Research Outputs

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Department: search.filters.department.Department of HistorySponsors: search.filters.sponsors.EU

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Now showing 1 - 10 of 23
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    PublicationOpen Access
    1899 yılı Osmanlı İmparatorluğu için jeomekansal ve çok modlu bir ulaşım ağı oluşturma denemesi
    (Koç University Research Center for Anatolian Civilizations (ANAMED) / Koç Üniversitesi Anadolu Medeniyetleri Araştırma Merkezi (ANAMED), 2020) Gerrits, Piet; Department of History; Kabadayı, Mustafa Erdem; Özkan, Osman; Koçak, Turgay; Faculty Member; Teaching Faculty; Department of History; College of Social Sciences and Humanities; 33267; N/A; N/A
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    PublicationOpen Access
    A preliminary attempt to construct a geospatial, multimodal Ottoman transport network for 1899
    (Koç University Press (KUP) / Koç Üniversitesi Yayınları (KÜY), 2021) Gerrits, Piet; Department of History; Kabadayı, Mustafa Erdem; Özkan, Osman; Koçak, Turgay; Faculty Member; Teaching Faculty; Department of History; College of Social Sciences and Humanities; 33267; N/A; N/A
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    PublicationOpen Access
    Agricultural land abandonment in Bulgaria: a long-term remote sensing perspective, 1950–1980
    (Multidisciplinary Digital Publishing Institute (MDPI), 2022) Department of History; Kabadayı, Mustafa Erdem; Osgouei, Paria Ettehadi; Sertel, Elif; Faculty Member; Researcher; Researcher; Department of History; College of Social Sciences and Humanities; 33267; N/A; N/A
    Agricultural land abandonment is a globally significant threat to the sustenance of economic, ecological, and social balance. Although the driving forces behind it can be multifold and versatile, rural depopulation and urbanization are significant contributors to agricultural land abandonment. In our chosen case study, focusing on two locations, Ruen and Stamboliyski, within the Plovdiv region of Bulgaria, we use aerial photographs and satellite imagery dating from the 1950s until 1980, in connection with official population census data, to assess the magnitude of agricultural abandonment for the first time from a remote sensing perspective. We use multi-modal data obtained from historical aerial and satellite images to accurately identify Land Use Land Cover changes. We suggest using the rubber sheeting method for the geometric correction of multi-modal data obtained from aerial photos and Key Hole missions. Our approach helps with precise sub-pixel alignment of related datasets. We implemented an iterative object-based classification approach to accurately map LULC distribution and quantify spatio-temporal changes from historical panchromatic images, which could be applied to similar images of different geographical regions.
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    PublicationOpen Access
    Automatic CNN-based Arabic numeral spotting and handwritten digit recognition by using deep transfer learning in Ottoman population registers
    (Multidisciplinary Digital Publishing Institute (MDPI), 2020) Department of History; Kabadayı, Mustafa Erdem; Can, Yekta Said; Faculty Member; Department of History; College of Social Sciences and Humanities; 33267; N/A
    Historical manuscripts and archival documentation are handwritten texts which are the backbone sources for historical inquiry. Recent developments in the digital humanities field and the need for extracting information from the historical documents have fastened the digitization processes. Cutting edge machine learning methods are applied to extract meaning from these documents. Page segmentation (layout analysis), keyword, number and symbol spotting, handwritten text recognition algorithms are tested on historical documents. For most of the languages, these techniques are widely studied and high performance techniques are developed. However, the properties of Arabic scripts (i.e., diacritics, varying script styles, diacritics, and ligatures) create additional problems for these algorithms and, therefore, the number of research is limited. In this research, we first automatically spotted the Arabic numerals from the very first series of population registers of the Ottoman Empire conducted in the mid-nineteenth century and recognized these numbers. They are important because they held information about the number of households, registered individuals and ages of individuals. We applied a red color filter to separate numerals from the document by taking advantage of the structure of the studied registers (numerals are written in red). We first used a CNN-based segmentation method for spotting these numerals. In the second part, we annotated a local Arabic handwritten digit dataset from the spotted numerals by selecting uni-digit ones and tested the Deep Transfer Learning method from large open Arabic handwritten digit datasets for digit recognition. We achieved promising results for recognizing digits in these historical documents.
