A case study the necropole area of Antandros ancient city (Turkey) by magnetic prospection

The aim of this study is to investigate by magnetic prospection ancient structures buried in the necropolis of the historically and archaeologically important city of Antandros in Altınoluk, Balıkesir (Turkey). Antandros is a city in Troas, located on the hillside of İda Mountain. Archaeological excavations started in a part of the city in 2001 have brought to light Late Roman and Hellenistic remains. Figure 1: Study location on the archaeological map of Turkey. The present magnetic study fo..

Paleovegetation Researches Based on Fossil Pollen Analysis in Akgöl (Sakarya): Preliminary Results

Fosil polen analizleri göl çökelleri, turbalıklar, akarsu ve deniz sedimanları, buzullar, linyitler ve taş kömürleri gibi çeşitli ortamlardan elde edilen polenlerin_x000D_ araştırılmasını kapsamaktadır. Kuvaterner dönemine ait palinolojik çalışmaların önemli veri kaynaklarından biri de göllerdir. Araştırma alanı olarak seçilen_x000D_ Akgöl, Sakarya ilinde, Ferizli ilçesinin Gölkent mahallesinde bulunmaktadır. Gölün yüzölçümü 3,5 km2_x000D_ ve maksimum derinliği 8 m’dir. Bu çalışmanın amacı:_x000D_ gölün dip sedimanlarında fosil polen analizleri yaparak gölün çevresinde son 1000 yılda meydana gelen vejetasyon değişimlerini ortaya çıkarmaktır._x000D_ Akgöl’den karot alımında İTÜ EMCOL Araştırma Uygulama Merkezi’nin 4x4 m. platformlu piston karotiyeri kullanılmıştır. İstanbul Üniversitesi-Cerrahpaşa,_x000D_ Orman Fakültesi Orman Botaniği Anabilim Dalında bulunan Palinoloji Laboratuvarı’na getirilen karot üzerinde her 5 cm’de bir 2 cm3_x000D_ lük sediman örnekleri_x000D_ alınmıştır. Bu örneklerde “klasik yönteme” göre fosil polen analizi yapılmıştır. Hazırlanan polen preparatlarında her bir bitki taksonu için polen yüzdesi_x000D_ değerleri hesaplanmış, odunsu ve otsu taksonlara ait yüzde grafikleri TILIA adlı programda çizilmiştir. Polen diyagramından elde edilen ilk bulgulara göre;_x000D_ Akgöl ve çevresinde son 1000 yılda yaprak döken orman vejetasyonu hâkimdir. Bu doğal orman varlığının içine son yıllarda Gymnospermae taksonlarından_x000D_ sahil çamları da dikim yoluyla getirilmiştir.Fossil pollen analyzes include research of pollen grains from various environments such as lake sediments, peatland, river and marine sediments, glaciers,_x000D_ lignite and coal. Lakes are one of the important data sources for Quaternary palynological studies. Akgöl, which is selected as a research area, is located in Gölkent district of the Ferizli township in the provincial city of Sakarya. Its surface area is 3.5 km2_x000D_ and its maximum depth is 8 meters. The aim of this study_x000D_ was to investigate vegetation changes around Akgöl in the last 1000 years using fossil pollen analysis in the bottom sediments of the lake. The Piston corer_x000D_ of ITU EMCOL Research Centre was used for recovering sediment cores from Akgöl. Sediment samples of 2 cm3_x000D_ were collected every 5 centimeters on one_x000D_ of the cores at the Palynology Laboratory of IUC. Fossil pollen analysis was performed according to the “classical method”. The pollen percentage values_x000D_ were calculated for each plant taxa and relative abundance graphs were plotted in the TILIA program. According to preliminary results of the pollen_x000D_ diagram; deciduous forest vegetation has been predominant in the last 1000 years around Akgöl. Maritime pine was introduced into this natural forest by_x000D_ plantation in the recent year

GPR Data Processing and Interpretation Based on Artificial Intelligence Approaches : Future Perspectives for Archaeological Prospection

Ground penetrating radar (GPR) is a well-established technique used in archaeological prospection and it requires a number of specialized routines for signal and image processing to enhance the data acquired and lead towards a better interpretation of them. Computer-aided techniques have advanced the interpretation of GPR data, dealing with a wide range of operations aiming towards locating, imaging, and diagnosis/interpretation. This article will discuss the novel and recent applications of machine learning (ML) and deep learning (DL) techniques, under the artificial intelligence umbrella, for processing GPR measurements within archaeological contexts, and their potential, limitations, and possible future prospects