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.

This study examines governance strategies that facilitate sustainable regional circular bioeconomy development, culminating in a typology which enables the classification of regional government good practices supporting circular bioeconomy deployment in diverse regions within Europe. Data on regional circular bioeconomy governance models were collected through desk research and a survey, resulting in a compilation of 61 circular bioeconomy governance models. From this compilation, 20 case studies were identified and further explored to develop a typology of regional circular bioeconomy governance strategies in the EU-27. Findings reveal a strong regional commitment to expanding bioeconomies; however, managing conflicting sustainability goals remains a challenge. This paper provides a comprehensive overview of successful governance models and practices, offering valuable insights for policymakers to support the co-development and replication of effective circular bioeconomy strategies across diverse European regions.

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.

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.

Urbanization poses growing threats to archaeological heritage sites embedded within cities, necessitating innovative monitoring and documentation strategies. This study investigates the use of Unmanned Aerial Vehicle (UAV) photogrammetry for mapping and 3D modelling of urban archaeological landscapes, focusing on the Byzantine-era Didymoteicho Fortress in northern Greece. High-resolution aerial imagery was captured and processed into an orthophoto mosaic and a detailed 3D model of the site’s monuments and their urban surroundings. The UAV-based survey provided comprehensive, up-to-date spatial data that traditional ground methods could not easily achieve in dense urban settings. The results illustrate how UAV mapping can document complex heritage structures, detect risks (such as structural deterioration or encroachment by development), and inform preservation efforts. The discussion situates these findings within global heritage management practices, highlighting UAV technology as a cost-effective, accurate, and non-invasive tool for safeguarding cultural heritage in urban areas. The suggested methodology enhances heritage documentation and risk assessment, demonstrating strong potential for policy integration and proactive conservation planning in historic cities.

This Data Descriptor presents the collected data on remote work among urban and rural workers, emphasizing differences in perceived flexibility, adaptability, preferences, career impacts, well-being, and productivity. The survey, conducted via Prolific between July and August 2024, captured data from 20,013 participants globally with European nationality, offering insights into the benefits and challenges faced by remote workers. Beyond its initial scope, the dataset can be reused to support a wide range of future studies and policy development initiatives. For instance, researchers can utilize this dataset to explore trends in work-life balance, productivity, and urban policy and planning related to remote work. Additionally, urban planners and policymakers can leverage the data to inform strategies for urban and regional development, infrastructure forecasting, planning, and community support in diverse geographic areas. The dataset’s granularity on socio-economic factors makes it an invaluable resource for developing targeted policies that address urban-rural disparities and foster sustainable remote working arrangements across Europe.