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    PublicationOpen Access
    Automatic detection of road types from the third military mapping survey of Austria-Hungary historical map series with deep convolutional neural networks
    (Institute of Electrical and Electronics Engineers (IEEE), 2021) Department of History; Kabadayı, Mustafa Erdem; Can, Yekta Said; Gerrits, Petrus Johannes; Faculty Member; Department of History; College of Social Sciences and Humanities; 33267; N/A; N/A
    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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    PublicationMetadata only
    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; Can, Yekta Said; Gerrits, Petrus Johannes; Kabadayı, Mustafa Erdem; Resercher; Master Student; Faculty Member; Department of History; 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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    PublicationOpen Access
    Automatic estimation of age distributions from the first Ottoman Empire population register series by using deep learning
    (Multidisciplinary Digital Publishing Institute (MDPI), 2021) Department of History; Kabadayı, Mustafa Erdem; Can, Yekta Said; Faculty Member; Department of History; College of Social Sciences and Humanities; 33267; N/A
    Recently, an increasing number of studies have applied deep learning algorithms for extracting information from handwritten historical documents. In order to accomplish that, documents must be divided into smaller parts. Page and line segmentation are vital stages in the Handwritten Text Recognition systems; it directly affects the character segmentation stage, which in turn deter-mines the recognition success. In this study, we first applied deep learning-based layout analysis techniques to detect individuals in the first Ottoman population register series collected between the 1840s and the 1860s. Then, we employed horizontal projection profile-based line segmentation to the demographic information of these detected individuals in these registers. We further trained a CNN model to recognize automatically detected ages of individuals and estimated age distributions of people from these historical documents. Extracting age information from these historical registers is significant because it has enormous potential to revolutionize historical demography of around 20 successor states of the Ottoman Empire or countries of today. We achieved approximately 60% digit accuracy for recognizing the numbers in these registers and estimated the age distribution with Root Mean Square Error 23.61.
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    PublicationOpen Access
    Automatic road extraction from historical maps using deep learning techniques: a regional case study of Turkey in a German World War II map
    (Multidisciplinary Digital Publishing Institute (MDPI), 2021) Sertel, Elif; Department of History; Kabadayı, Mustafa Erdem; Ekim, Burak; Faculty Member; Department of History; College of Social Sciences and Humanities; 33267; N/A
    Scanned historical maps are available from different sources in various scales and contents. Automatic geographical feature extraction from these historical maps is an essential task to derive valuable spatial information on the characteristics and distribution of transportation infrastructures and settlements and to conduct quantitative and geometrical analysis. In this research, we used the Deutsche Heereskarte 1:200,000 Türkei (DHK 200 Turkey) maps as the base geoinformation source to construct the past transportation networks using the deep learning approach. Five different road types were digitized and labeled to be used as inputs for the proposed deep learning-based segmentation approach. We adapted U-Net++ and ResneXt50_32×4d architectures to produce multi-class segmentation masks and perform feature extraction to determine various road types accurately. We achieved remarkable results, with 98.73% overall accuracy, 41.99% intersection of union, and 46.61% F1 score values. The proposed method can be implemented in DHK maps of different countries to automatically extract different road types and used for transfer learning of different historical maps.
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    PublicationOpen Access
    Bridging the gap between pre-census and census-era historical data: devising a geo-sampling model to analyse agricultural production in the long run for Southeast Europe, 1840–1897
    (Edinburgh University Press, 2020) Gerrits, Piet; Department of History; Kabadayı, Mustafa Erdem; Boykov, Grigor; Faculty Member; Department of History; College of Social Sciences and Humanities; 33267; N/A
    This research introduces a novel geo-spatial sampling model to overcome a major difficulty in historical economic geography of Bulgarian lands during a crucial period: immediately before and after the de facto independence of the territory from the Ottoman Empire in the second half of the nineteenth century. At its core it seeks to investigate the research question how the Bulgarian independence affected agricultural production in two regions (centered around the cities of Plovdiv and Ruse) of today's Bulgaria, for which there are conflicting yet empirically unsubstantiated claims concerning the economic impact of the political independence. Using our be-spoke geo-sampling strategy we believe, we have sampled regionally representative commensurable agricultural data from the 1840s Ottoman archival documentation, in accord with agricultural censuses conducted by the nascent nation state of Bulgaria in the 1890s.
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    PublicationOpen Access
    CNN-based page segmentation and object classification for counting population in Ottoman archival documentation
    (Multidisciplinary Digital Publishing Institute (MDPI), 2020) Department of History; Kabadayı, Mustafa Erdem; Can, Yekta Said; Faculty Member; Department of History; 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.