Activities conducted using UAV photogrammetry in archaeology have become increasingly common in recent years as they simplify documentation in archaeological excavations and surface surveys while providing a new perspective. In this study, Digital Elevation Models, Orthophotos, and 3D Models were produced using Unmanned Aerial Vehicle (UAV) photogrammetry at the ancient city of Lystra, generating detailed topographic products for pre-excavation preparation phases. Thanks to UAV technology, archaeological remains on the surface were documented through three-dimensional models and high-resolution orthophotos. This method not only saves time in excavation planning but also significantly reduces costs and enhances the accuracy of archaeological data. This study highlights how effectively technology can be utilized in excavation planning, the analysis of surface remains, and the documentation of cultural heritage. Future research suggests the integration of different sensors for more comprehensive analyses.

This study presents a new way to combine the FAO land suitability framework with the Analytical Hierarchy Process (AHP) and Geographic Information System (GIS) tools to assess how suitable the land is for farming in Egypt’s Western Desert, an important area that hasn't been thoroughly examined for its farming potential. Egypt faces increasing food security challenges due to limited arable land and rapid population growth, prompting the need to evaluate the feasibility of expanding cultivation into arid zones. The aim of this study is to assess land suitability across the Western Desert using a multi-criterion, spatially integrated model. Key input parameters include soil type, slope, evapotranspiration (ETo), precipitation, and land use/land cover (LULC). Datasets are sourced from SoilGrids250m, WorldClim, and remote sensing imagery. The AHP is used to assign weights based on expert evaluation, and a GIS-based weighted overlay analysis is applied to generate a suitability map. Results indicate that 20.74% of the area is highly suitable (S1), 41.56% moderately suitable (S2), and 37.36% marginally suitable (S3), with only 0.33% considered currently not suitable (N1). Notably, no areas fall under the permanently not suitable (N2) category, indicating strong potential for land reclamation if supported by appropriate interventions. These findings suggest that the Western Desert holds considerable agricultural promise, provided that challenges related to water availability, infrastructure, and sustainability are addressed. The study provides a transferable methodology to support evidence-based agricultural planning and national policy efforts aimed at land reclamation and food security enhancement.

This study analyses the impact of Land Use Land Cover (LULC) dynamics on the development of Urban Heat Islands (UHI) in a diverse topographic region of Nowshera district, Pakistan. The rapid increase in the study region is causing a rise in land surface temperatures, and subsequently bring alterations in the local climate. The growth of cities and changes in LULC have modified the surface of land and near-surface atmospheric temperature and changed the thermal properties of the gray infrastructure (built-up areas), causing warming compared to the area of non-urbanized surroundings, contributing to the formation of the Urban Heat Island effect. For this study, Landsat 5 (1990), Landsat 7 (2005), and Landsat 8 (2020) were used. LST, LULC, Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Bareness Index (NDBaI), and Urban Index (UI) were derived to assess the influence of LULC changes on UHI. The analysis revealed a 12.44% increase in built-up areas between 1990 and 2020, driven by population growth and urbanization, which led to a 2°C rise in mean LST, with maximum temperatures increasing from 46°C to 48°C. In contrast, vegetation cover, water bodies, and barren land declined by 6.1%, 1.3%, and 5%, respectively, reflecting trade-offs between urban expansion and natural resources. Higher LST values were concentrated around main urban centers and gray infrastructure, while green infrastructure and water bodies experienced lower LST. A negative correlation was observed between NDVI and LST, whereas LST showed positive correlations with NDBI, NDBaI, and UI. The urban and rural temperature range widened with LULC change during the study period. These changes in LST were mainly associated with changes in LULC. This study will be helpful in highlighting the importance of vegetation areas, especially in urban areas, in minimizing the increasing impacts of UHI.

The increasing demand for water in recent decades has led to continuous exploitation and mismanagement of groundwater resources worldwide. This has often resulted in the reduction of the water table and deterioration of water quality due to non-sustainable consumption and excessive extraction practices. To address these issues, it is very crucial to analyse Groundwater Potential (GWP) zones periodically. In this study, Geographic Information System (GIS) and Remote Sensing (RS) techniques coupled with Analytical Hierarchy Process (AHP), Multi Influencing Factor (MIF), and Random Forest (RF) algorithm have been used to define GWP zones. These methods helped to identify, weigh, and rank eleven major hydrogeological factors influencing groundwater potential (GWP). A novel application of the RF algorithm utilized to generate high-resolution GWP maps outperformed AHP (0.875) and MIF (0.828) with a Receiver Operating Characteristic (ROC) of 0.982 in GWP delineation, as assessed by the Area Under the Curve (AUC) analysis. The outcome from AHP, MIF, and RF methods revealed that around 60-70% of the study area showed poor to fair GWP while only 30- 40% of the area exhibited good to excellent GWP. The results revealed that a significant portion of the study area exhibits poor to fair GWP, highlighting the urgent need for sustainable GW management strategies. These findings provide valuable insights for policymakers and local farmers to make informed decisions on sustainable GW management plans tailored to the specific needs of the study area.

