Hesaplamalı Arşiv Bilimi ile Görüntü Sınıflandırma ve Dijital Provenans

Mehmet Oytun Cibaroğlu

doi:10.33721/by.1772464

TR EN

Hesaplamalı Arşiv Bilimi ile Görüntü Sınıflandırma ve Dijital Provenans

Öz

Arşiv bilimi; büyük veri ve yapay zekâ odaklı oldukça geniş çaplı bir dönüşümün içinde yer almaya başlamıştır. Bu doğrultuda hesaplamalı arşiv bilimi adı verilen yeni bir paradigma ortaya çıkmıştır. Bu alan, bilgisayar bilimi ile arşiv biliminin kolektif bilgisini birleştirerek dijital arşiv materyallerinin analizi, uzun süreli korunması ve erişimi için hesaplamalı yöntemler sunmaktadır. Bu çalışmada, hesaplamalı arşiv biliminin temel yaklaşımları benimsenerek; yapılandırılmamış (uydu tarafından çekilen yer görüntüleri) dijital arşiv veri seti üzerinde transfer öğrenmesi bazlı sınıflandırma görevi gerçekleştirilmiş, hesaplamalı süreçlere ait provenans verisi oluşturulmuş ve tüm çıktılar, Archivematica programı aracılığıyla uzun süreli dijital korumaya alınmıştır. Transfer öğrenmesi kapsamında ise EfficientNet mimarisinin (B0-B3, V2B0-V2B3) 8 farklı varyantı kullanılmış, öncelikle ağırlıksız olarak eğitilen temel modellerin, sonrasında Keras Tuner RandomSearch tekniği ile en iyi hiperparametre aramaları yapılarak başarımları geliştirilmeye çalışılmıştır. Optimize edilen modellerde B3, V2B2 ve V2B3 varyantları %97,20 doğruluk ve sırasıyla 0,9713, 0,9716 ve 0,9718 F1 skorları ile en iyi sonucu vermiştir. Tüm hesaplamalı süreçler, JSON formatında provenans verisi olarak yapılandırılmış; analizlere dair işlem bilgileri, kullanılan yöntemler, hiperparametreler ve çıktılar zaman damgalı şekilde kayıt altına alınmıştır. Örnek olarak seçilen EfficientNetB3 modelinin analiz süreçleri, BagIt kütüphanesi ile paketlenmiş, orijinal veri setiyle birlikte Archivematica sistemine yüklenmiş ve burada SIP, AIP ve DIP paketlerine dönüştürülerek uzun süreli koruma sağlanmıştır. Çalışmanın özgünlüğü, sadece hesaplamalı modeller oluşturulması ile sınırlı kalmamakta olup aynı zamanda bu süreçlerin arşivsel bağlamda belgelenmesi, versiyonlanması ve korunması hedeflerini de içermesi ile diğer çalışmalardan ayrışmaktadır.

Anahtar Kelimeler

Hesaplamalı Arşiv Bilimi, Derin Öğrenme, Transfer Öğrenmesi

Image Classification and Digital Provenance with Computational Archival Science

Öz

Archival science has begun to take part in a large-scale transformation driven by big data and artificial intelligence. In this regard, a new paradigm called computational archival science has emerged. This field combines the collective knowledge of computer science and archival science to provide computational methods for the analysis, long-term preservation, and accessibility of digital archival materials. In this study, by adopting the fundamental approaches of computational archival science, a transfer learning–based classification task was carried out on an unstructured digital archival dataset (satellite-captured land imagery). Provenance data related to the computational processes were generated, and all outputs were preserved for the long term through the Archivematica platform. Within the scope of transfer learning, eight variants of the EfficientNet architecture (B0-B3, V2B0-V2B3) were employed. Initially, the base models were trained without pre-trained weights, after which Keras Tuner’s RandomSearch technique was applied to optimize hyperparameters and improve performance. Among the optimized models, the B3, V2B2, and V2B3 variants yielded the best results, achieving an accuracy of 97.20% and F1 scores of 0,9713, 0,9716, and 0,9718, respectively. All computational processes were structured as provenance data in JSON format; details of operations, methods used, hyperparameters, and outputs were recorded with time stamps. As a case example, the analysis workflow of the EfficientNetB3 model was packaged with the BagIt library, together with the original and derivative datasets, uploaded into the Archivematica system, and transformed into SIP, AIP, and DIP packages to ensure long-term digital preservation. The originality of the study is not limited to creating computational models only, but also distinguishes it from other studies by including the goals of recording, versioning and preserving these processes in an archival context.

