Hariklia D. Skilodimou

The Foinikas River drainage basin of the Western Gulf of Corinth, Greece, represents a complex tectonic environment that is highly susceptible to multiple natural threats. A high-resolution multi-hazard model is developed by integrating seismic, flood, and ground deformation hazards. Seismic hazard was assessed through a refined Probabilistic Seismic Hazard Assessment (PSHA) using a logic tree approach to incorporate multiple source models and Ground Motion Models (GMMs). Flood susceptibility was represented using an existing flood hazard dataset derived from previous hydrological modeling and past flood events, available through an open-access repository. Ground deformation was quantified using satellite radar interferometry (InSAR) from the Copernicus European Ground Motion Service for the period 2019–2023 and compared with previous InSAR results spanning from 2002 to 2017. Results identified a clear spatial pattern: the southern rocky section of the basin exhibits very low hazard, whereas the northern and northeastern coastal areas show high to very high susceptibility, respectively. The integrated multi-hazard zonation, validated with an Area Under the Curve (AUC) of 0.95, demonstrates high predictive accuracy. The outcome of this study can be exploited by local authorities to enhance the management and urban planning of the hazard-prone sectors. All data used for multi-hazard modeling are open and publicly available through open repositories.

Cartography spans millennia of human civilization. Maps helped humans interpret and represent the world before writing. This review examines the evolution of cartographic knowledge from Prehistory to the Middle Ages. It focuses on how maps helped people understand, represent and navigate the world. The methodology used bibliographic search and analysis. Sources came from academic libraries, bibliographic databases and repositories. The literature was divided into Prehistory, Antiquity, and Middle Ages. Approximately 20 maps were selected for their innovations or cosmological significance. They were grouped by orientation, material, content, and Earth’s shape for systematic comparison. The study of maps showed that the Earth‑centred model changed over centuries, slowly replaced by heliocentrism. In Antiquity, Greeks proved the Earth is a sphere, a view that remained mainstream throughout the Middle Ages. Map orientation varied, with Babylonian and Ptolemaic maps facing north, mappae mundi facing east, while Islamic ones facing south. Cartographic materials changed over time, with prehistoric maps on rock, Antiquity using clay tablets, metal, papyrus and parchment, while in the Middle Ages, parchment became common. Content changed with prehistoric artefacts used for local land, in Antiquity became mathematical and administrative, Medieval ones showing religious symbols. By the 13th century, maps had become practical navigation tools.

The continuous growth of Earth’s population poses increasingly complex challenges to the physical environment

Heavy metal soil pollution in urban areas is a critical environmental, public health, and sustainable living issue. The quantities of cadmium (Cd), copper (Cu), lead (Pb), and zinc (Zn) in urban soils in Larissa, Greece, are evaluated in this study along with their risks to human health. A total of 198 surface soil samples were collected from green areas over a three-year period (2021–2023) and analyzed using atomic absorption spectrometry. The results show that Zn has the highest mean concentration (99.80 mg/kg in the summer), followed by Cu (57.33 mg/kg), Pb (48.60 mg/kg), and Cd (0.10 mg/kg). Seasonal variations revealed increased metal levels in the summer due to reduced soil moisture and atmospheric deposition. For assessing the level of pollution in Larissa’s urban areas, thematic maps were created. Using the United States Environmental Protection Agency (USEPA) risk assessment framework, model estimates indicated that ingestion was the dominant exposure route, with children predicted to experience higher non-carcinogenic risks than adults due to the model default exposure assumptions. The hazard quotient (HQ) for Pb in children with soil-pica disorder reached 6.79, exceeding the safe threshold (HQ = 1), indicating significant adverse health risks. Although average metal concentrations were within EU safety limits, the cumulative health risk assessment highlights the need for continuous monitoring and pollution mitigation strategies in urban environments.