This article focuses on analyzing the relationship between the hydrological response and the morphometric characteristics of the Tamri watershed, located in the Western High Atlas of Morocco. The research seeks to answer a key question: how do the Tamri watershed’s geometric and topographic features influence its hydrological behavior? Given the arid and semi-arid nature of the region, understanding these interactions is essential for effective water resource management and flood risk mitigation. The study utilizes a quantitative approach, integrating Geographic Information Systems (GIS) and remote sensing techniques to analyze morphometric parameters such as drainage density, basin shape, slope, and stream network. These analyses are based on a Digital Elevation Model (DEM), topographical maps, and relevant hydrological and climatic datasets. Additionally, a geographical approach is adopted to describe and interpret the relationships between the morphometric characteristics of the watershed and its hydrological response. The main results reveal that the hydrological response of the Tamri watershed is primarily influenced by the interaction between its geometric and topographic characteristics. High drainage density and steep slopes contribute to rapid runoff and increased flood risk. Furthermore, effective spatial management of arid and semi-arid watersheds depends on regulating surface water flow and optimizing the connection between upstream and downstream areas. These insights highlight the importance of integrating morphometric analysis into watershed management strategies to enhance resilience against extreme hydrological events in similar environments.

This study investigates the dynamics of Land Use and Land Cover (LULC) changes along the İzmir-Denizli Highway corridor in western Turkey from 1984 to 2025, utilizing remote sensing techniques and the Landscape Expansion Index (LEI) to analyze urban growth patterns. Employing cloud-free Landsat satellite imagery and the Random Forest classification algorithm within Google Earth Engine, the research identifies and quantifies built-up area expansion over four decades. The findings reveal a significant increase in built-up areas, particularly after 2000, with a total expansion from 45682 hectares in 1984 to 68869 hectares in 2025. The analysis highlights a predominance of edge-expansion growth (71.3%), with outlying growth (27.4%) and minimal infilling growth (1.3%). This trend indicates a shift towards urban sprawl, raising concerns about the sustainability of land use practices. The study underscores the importance of integrating spatial and temporal analyses in urban planning to promote more sustainable development patterns and mitigate the adverse effects of urbanization on the environment.

Rapid, post-conflict urbanization in Baghdad presents acute socio-environmental and infrastructure challenges that conventional remote-sensing models struggle to capture. This study develops a hybrid geospatial–socio-political framework that integrates high-resolution Landsat/Sentinel imagery and spatial indicators (Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Nighttime Lights (NTL), Population Density, Digital Elevation Model (DEM), Distance-to-Road/Water, Building Footprints) with socio-political rasters (UNHCR displacement statistics; subnational governance indices from the Global Data Lab) to forecast land-use/land-cover (LULC) to 2050. A multi-layer perceptron Artificial neural networks (ANN) (input = 9 predictors; hidden layers = 64–128–64; ReLU + dropout 0.3; softmax output) was trained on stratified samples (≈50,000 pixels/city) and implemented in Keras. Historical analysis (1990–2020) shows Baghdad’s built-up area rose ≈82% with mean NDVI declining ≈40%, while Riyadh’s built-up rose ≈55% with NDVI declining ≈20%. The ANN achieved ~88% overall accuracy and a Kappa of 0.82 on the test set. Projections to 2050 (medium-trend scenario) indicate further built-up increases of ≈25% for Baghdad and ≈15% for Riyadh. Feature-importance and ablation tests attribute the largest predictive contribution to displacement density (permutation accuracy drop ≈10.4%), followed by NDVI (≈8.8%) and governance indices (≈7.2%). Scenario-based sensitivity (±25% socio-political perturbations) alters Baghdad’s projected built-up share by ≈8 percentage points, underscoring high socio-political sensitivity; input extrapolation and sensor inter-calibration introduce additional uncertainty (assessed at ~±15–25% across inputs). The results argue for policy responses combining slum-upgrading, adaptive zoning, institutional strengthening, and real-time monitoring (IoT/NTL integration). Future work should apply explainable-AI methods, finer-scale socio-political data, and dynamic (feedback) models to improve causal interpretation and scenario planning.

