Researcher:
Kabadayı, Mustafa Erdem

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Mustafa Erdem

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Kabadayı

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Kabadayı, Mustafa Erdem

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2016 - 201912020 - 202435

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Researcher Of Publications: search.filters.isAuthorOfPublication.613542ab-2038-4fec-9dd7-e922d7216d90

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Now showing 1 - 10 of 36
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    PublicationOpen Access
    A nineteenth-century urban ottoman population micro dataset: data extraction and relational database curation from the 1840s pre-census bursa population registers
    (Nature Portfolio, 2024) Department of History; Department of History; Kabadayı, Mustafa Erdem; Erünal, Efe; College of Social Sciences and Humanities; Graduate School of Social Sciences and Humanities
    In recent decades, the "big microdata revolution" has transformed access to transcribed historical census data for social science research. However, the population records of the Ottoman Empire, spanning Southeastern Europe, Western Asia, and Northern Africa, remained inaccessible to the big microdata ecosystem due to their prolonged unavailability. This publication marks the inaugural release of complete population data for an Ottoman urban center, Bursa, derived from the 1839 population registers. The dataset presents originally non-tabulated register data in a tabular format integrated into a relational Microsoft Access database. Thus, we showcase the extensive and diverse data found in the Ottoman population registers, demonstrating a level of quality and sophistication akin to the censuses conducted worldwide in the nineteenth century. This valuable resource, whose potential has been massively underexploited, is now presented in an accessible format compatible with global microdata repositories. Our aim with this dataset is to enable historical demographic studies for the Ottoman realm and beyond, while also broadening access to the datasets constructed by our large research team.
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    PublicationOpen Access
    Assessing the accuracy of the ESA Worldcover 2021 for the local region of Lalapasa/Edirne, Turkey and recommending possible accuracy improvement strategies
    (Institute of Electrical and Electronics Engineers Inc., 2023) Sertel, Elif; Department of History; Department of History; Kabadayı, Mustafa Erdem; Osgouei, Paria Ettehadi; College of Social Sciences and Humanities
    Global Land Cover (LC) datasets are important geo-information sources for environmental, climate, agriculture, and landscape applications. The ESA WorldCover project (2020 and 2021) provides a global scale land cover map with the predefined 11 generic classes for almost the current state. This study aims to evaluate the accuracy of the ESA LC data for a local region in Lalapasa/Edirne to provide insights into this data for possible local-level applications. Our study revealed that while the grassland, shrubland, and bare classes have inaccuracies that need to be further addressed, tree cover, water bodies, and cropland LC classified were correctly mapped for the studied region. We proposed strategies to improve the accuracy of some classes in the ESA LC map with integrated usage of open geospatial datasets and object-based classification. We encourage merging segments with their best-fitting surrounding segments if they are smaller than a minimum mapping unit of 1 ha. By doing this, we aim to improve the representation of the integrity and compactness of built-up regions and agricultural lands. © 2023 IEEE.
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    Ethno-religious division of labour in urban economie s of the Ottoman Empire in the nineteenth century
    (Berghahn Books, 2020) Güvenç, Murat; Department of History; Department of History; Kabadayı, Mustafa Erdem; Faculty Member; College of Social Sciences and Humanities; 33267
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    Automatic detection of road types from the third military mapping survey of Austria-Hungary historical map series with deep convolutional neural networks
    (IEEE-inst Electrical Electronics Engineers inc, 2021) N/A; N/A; Department of History; Department of History; Can, Yekta Said; Gerrits, Petrus Johannes; Kabadayı, Mustafa Erdem; Resercher; Master Student; Faculty Member; College of Social Sciences and Humanities; Graduate School of Social Sciences and Humanities; College of Social Sciences and Humanities; N/A; N/A; 33267
    With the increased amount of digitized historical documents, information extraction from them gains pace. Historical maps contain valuable information about historical, geographical and economic aspects of an era. Retrieving information from historical maps is more challenging than processing modern maps due to lower image quality, degradation of documents and the massive amount of non-annotated digital map archives. Convolutional Neural Networks (CNN) solved many image processing challenges with great success, but they require a vast amount of annotated data. for historical maps, this means an unprecedented scale of manual data entry and annotation. in this study, we first manually annotated the Third Military Mapping Survey of austria-Hungary historical map series conducted between 1884 and 1918 and made them publicly accessible. We recognized different road types and their pixel-wise positions automatically by using a CNN architecture and achieved promising results.
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    Reading and mapping mid-nineteenth century Ottoman tax registers: an early attempt toward building a digital research infrastructure for Ottoman economic and social history
