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Murat Arslan - Reyhan Çakir - İsra Akyazi
                                Nida Kumbasar - Ahmet Doğan - Emre Yavuz

                The creation of an inventory of fundamental land use items such as artificial
              surfaces, agricultural areas, forest areas, water and wetlands with locational
              data will provide an important source of data for a variety of purposes such as
              conducting work towards monitor land damage, making product anticipations,
              calculating carbon emission as well as creating landslide inventory maps and
              flood and overflow modeling work.
                As data taken from the institutions of our country are used for the maps
              produced within the project, the currency and accuracy of these data have a
              direct impact on the accuracy of the classification provided by the UASIS land
              cover classification algorithms.   For this reason, as  the periods of update for
              the data generated by the institutions and data accuracy increase, UASIS land
              cover classification maps will be produced with higher accuracy.
                In addition, the Eastern Black Sea region is problematic in terms of obtaining
              usable satellite images due to its cloudiness.  Therefore, classification work in
              the Eastern Black Sea region exhibit lower accuracy compared to other regions
              It is thought that a solution to the issue of cloudiness could be avoided with
              the integration of Synthetic Aperture Radar (SAR). In addition to SAR data, it is
              considered important to include orthophotos and images collected by drones
              from the field.
                UASIS has a dynamic structure as it  is a sysem that can be  updated and
              improved, In the future, capacity-building efforts are planned to improve
              the accuracy of the semi-automatically detected land cover classes and to
              implement auxiliary data automation.

                Acknowledgments


                We would like to thank all institutions which did not hesitate to help us by
              sharing public data to be used as part of the project.   We would also like to
              thank the project team of the Informatics and Information Security Advanced
              Technologies Research Center of the Scientific and Technological Research
              Council of Türkiye, who are in cooperation with the General Directorate of
              Combating Desertification and Erosion within the scope of the UASIS project.

















              128 Journal of Environment, Urban and Climate
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