Unmanned Aerial Vehicles (UAVs) have revolutionized the field of cultural heritage documentation by providing high-resolution, flexible, and cost-effective alternatives to traditional surveying methods. UAVs enable rapid acquisition of aerial imagery and three-dimensional (3D) data, supporting photogrammetric reconstruction, laser scanning, and continuous temporal monitoring of historical structures, archaeological sites, and urban heritage landscapes. This study comprehensively reviews contemporary UAV applications in cultural heritage, emphasizing case studies from Turkey that illustrate the effectiveness of UAV-based surveys in documenting architectural details, assessing material degradation, and informing conservation strategies. The research further examines the integration of UAV workflows with complementary techniques such as terrestrial laser scanning (TLS), close-range photogrammetry (CRP), and Heritage Building Information Modeling (HBIM), highlighting the advantages of hybrid data fusion for producing accurate, visually rich, and analytically robust 3D models. Key benefits, including operational efficiency, non-invasive data collection, and the ability to perform temporal monitoring, are discussed alongside inherent limitations such as environmental constraints, sensor capabilities, and data processing requirements. Finally, the study explores future prospects of UAV-based heritage documentation, including the use of multispectral and hyperspectral sensors, AI-assisted feature extraction, and cloud-based collaborative platforms, emphasizing their potential to enhance preventive conservation, structural assessment, and public engagement. Through this comprehensive review, UAV technology is demonstrated as a transformative tool that not only advances the scientific understanding and preservation of cultural heritage but also facilitates innovative visualization, virtual reconstruction, and broad societal access to historically significant sites.

Two major earthquakes with moment magnitudes (Mw) of 7.7 and 7.6 occurred in the East Anatolian Fault Zone (EAFZ) in Türkiye on February 6, 2023, followed by another earthquake of Mw = 6.4 on February 20, 2023. In addition to the damage to buildings and infrastructure during the earthquake, surface ruptures and ground movements were also observed. The use of Global Navigation Satellite System (GNSS) observations have been implemented in seismic and geodynamic studies since the 1980s. In particular, GNSS observations based on Continuously Operating Reference Stations (CORS) are used to determine pre- and post-earthquake surface movements. A review of the literature revealed that no study has been conducted to observe the surface displacements over a 6-month period using the static PPP technique based on CORS stations for the earthquakes that occurred in Türkiye in February 2023. In this study, 18 of the CORS stations covering the 11 provinces most affected by the earthquakes were selected to detect surface movements and displacements utilizing GNSS data derived from CORS stations. The 24-hour Receiver Independent Exchange (RINEX) data of these stations were obtained. Pre-earthquake and post-earthquake data of the stations were processed separately. Processes were carried out online using the Trimble CenterPoint RTX Post-Processing software with the static precise point positioning (PPP) technique. These stations were then monitored for 6 months after the earthquake. The effect of the ongoing aftershocks was also revealed by comparing the monthly coordinate differences. According to the 6-month results, it was observed that the maximum displacement was 250.07 cm in the x direction, -395.20 cm in the y direction and 30.63 cm in the z direction at Ekinözü (EKZ1) station, and the 3-dimensional displacement was 468.67 cm. The minimum 3-dimensional displacement was observed to be 0.10 cm at the Adana (ADN2) station. This applied method distinguishes this study from other studies and makes it original in its field. This study simultaneously contributed to earthquake researchers and scientific literature

Urban sprawl is a significant phenomenon that emerges from the growth process of settlement areas, which has evolved over time. The historical background and geographical characteristics of a city directly influence its sprawl process. Additionally, the changing sectoral structure of the city, population growth, technological advancements, and economic fluctuations can indirectly affect the direction, speed, and extent of urban sprawl, potentially leading to adverse outcomes. Therefore, it is essential to monitor this process and implement spatial and temporal modeling to keep urban sprawl under control. This study simulates urban sprawl in Konya, a city with valuable agricultural lands, for the year 2040 using two scenarios based on expert knowledge and artificial intelligence. The first scenario combines the Analytic Hierarchy Process (AHP) for weighting sprawl criteria with Cellular Automata (CA), while the second scenario employs Artificial Neural Networks (ANN) with CA to predict future land use changes. Both models used six spatial datasets (DEM, slope, aspect, distances to streams, roads, and protected areas) and CORINE land use maps (2000, 2018), with the 2023 map obtained from Konya GIS data. Model performance was evaluated by comparing simulated and actual 2023 maps using accuracy, Kappa, precision, recall, and F1-score; AHP-CA achieved 96.13 % accuracy and 0.94 Kappa, whereas ANN-CA reached 92.13 % and 0.89, indicating both models reliably capture urban dynamics, with AHP-CA performing better. Both scenarios predict inevitable urban expansion, but the expert-based AHP-CA scenario better preserves agricultural lands and natural vegetation. Based on these results, the study discusses the directions and factors influencing urban change and provides spatial planning recommendations for urban managers