Researcher:
Osgouei, Paria Ettehadi

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Paria Ettehadi

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Osgouei

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Osgouei, Paria Ettehadi

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2022 - 20233

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Researcher Of Publications: search.filters.isAuthorOfPublication.36dfc6b9-ba1d-4163-80a4-eb31090335f5

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    PublicationOpen Access
    Assessing the accuracy of the ESA Worldcover 2021 for the local region of Lalapasa/Edirne, Turkey and recommending possible accuracy improvement strategies
    (Institute of Electrical and Electronics Engineers Inc., 2023) Sertel, Elif; Department of History; Department of History; Kabadayı, Mustafa Erdem; Osgouei, Paria Ettehadi; College of Social Sciences and Humanities
    Global Land Cover (LC) datasets are important geo-information sources for environmental, climate, agriculture, and landscape applications. The ESA WorldCover project (2020 and 2021) provides a global scale land cover map with the predefined 11 generic classes for almost the current state. This study aims to evaluate the accuracy of the ESA LC data for a local region in Lalapasa/Edirne to provide insights into this data for possible local-level applications. Our study revealed that while the grassland, shrubland, and bare classes have inaccuracies that need to be further addressed, tree cover, water bodies, and cropland LC classified were correctly mapped for the studied region. We proposed strategies to improve the accuracy of some classes in the ESA LC map with integrated usage of open geospatial datasets and object-based classification. We encourage merging segments with their best-fitting surrounding segments if they are smaller than a minimum mapping unit of 1 ha. By doing this, we aim to improve the representation of the integrity and compactness of built-up regions and agricultural lands. © 2023 IEEE.
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    PublicationOpen Access
    Land use and land cover mapping using deep learning-based segmentation approaches and VHR Worldview-3 images
    (Multidisciplinary Digital Publishing Institute (MDPI), 2022) Department of History; Department of History; Sertel, Elif; Ekim, Burak; Osgouei, Paria Ettehadi; Kabadayı, Mustafa Erdem; Researcher; Researcher; Faculty Member; College of Social Sciences and Humanities; 112090; N/A; N/A; 33267
    Deep learning-based segmentation of very high-resolution (VHR) satellite images is a significant task providing valuable information for various geospatial applications, specifically for land use/land cover (LULC) mapping. The segmentation task becomes more challenging with the increasing number and complexity of LULC classes. In this research, we generated a new benchmark dataset from VHR Worldview-3 images for twelve distinct LULC classes of two different geographical locations. We evaluated the performance of different segmentation architectures and encoders to find the best design to create highly accurate LULC maps. Our results showed that the DeepLabv3+ architecture with an ResNeXt50 encoder achieved the best performance for different metric values with an IoU of 89.46%, an F-1 score of 94.35%, a precision of 94.25%, and a recall of 94.49%. This design could be used by other researchers for LULC mapping of similar classes from different satellite images or for different geographical regions. Moreover, our benchmark dataset can be used as a reference for implementing new segmentation models via supervised, semi- or weakly-supervised deep learning models. In addition, our model results can be used for transfer learning and generalizability of different methodologies.
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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; Department of History; Kabadayı, Mustafa Erdem; Osgouei, Paria Ettehadi; Sertel, Elif; Faculty Member; Researcher; Researcher; 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.