    (Peter Lang AG, 2020) Güvenç, Murat; Department of History; Department of History; Kabadayı, Mustafa Erdem; Faculty Member; College of Social Sciences and Humanities; 33267
    This proposed paper would use advanced methods of cluster analysis and multiple correspondence analysis (MCA). It will present some outcomes of a three-year research project An Introduction to the Occupational History of Turkey via New Methods and New Approaches (1840-1940), funded by the Scientific and Technological Research Council of Turkey (TUBITAK) (Nr. 112K271), which will be completed in September 2015. Kabadayi is the principal investigator and Güvenç is the advisor of this project. Especially in recent years, MCA has gained popularity among social scientists, but to the best of our knowledge, it has not yet been used in economic or social history in the international literature in English. We will present the preliminary results of our application of MCA to the 1845 Ottoman tax registers (temettuat) focusing on Bursa. For this city, we have been able to locate 126 of the 145 neighbourhoods listed in the records. 7,914 individuals were recorded. 5,662 identified as household heads and 2,252 not. An important aspect of this work involves identifying patterns connected to ethnoreligious affiliations.
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    Epilogue
    (Berghahn Books, 2020) Papastefanaki, Leda; Department of History; Department of History; Kabadayı, Mustafa Erdem; Faculty Member; College of Social Sciences and Humanities; 33267
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    Introduction and historiographical essay: Greek and Turkish economic and social history, and labour history
    (Berghahn Books, 2020) Papastefanaki, Leda; Department of History; Department of History; Kabadayı, Mustafa Erdem; Faculty Member; College of Social Sciences and Humanities; 33267
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    Text detection and recognition by using CNNs in the Austro-Hungarian historical military mapping survey
    (Association for Computing Machinery, 2021) Department of History; N/A; Department of History; Kabadayı, Mustafa Erdem; Can, Yekta Said; Faculty Member; Researcher; College of Social Sciences and Humanities; College of Social Sciences and Humanities; 33267; N/A
    Historical maps include precious data about historical, geographical and economic perspectives of a period. However, several unique challenges and opportunities accompany historical maps compared to modern maps, such as low-quality images, degraded manuscripts and the huge quantity of non-annotated digital map collections. In the recent decade, Convolutional Neural Networks (CNNs) are applied to solve various image processing problems, but they need enormous annotated data to have accurate results. In this work, we annotated text regions of the Third Military Mapping Survey of Austria-Hungary historical map series conducted between 1884 and 1918 manually and made them accessible for researchers. Then, we detected the pixel-wise positions of text regions by employing the deep neural network architecture and recognized them with encouraging error rates.
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    CNN-based page segmentation and object classification for counting population in ottoman archival documentation
    (Multidisciplinary Digital Publishing Institute (MPDI), 2020) Department of History; N/A; Department of History; Kabadayı, Mustafa Erdem; Can, Yekta Said; Faculty Member; Researcher; College of Social Sciences and Humanities; College of Social Sciences and Humanities; 33267; N/A
    Historical document analysis systems gain importance with the increasing efforts in the digitalization of archives. Page segmentation and layout analysis are crucial steps for such systems. Errors in these steps will affect the outcome of handwritten text recognition and Optical Character Recognition (OCR) methods, which increase the importance of the page segmentation and layout analysis. Degradation of documents, digitization errors, and varying layout styles are the issues that complicate the segmentation of historical documents. The properties of Arabic scripts such as connected letters, ligatures, diacritics, and different writing styles make it even more challenging to process Arabic script historical documents. In this study, we developed an automatic system for counting registered individuals and assigning them to populated places by using a CNN-based architecture. To evaluate the performance of our system, we created a labeled dataset of registers obtained from the first wave of population registers of the Ottoman Empire held between the 1840s and 1860s. We achieved promising results for classifying different types of objects and counting the individuals and assigning them to populated places.
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    Developing an automatic layout analysis system for Ottoman population registers
    (IEEE, 2020) Can, Yekta Said; Kabadayı, Mustafa Erdem; Researcher; Faculty Member; College of Social Sciences and Humanities; N/A; 33267
    For extracting information from the historical documents, digitization efforts have increased dramatically in the recent decades. Accurate layout analysis will help researchers for developing more robust HTR and OCR techniques which will extract meaningful information from these documents. Variable layouts, low quality and distorted images of historical documents create different problems to deal with when compared to modern document processing. Arabic script features have even more problems for these automatic processing systems. In this study, we have developed a tool for automatically analyzing the layouts of the first Ottoman population registers which are written in Arabic script form. We built a dataset for testing the performance of our system which are chosen from the first population records of the Ottoman Empire between the 1840s and 1860s. We successfully classified two different object types in those documents.