Mapping urban pluvial flooding (UPF) in data-scarce regions poses significant challenges, particularly when drainage systems are inadequate or outdated. These limitations hinder effective flood mitigation and risk assessment. This study proposes an innovative approach to address these challenges by integrating deep learning (DL) models with traditional methods. First, deep convolutional generative adversarial networks (DCGANs) were employed to enhance drainage network data generation. Second, deep recurrent neural networks (DRNNs) and multi-criteria decision analysis (MCDA) methods were implemented to assess UPF. The study compared the performance of these approaches, highlighting the potential of DL models in providing more accurate and robust flood mapping outcomes. The methodology was applied to Lahore, Pakistan—a rapidly urbanizing and data-scarce region frequently impacted by UPF during monsoons. High-resolution ALOS PALSAR DEM data were utilized to extract natural drainage networks, while synthetic datasets generated by GANs addressed the lack of historical flood data. Results demonstrated the superiority of DL-based approaches over traditional MCDA methods, showcasing their potential for broader applicability in similar regions worldwide. This research emphasizes the role of DL models in advancing urban flood mapping, providing valuable insights for urban planners and policymakers to mitigate flooding risks and improve resilience in vulnerable regions.

The study put forward a data fusion approach for urban remote sensing that combines SAR (Synthetic Aperture Radar) and optical satellite data. By integrating datasets from different sensors and spatial-temporal scales, the technique aims to extract more accurate information. The fusion approach utilizes two methods: feature-based fusion, where relevant features are extracted and fused, and simple layer stacking (SLS), where the original datasets are directly stacked as multiple layers. This study extracted features using SAR textures (using Sentinel-1) and modified indices (using Landsat-8), and then classified these features using an XGBoost algorithm implemented in Python and Google Earth Engine. Researchers examined five cities, each representing a distinct climatic zone and urban dynamic: Cape Town, Guangzhou, Los Angeles, Mumbai, and Osaka. An accuracy assessment was conducted using random validation points, achieving an overall accuracy of 89.5% using the proposed MSFI method. A comparison was also performed with three well-known global products. The proposed approach, outperformed all three global products achived 89% accuracy while ESA (84%), ESRI (81%) and Dynamic World (82%). Additionally, Land surface temperature analysis was accomplished to investigate the relationship between extracted UIS and Land Surface Temperature (LST) across selected cities to show the practical use of proposed MSFI method. Los Angeles, a warm temperate city, showed the highest LST among all five cities. The datasets, along with the GEE and Python codes, are available at https://github.com/mnasarahmad/sls.

This study proposes a fusion approach to enhancing urban remote sensing applications by integrating SAR (Sentinel-1) and optical (Landsat-8) satellite datasets. The fusion technique combines feature-based fusion and simple layer stacking (SLS) to improve the accuracy of urban impervious surface (UIS) extraction. SAR textures and modified indices are used for feature extraction, and classification is performed using the XGBoost machine learning algorithm in Python and Google Earth Engine. The study focuses on four global cities (New York, Paris, Tokyo, and London) with heterogeneous climatic zones and urban dynamics. The proposed method showed significant results. The accuracy assessment using random validation points shows an overall accuracy of 86% for UIS classification with the SLS method, outperforming single-data classification. The proposed approach achieves higher accuracy (86%) compared to three global products (ESA, ESRI, and Dynamic World). New York exhibits the highest overall accuracy at 88%. This fusion approach with the XGBoost classifier holds potential for new applications and insights into UIS mapping, with implications for environmental factors such as land surface temperature, the urban heat island effect, and urban pluvial flooding.

Water ingress is a common and costly problem in tunnel construction, and understanding the hydrogeological characteristics of a site is crucial for mitigating its effects. This study presents a detailed analysis of the water ingress problems experienced during the construction and operation stages of the Lowari Tunnel in Khyber Pakhtunkhwa, Pakistan, and evaluates the suitability of the water for various purposes. The study found that the water quality varied significantly, depending on the geological conditions and water quantity, with the south portal of the tunnel dominated by Mg cations and bicarbonate anions. The water was found to be suitable for tunnel support systems, including concrete and steel installations, with negligible corrosion observed over the study period. However, the water coming out of the tunnel at the south portal was found to be unsuitable for drinking due to its low pH value. The Wilcox plot classified the water samples as excellent for irrigation, which could benefit local agriculture in the area. The findings of this study can provide valuable insights into the hydrogeological characteristics of the Lowari Tunnel, contributing to the design and construction of similar infrastructure projects in the region. Furthermore, these insights can help improve access to safe and reliable water sources for local communities.