Mehmet Özgür Çelik

In the originally published article, incorrect affiliation and ORCID information were provided for Hossein Azadi due to an unintentional error in the author information submitted with the manuscript.

Housing valuation is a concrete reflection of socio-economic inequalities in urban space. Particularly in densely populated, spatially fragmented large cities like Istanbul, the current official mass appraisal system used for property taxation fails to reflect market reality. This situation results in revenue losses in property taxes and spatial injustices. This study develops a Deep Neural Network (DNN)-based model that integrates spatially derived variables from Geographic Information Systems (GIS) and incorporates 24 objective variables related to location, structure, and access in Istanbul. The model, trained on3,757 samples created using open-source big data, estimated housing values with high accuracy (R² = 0.979). The findings show that spatial differences in housing values are strongly related to urban variables such as accessibility and proximity to infrastructure. This approach not only produces housing value estimates but also provides a theoretical and methodological framework for spatial analyses of how value is produced in urban space. The study has the potential to support the development of a fair, transparent, and updatable mass appraisal system, especially for developing cities.

The escalating global population, industrialization, and climate change are increasing pressure on agricultural lands. In this context, sustainable agricultural land management is critically important, particularly for high-value crops such as citrus, which plays critical role in economic and food security. Accurate detection and enumeration of citrus trees are essential for ensuring the sustainability and effective monitoring of citrus cultivation. This study employs deep learning methods for object detection of citrus trees in the Tarsus district of Mersin, comparing the performance of Mask R-CNN, YOLOv8, and YOLO11 models using low-resolution satellite imagery. Additionally, the impact of super-resolution (SR) techniques on model accuracy is examined. Results demonstrate that integrating SR techniques significantly improves object detection accuracy, with the YOLO11 model achieving the highest performance. In the raw dataset, the YOLO11 model obtained mAP50 (45.39%) and mAP50-95 (22.15%) values; in the SR applied dataset, these metrics were 85.93% and 67.66%, respectively. This research underscores the potential of deep learning-based approaches to enhance citrus tree monitoring, yield estimation, and agricultural management practices, offering actionable insights for sustainable agriculture.

Osmaniye is a province in the Mediterranean Region of Türkiye that stands out with its fertile agricultural lands and suitable climate conditions. Among the agricultural activities that form the basis of the regional economy, peanut production has an important economic and social place. Sustainable agricultural practices aim at the efficient use of agricultural lands and the protection of natural resources. In this context, determining suitable areas for cultivating strategic products such as peanuts is critical for protecting long-term agricultural production and ecosystem balance. In this study, 11 parameters were determined using the remote sensing & geographic information systems (RS&GIS)-based analytical hierarchy process (AHP) method to determine suitable areas for peanut cultivation in Osmaniye province. The receiver operating characteristic (ROC) curve was used to verify the produced model, and the area under the curve (AUC) value was 78.5%. Consequently, the analysis performed was found to be consistent and reliable. The study findings showed that Osmaniye allows peanut cultivation to be carried out sustainably and is strategically important for this species.

Climate change constitutes an escalating global crisis that features significant implications for ecosystems, water resources, agriculture, and industry. Understanding its causes and potential impacts is crucial for formulating effective mitigation and adaptation strategies. This study systematically reviews recent scientific findings and scenario-based climate models to assess both the natural and anthropogenic (human-induced) factors that are driving climate change. It highlights the rising levels of atmospheric carbon, the increasing global temperatures, and the resulting environmental challenges (including extreme weather events, rising sea levels, and biodiversity losses). By drawing on more than 40 regional and global studies, this paper synthesizes results from various GCMs – particularly those that are applied in arid and semi-arid regions (where climate vulnerabilities are most pronounced). The findings revealed disparities in regional vulnerability and highlighted the necessity of selecting accurate GCMs for effective adaptation planning. By synthesizing the existing literature and climate models, this paper aims to establish a comprehensive reference for researchers and policymakers, thus facilitating informed decisionmaking that is geared toward a sustainable future.

