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Deep Learning in Archaeological Remote Sensing: Automated Qanat Detection in the Kurdistan Region of Iraq

Soroush, Mehrtash, Khazraee, Ur
2020 Remote Sensing  
Our case study is the qanat systems of the Erbil Plain in the Kurdistan Region of Iraq.  ...  We have tested the application of deep convolutional neural networks (CNNs) for automated remote sensing detection of archaeological features.  ...  EPAS would like to express its gratitude for the help and encouragement of the following institutions and individuals from the Kurdistan Regional Government, Iraq: General Directorate of Antiquities, in  ... 
doi:10.3390/rs12030500 fatcat:l2rkex4lnva37aq3j64gxdirki

Semantic Segmentation of Airborne LiDAR Data in Maya Archaeology

Marek Bundzel, Miroslav Jaščur, Milan Kováč, Tibor Lieskovský, Peter Sinčák, Tomáš Tkáčik
2020 Remote Sensing  
We used a data set from Pacunam LiDAR Initiative survey of lowland Maya region in Guatemala.  ...  Furthermore, we discuss the problems of re-purposing the archaeological style labeling for production of valid machine learning training sets.  ...  Deep learning in archaeological remote sensing: Automated qanat detection in the Kurdistan region of Iraq. Remote Sens.2020, 12, 500.  ... 
doi:10.3390/rs12223685 fatcat:h3nqbydxjbcwzp3cma3wc6ytou

Green infrastructure planning in developing countries; developing green concept in Kurdistan region-Iraq [article]

Sawsan Mohamed, Universität Stuttgart, Universität Stuttgart
2017
In the course of this study, the focus is on a certain function related to climatic, engineering and ecological benefits that will be used as the basic principal in developing the Green Infrastructure  ...  In the course of the thesis framework, Climate Change adaptation is limited to Green Infrastructure application as an integral and important practice of the development process.  ...  Acknowledgements I wish to express my gratitude to Universität Stuttgart for giving me this opportunity to learn academically within international context, to Ministry of Environment (Umweltministerium  ... 
doi:10.18419/opus-9164 fatcat:vtr6jnknmbfcdj2fwmc5i3ukqa