This paper explores the diverse array of digital tools utilized for data acquisition in archaeology. This abstract outlines the various categories of digital tools commonly employed, including geographic information systems (GISs), global positioning systems (GPSs), remote sensing technologies, 3D scanning and photogrammetry, drones and aerial photography, as well as mobile applications and digital recording systems. Each category is elucidated with examples of their application in archaeological research, emphasizing their roles in site mapping, spatial data collection, artefact documentation, and landscape analysis. Furthermore, it discusses the advancements, challenges, and best practices associated with the integration of digital tools into archaeological fieldwork. It also highlights the potential for future developments in digital technologies to enhance data acquisition capabilities further, ultimately contributing to a deeper understanding of human history and cultural heritage.

The Urban Heat Island (UHI) phenomenon, combined with reduced vegetation and heat generated by human activities, presents a major environmental challenge for many European urban areas. The UHI effect is especially concerning in hot and temperate climates, like the Mediterranean region, during the summer months as it intensifies the discomfort and raises the risk of heat-related health issues. As a result, assessing urban heat dynamics and steering sustainable land management practices is becoming increasingly crucial. Analyzing the relationship between land cover and Land Surface Temperature (LST) can significantly contribute to achieving this objective. This study evaluates the spatial correlations between various land cover types and LST trends in Thessaloniki, Greece, using data from the Coordination of Information on the Environment (CORINE) program and advanced vegetation index techniques within Google Earth Engine (GEE). Our analysis revealed that there has been a gradual increase in average surface temperature over the past five years, with a more pronounced increase observed in the last two years (2022 and 2023) with mean annual LST values reaching 26.07 °C and 27.09 °C, respectively. By employing indices such as the Normalized Difference Vegetation Index (NDVI) and performing correlation analysis, we further analyzed the influence of diverse urban landscapes on LST distribution across different land use categories over the study area, contributing to a deeper understanding of UHI effects.

High-resolution remotely sensed data, which are characterised by their advanced spectral and spatial capabilities, provide unprecedented opportunities to monitor and analyse the dynamic structures of urban environments. Platforms like Google Earth Engine (GEE) enhance these capabilities, as they provide access to vast datasets and tools for analysing key urban parameters, including land use, vegetation cover, and surface roughness–all critical components in urban sustainability studies. This study presents a knowledge-based framework for processing high-resolution satellite imagery tailored to address the demands of sustainable urban planning in the Municipality of Kalamaria in Thessaloniki, Greece. The framework emphasises the extraction of essential urban parameters, such as the spatial distribution of built-up and green spaces, alongside the analysis of surface roughness attributes, including displacement height and roughness length. Unlike conventional methods, our framework enables a detailed intra-urban analysis as these surface roughness attributes are calculated within 200 m × 200 m sub-units. Surface roughness indicators offer essential insights into aerodynamic drag and turbulent air mixing, both of which are directly influenced by the structural characteristics of the urban landscape. Using this approach, ‘wake interference flow’ type was identified as the dominant airflow pattern in the study area. This type was observed in 105 out of 150 sub-units, suggesting that these areas likely suffer from poor air circulation and are prone to higher concentrations of air pollutants. The integration of Google Earth Engine offered a scalable and replicable solution for large-scale urban analysis making it easily adaptable to other urban areas, especially where detailed morphological datasets are unavailable. By providing a robust, scalable, and data-driven tool for assessing urban form and airflow characteristics, our study offers a significant advancement in sustainable urban planning and climate resilience strategies, with clear potential for adaptation in other cities facing similar data limitations.