Anahtar Kelimeler

Computational Archival Science, Deep Learning, Transfer Learning

Kaynakça

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Ayrıntılar

Birincil Dil

Türkçe

Konular

Arşiv, Dijital Küratörlük ve Koruma, Kayıt Tutma Bilişimi, Arşivcilik, Depolama ve İlgili Çalışmalar

Bölüm

Araştırma Makalesi

Yazarlar

Mehmet Oytun Cibaroğlu ^*
0000-0002-5763-0770
Türkiye

Yayımlanma Tarihi

31 Aralık 2025

Gönderilme Tarihi

26 Ağustos 2025

Kabul Tarihi

6 Ekim 2025

Yayımlandığı Sayı

Yıl 2025 Cilt: 8 Sayı: 2

DOI

https://doi.org/10.33721/by.1772464

IZ

https://izlik.org/JA53MK64ER

APA

Cibaroğlu, M. O. (2025). Hesaplamalı Arşiv Bilimi ile Görüntü Sınıflandırma ve Dijital Provenans. Bilgi Yönetimi, 8(2), 244-283. https://doi.org/10.33721/by.1772464

AMA

1.Cibaroğlu MO. Hesaplamalı Arşiv Bilimi ile Görüntü Sınıflandırma ve Dijital Provenans. BY. 2025;8(2):244-283. doi:10.33721/by.1772464

Chicago

Cibaroğlu, Mehmet Oytun. 2025. “Hesaplamalı Arşiv Bilimi ile Görüntü Sınıflandırma ve Dijital Provenans”. Bilgi Yönetimi 8 (2): 244-83. https://doi.org/10.33721/by.1772464.

EndNote

Cibaroğlu MO (01 Aralık 2025) Hesaplamalı Arşiv Bilimi ile Görüntü Sınıflandırma ve Dijital Provenans. Bilgi Yönetimi 8 2 244–283.

IEEE

[1]M. O. Cibaroğlu, “Hesaplamalı Arşiv Bilimi ile Görüntü Sınıflandırma ve Dijital Provenans”, BY, c. 8, sy 2, ss. 244–283, Ara. 2025, doi: 10.33721/by.1772464.

ISNAD

Cibaroğlu, Mehmet Oytun. “Hesaplamalı Arşiv Bilimi ile Görüntü Sınıflandırma ve Dijital Provenans”. Bilgi Yönetimi 8/2 (01 Aralık 2025): 244-283. https://doi.org/10.33721/by.1772464.

JAMA

1.Cibaroğlu MO. Hesaplamalı Arşiv Bilimi ile Görüntü Sınıflandırma ve Dijital Provenans. BY. 2025;8:244–283.

MLA

Cibaroğlu, Mehmet Oytun. “Hesaplamalı Arşiv Bilimi ile Görüntü Sınıflandırma ve Dijital Provenans”. Bilgi Yönetimi, c. 8, sy 2, Aralık 2025, ss. 244-83, doi:10.33721/by.1772464.

Vancouver

1.Mehmet Oytun Cibaroğlu. Hesaplamalı Arşiv Bilimi ile Görüntü Sınıflandırma ve Dijital Provenans. BY. 01 Aralık 2025;8(2):244-83. doi:10.33721/by.1772464

Hesaplamalı Arşiv Bilimi ile Görüntü Sınıflandırma ve Dijital Provenans

Öz

Anahtar Kelimeler

Image Classification and Digital Provenance with Computational Archival Science

Öz

Anahtar Kelimeler

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Yayımlanma Tarihi

Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

DOI

IZ

Kaynak Göster