Research Project: Industrialisation and Urban Growth from the mid-nineteenth century Ottoman Empire to Contemporary Turkey in a Comparative Perspective, 1850-2000
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Contributors
Funders
ID
EC.00091
Authors
Kabadayı, Mustafa Erdem
Faculty Member
Publications
Land cover feature extraction from Corona spy satellite images during the Cold War - 1968
(Institute of Electrical and Electronics Engineers (IEEE), 2021) Kabadayı, Mustafa Erdem; Stratoulias, Dimitris; Department of History; Yes; College of Social Sciences and Humanities
For 25 years the Corona reconnaissance satellite mission has been declassified, making available an unprecedented historic archive of very high spatial resolution panchromatic images acquired during the cold war era. During the same time, Bulgaria has observed a decreasing and ageing population and a slow agricultural productivity. In this study, we attempt to map the settlements around the city of Plovdiv, Bulgaria based on textural analysis of a Corona image acquired in 1968. We compare the results with information derived from recent Sentinel-2B and Landsat-8 images. We discuss on the discerning capability of the Corona image in mapping settlements and the potential usability of textural analysis in the context of land use and land cover mapping of historical images. Overall, we present the textural analysis of an image from one of the first satellite missions and report on the potential for feature extraction from such primitive satellite products.
Deep learning-based road extraction from historical maps
(IEEE-Inst Electrical Electronics Engineers Inc, 2022) Avcı, Cengiz; Kabadayı, Mustafa Erdem; N/A; Department of History; Yes; College of Social Sciences and Humanities
Automatic road extraction from historical maps is an important task to understand past transportation conditions and conduct spatiotemporal analysis revealing information about historical events and human activities over the years. This research aimed to propose the ideal architecture, encoder, and hyperparameter settings for the historical road extraction task. We used a dataset including 7076 patches with the size of 256 x 256 pixels generated from scanned historical Deutsche Heereskarte 1:200 000 Turkei (DHK 200 Turkey) maps and their corresponding digitized ground truth masks for five different roads types. We first tested the widely used Unet++ and Deeplabv3 architectures. We also evaluated the contribution of attention models by implementing Unet++ with the concurrent spatial and channel-squeeze and excitation block and multiscale attention net. We achieved the best results with split-attention network (Timm-resnest200e) encoder and Unet++ architecture, with 98.99% overall accuracy, 41.99% intersection of union, 51.41% precision, 69.7% recall, and 57.72% F1 score values. Our output weights could be directly used for the inference of other DHK maps and transfer learning for similar or different historical maps. The proposed architecture could also be implemented in different road extraction studies.
Text detection and recognition by using CNNs in the Austro-Hungarian historical military mapping survey
(Association for Computing Machinery, 2021) Can, Yekta Said; Kabadayı, Mustafa Erdem; Department of History; Yes; College of Social Sciences and Humanities
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.
CNN-based page segmentation and object classification for counting population in ottoman archival documentation
(MDPI Multidisciplinary Digital Publishing Institute, 2020) Can, Yekta Said; Kabadayı, Mustafa Erdem; Department of History; Yes; College of Social Sciences and Humanities
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.
Examining age structure and estimating mortality rates in Ottoman Bursa using Mid-Nineteenth-Century population registers
(Taylor _ Francis, 2021) Erünal, Efe; Department of History; Graduate School of Social Sciences and Humanities; No; College of Social Sciences and Humanities; GRADUATE SCHOOL OF SOCIAL SCIENCES AND HUMANITIES
This study aims to document the age structure and mortality by age in the Ottoman city of Bursa that served as a politically and commercially significant urban center over centuries. It uses a set of hitherto unexamined Ottoman population registers kept in 1839 and updated until 1842 that provide detailed self-reported data on all male inhabitants regardless of age, including deaths, births, and migration. The study tests the quality of age and mortality data in conjunction with the Coale and Demeny regional model life tables and compares the results to historical demographic studies conducted for European regions. The results point to a demographic structure marked by high birth and death rates and prove promising for extending back the study of Ottoman demographic transition and establishing historical comparison points with the global experience.