This study, conducted in Mersin, a Mediterranean sub-tropical area, examined the potential of avocado and pitaya to thrive under current and future climate conditions. Researchers utilized climate and soil data, initially selecting 14 parameters (mean annual temperature, mean minimum temperature of the coldest month, mean maximum temperature of the warmest month, mean annual precipitation, soil texture, soil depth, land use capability, soil pH, soil organic carbon, soil salinity, land cover, elevation, slope, and groundwater level) for analysis, which were narrowed down to 12 after correlation analysis. The potential distributions were projected using the MaxEnt model for current and future scenarios. Three global climate models—HadGEM3-GC31-LL, MPI-ESM1-2-HR, and GFDL-ESM4—were utilized under the SSP2-4.5 and SSP5-8.5 scenarios. Under SSP2-4.5, an average increase of 1.32%, 1.95%, and 4.02% in the “S1” class is expected. For SSP5-8.5, average gains of 1.33%, 1.58%, and 0.77% are projected. In Pitaya, the “S1” class in SSP2-4.5 is expected to increase by 0.96% compared to the first model and decrease by 7.06% and 5.71% compared to the other models, respectively. Under SSP5-8.5, the changes are determined to be 1.49%, −7.27%, and −7.28%, respectively. Our findings indicate that climate change poses a significant threat to the region; however, the application demonstrates that agricultural activities can remain sustainable despite climate change impacts.

Tüm dünyada kadastro ve tapu işlemlerinin de içinde yer aldığı arazi yönetimi sistemlerine öncülük eden FIG (International Federation of Surveyors), bu alanda ortaya koyduğu çalışmalarla emlak sektörünü topyekûn etkilemektedir. Türkiye hem "Kadastro 2014" ifadeleri hem de AİTM (Arazi İdaresi Temel Modeli) çalışmalarını dikkate alarak kendi tapu ve kadastro sistemini sürekli geliştirmektedir. Ülkemizde TKGM (Tapu ve Kadastro Genel Müdürlüğü) taşınmaz mallara ilişkin akit ve tescil işlemlerini yerine getirmek gibi birçok görev üstlenmiş bir kamu kamu kurumudur. Yasalarla kendisine verilmiş olan görevlerini ülke genelinde 24 Bölge Müdürlüğü, 81 İl Kadastro Müdürlüğü ve her bir ilçede en az bir tane olmak üzere toplam 973 Tapu Müdürlüğü ile yürütmektedir. Taşınmaz alım-satımı ve tapu siciline tescil gerektiren tüm işlemlerde sadece Tapu Müdürlükleri sorumludur. Noterlerin tapu işlemlerinin hiçbirini yapma yetkisi bulunmamaktadır. En çok yapılan işlem olan alım-satım işlemi, tapu müdürlüklerinin yoğunluğunu artırdığından 28 Haziran 2022’de Noterlere taşınmaz satışına ilişkin, daha sonra tapu sicilinde tescil edilmek üzere, satış sözleşmesi düzenleme yetkisi verilmiştir. Bu çalışmada noterlere verilen taşınmaz satış yetkisinin tapu sicil sistemi üzerindeki etkileri araştırılmıştır. Çalışmada veri toplama aracı olarak bir yapılandırılmış anket formu geliştirilmiş, belirlenen örneklem grubuna yönlendirilerek, görüş ve öneriler alınarak, basit deneysel istatistiksel yöntemlerle değerlendirilmiştir. Çalışmadan elde edilen en önemli sonuç, noterlerin taşınmaz alım-satım işlerini yapmasının vatandaşlar açısından zaman ve maddi yarar sağlamada yetersiz kalacağı görüşüdür. Bununla birlikte alım-satım işlemi için alternatif bir seçenek sunması olumlu olarak değerlendirilebilir. Ayrıca, bu yetkinin Kadastro 2014’ün 5. İfadesine (Kamu-özel sektör entegrasyonu) uygun olduğu da belirtilmelidir.

The indiscriminate use of surface water has heightened the demand for groundwater supplies. Therefore, it is critical to locate potential groundwater sources to develop alternative water resources. Groundwater detection is tremendously valuable, as is sustainable groundwater management. Mersin, in southern Türkiye, is expected to confront drought shortly due to increased population, industry, and global climate change. The groundwater potential zones of Mersin were determined in this study by GIS-based AHP, VIKOR, and TOPSIS methods. Fifteen parameters were used for this goal. The study area was separated into five categories. The results show that the study area can be divided into “Very High” zones (4.98%, 5.94%, 7.96%), followed by “High” zones (10.89%, 10.32%, 16.50%), “Moderate” zones (60.68%, 52.41%, 51.56%), “Low” zones (21.28%, 28.53%, 20.90%), and “Very Low” zones (2.18%, 2.80%, 3.07%) in turn. Data from 60 wells were used to validate potential groundwater resources. The ROC-AUC technique was utilized for this. It was seen that the performance of the VIKOR model is better than that of the AHP and TOPSIS (76.5%). The findings demonstrated that the methods and parameters used are reliable for sustainable groundwater management. We believe that the study will also help decision makers for this purpose.