Over the last fifteen years, the Google Earth Engine (GEE) has become a pivotal tool for large-scale geospatial analysis, with growing applications in urban environmental monitoring. This review examines the peer-reviewed literature, published between 2015 and 2024, that utilizes GEE to evaluate urban environments through remote sensing-derived indices. The literature search strategy was guided by predefined search terms, which were applied to online databases including Scopus and Google Scholar. The inclusion criteria for this review comprised English-language publications, limited to articles only from journals, while book series, books, and conference articles were excluded. The eligibility criteria applied aimed to identify peer-reviewed studies that applied GEE to urban contexts using vegetation, thermal, greenness, or density indices. Studies without a clear urban focus or not employing GEE as a primary tool were excluded. The selection process followed a structured methodological flow, where a total of 291 studies were identified that fulfilled the applied criteria. This review indicates that key methodological trends encompass both conventional techniques, such as Random Forests (RFs), Support Vector Machines (SVMs), and classification/regression trees, as well as emerging machine learning algorithms, with Landsat, Sentinel, and MODIS as the most commonly used satellite datasets. The articles included in this review show a geographic focus, with over 44% of publications from China, 11% from the United States, and 9% from India, while the rest of the countries identified in this review contribute fewer than 5% each, suggesting that there is a significant opportunity for research in underrepresented regions. The main result of this review is that GEE proves to be an effective, scalable, and reproducible platform for urban environmental analysis, with most studies focusing on vegetation and thermal indices using Landsat, Sentinel, and MODIS data. As GEE has become one of the most widely used platforms for urban environmental monitoring, future research should focus on addressing challenges such as the standardization of indices, the consistency of methodological approaches, and the expansion of global coverage through advanced cloud-based geospatial frameworks.

The Mediterranean region, identified by scientists as a ‘climate hot spot’, is experiencing warmer and drier conditions, along with an increase in the intensity and frequency of extreme weather events. One such extreme phenomena is droughts. The recent wildfires in this region are a concerning consequence of this phenomenon, causing severe environmental damage and transforming natural landscapes. However, droughts involve a two-way interaction: On the one hand, climate change and various human activities, such as urbanization and deforestation, influence the development and severity of droughts. On the other hand, droughts have a significant impact on various sectors, including ecology, agriculture, and the local economy. This study investigates drought dynamics in four Mediterranean countries, Greece, France, Italy, and Spain, each of which has experienced severe wildfire events in recent years. Using satellite-based Earth observation data, we monitored drought conditions across these regions over a five-year period that includes the dates of major wildfires. To support this analysis, we derived and assessed key indices: the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Drought Index (NDDI). High-resolution satellite imagery processed within the Google Earth Engine (GEE) platform enabled the spatial and temporal analysis of these indicators. Our findings reveal that, in all four study areas, peak drought conditions, as reflected in elevated NDDI values, were observed in the months leading up to wildfire outbreaks. This pattern underscores the potential of satellite-derived indices for identifying regional drought patterns and providing early signals of heightened fire risk. The application of GEE offered significant advantages, as it allows efficient handling of long-term and large-scale datasets and facilitates comprehensive spatial analysis. Our methodological framework contributes to a deeper understanding of regional drought variability and its links to extreme events; thus, it could be a valuable tool for supporting the development of adaptive management strategies. Ultimately, such approaches are vital for enhancing resilience, guiding water resource planning, and implementing early warning systems in fire-prone Mediterranean landscapes.

One of the most pressing issues of our time is the destabilization of the global climate as a result of rapid urbanization. In Europe, according to United Nations reports, almost 73% of the population lives in cities and this percentage is expected to rise to 85% by 2050 (United Nations and Department of Economic and Social Affairs, ‘World Urbanization Prospects 2018 Highlights’, 2018). This observed urban development and its trends pose significant challenges to our urban environments, therefore it is critical to improve our understanding and assess its impact by measuring and analyzing its patterns. This study presents a comprehensive five-year analysis of microclimatic parameters that can contribute to this analysis, such as land surface temperature (LST) and surface urban heat island (SUHI) intensity, using remotely sensed data over three cities in Greece. Specifically, the selected cities are the city of Thessaloniki, which is the second largest city situated in the northern part of Greece, the city of Larissa on the mainland, and finally the city of Tripoli in the Peloponnese peninsula in southern Greece. Each city represents urban areas with different physical and urban characteristics. For this purpose, the vegetation index-based technique for LST extraction was applied within the Google Earth Engine (GEE) platform. Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) calculations were also performed over a five-year period. The results highlighted the spatio-temporal variations and trends that occur in the studied urban areas, providing valuable insights into the annual patterns of the SUHI phenomenon in different geographical contexts within Greece. This is particularly relevant in countries like Greece, where the density of World Meteorological Organization (WMO) stations providing reliable temperature data is low, usually limited to one per city. Consequently, LST derived from satellite data can provide more accurate intra-urban results of the SUHI effect, demonstrating the value of remotely sensed data as a cost effective and supportive tool for decision-making in environmental and urban planning policies.