Dynamic processes in coastal zones and human activities in the coastal environment produce pressure on cultural heritage, especially in touristic places. Unmanned aerial systems (UAS) are used as an additional tool for monitoring cultural heritage sites in sensitive coastal areas. UASs provide low-cost accurate spatial data and high-resolution imagery products in various spatial and temporal scales. The use of UAS for mapping cultural heritage sites in the coastal zone is of increasing interest among scientists and archaeologists in terms of monitoring, documentation, mapping, and restoration. This study outlines the integration of UAS data acquisition and structure from motion (SfM) pipeline for the visualization of selected cultural heritage areas (ancient harbors) in the coastal zone. The UAS-SfM methodology produces very detailed orthophoto maps for mapping and detecting cultural heritage sites. Additionally, a metadata cataloging system has been developed in order to facilitate online searching operations for all products of the data acquisition, SfM pipeline, and cartographic processes. For this reason, a specific metadata profile was implemented, based on the European INSPIRE framework. As a result, datasets reusability and catalogs interoperability are promoted.

Large-scale wildfires have become more frequent in Greece and coupled with the country’s limited economic resources, investments in both pre-fire planning and post-fire rehabilitation for most affected areas are not feasible. From the perspective of forest and fire management agencies, the severity and importance of fire effects are evaluated based only on total area burned, while from a societal standpoint, by the number of fatalities and destroyed houses. A holistic approach to rank wildfires with an inclusive assessment of all their effects is missing. We developed a new evaluation and ranking index based on expert judgment, the study of 50 large-scale fire events in Greece and a detailed review of the literature, to develop a set of categories and criteria to assess ecological and socioeconomic effects of wildfires. The Fire Inventory and Ranking of Effects (FIRE) Index provides a comprehensive and easy-to-use semi-numeric framework that combines scores from seven fire effects categories and 56 criteria through a user-friendly web-platform. The seven categories include fire effects on landscape and vegetation, general environmental impacts, regeneration potential and vegetation recovery, casualties and fatalities, destruction and damages to infrastructure, economic losses, and firefighting and wildfire suppression. Each of the 56 criteria within these categories describes a different anticipated fire effect. The magnitude of each fire effect criterion is estimated by predefined ranked choices by one or more persons/assessors in a multi-level evaluation procedure. We apply the FIRE Index assessment to a significant 5900-ha wildfire that occurred in 2011 in northern Greece, including a sensitivity analysis of how different category weights impact the final index score. More diverse metrics to assess wildfire effects will help address the complex social and biophysical dimensions of the wildfire governance challenge and help guide pre- and post-fire management actions.

The increasing availability of linked data poses new challenges for the identification and retrieval of the most appropriate data sources that meet user needs. Recent dataset catalogs and recommenders provide advanced methods that facilitate linked data search, but none exploits the spatial characteristics of datasets. In this paper, we present GeoLOD, a web catalog of spatial datasets and classes and a recommender for spatial datasets and classes possibly relevant for link discovery processes. GeoLOD Catalog parses, maintains and generates metadata about datasets and classes provided by SPARQL endpoints that contain georeferenced point instances. It offers text and map-based search functionality and dataset descriptions in GeoVoID, a spatial dataset metadata template that extends VoID. GeoLOD Recommender pre-computes and maintains, for all identified spatial classes in the Web of Data (WoD), ranked lists of classes relevant for link discovery. In addition, the on-the-fly Recommender allows users to define an uncatalogued SPARQL endpoint, a GeoJSON or a Shapefile and get class recommendations in real time. Furthermore, generated recommendations can be automatically exported in SILK and LIMES configuration files in order to be used for a link discovery task. In the results, we provide statistics about the status and potential connectivity of spatial datasets in the WoD, we assess the applicability of the recommender, and we present the outcome of a system usability study. GeoLOD is the first catalog that targets both linked data experts and geographic information systems professionals, exploits geographical characteristics of datasets and provides an exhaustive list of WoD spatial datasets and classes along with class recommendations for link discovery.

Scientists in the marine domain process satellite images in order to extract information that can be used for monitoring, understanding, and forecasting of marine phenomena, such as turbidity, algal blooms and oil spills. The growing need for effective retrieval of related information has motivated the adoption of semantically aware strategies on satellite images with different spatio-temporal and spectral characteristics. A big issue of these approaches is the lack of coincidence between the information that can be extracted from the visual data and the interpretation that the same data have for a user in a given situation. In this work, we bridge this semantic gap by connecting the quantitative elements of the Earth Observation satellite images with the qualitative information, modelling this knowledge in a marine phenomena ontology and developing a question answering mechanism based on natural language that enables the retrieval of the most appropriate data for each user’s needs. The main objective of the presented methodology is to realize the content-based search of Earth Observation images related to the marine application domain on an application-specific basis that can answer queries such as “Find oil spills that occurred this year in the Adriatic Sea”.