. And I acknowledge that my praise can never truly encompass the perfection of Allah nor fully express the magnitude of His favors upon me.  \nI would like to express my gratitude to Prof. Dr. Andreas Dengel for granting me the opportunity to pursue my PhD in his research group, and for his support throughout this ","cbCaiafAuKPgvUd3","https://ap.wps.com/l/cbCaiafAuKPgvUd3","pdf",63579419,1,199,"English","en",105,"# Abstract\n## XAI for Justification\n## XAI for Improvement\n## Benchmarking Across Datasets","[{\"question\":\"What problem does the thesis address in Earth Observation machine learning?\",\"answer\":\"It addresses the interpretability gap of complex deep models used in remote sensing, which can hinder trust and usability in EO tasks.\"},{\"question\":\"How does the work use explainable AI for justification?\",\"answer\":\"It analyzes how input features contribute to outputs, leveraging and aggregating explanations of individual predictions to understand overall model behavior.\"},{\"question\":\"How does explainable AI support improvement in this thesis?\",\"answer\":\"It uses justification insights to identify necessary and sufficient features and to optimize the selection and design of vegetation indices for EO modeling.\"}]","Enhancing Interpretable Machine Learning for Earth Observation - Doctoral Thesis | PDF",1785939230,501,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"enhancing-interpretable-machine-learning-for-earth-observation-doctoral-thesis","",{"@graph":36,"@context":85},[37,54,68],{"@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":53},"https://docshare.wps.com/document/enhancing-interpretable-machine-learning-for-earth-observation-doctoral-thesis/127479/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the thesis address in Earth Observation machine learning?","Question",{"text":75,"@type":76},"It addresses the interpretability gap of complex deep models used in remote sensing, which can hinder trust and usability in EO tasks.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the work use explainable AI for justification?",{"text":80,"@type":76},"It analyzes how input features contribute to outputs, leveraging and aggregating explanations of individual predictions to understand overall model behavior.",{"name":82,"@type":73,"acceptedAnswer":83},"How does explainable AI support improvement in this thesis?",{"text":84,"@type":76},"It uses justification insights to identify necessary and sufficient features and to optimize the selection and design of vegetation indices for EO modeling.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]