Gloria Bordogna

This contribution outlines the design of a Knowledge Hub of heterogeneous documents related to the UNEP/MAP Barcelona Convention system. The Knowledge Hub is intended to serve as a resource to assist public authorities and users with different backgrounds and needs in accessing information efficiently; users should be able to either formulate natural language queries or to navigate a knowledge graph that is automatically generated to find relevant documents. The ad hoc retrieval task and the Knowledge Hub creation are defined based on state-of-the-art Large Language Models (LLMs). Specifically, this contribution focuses on a user-evaluation experiment that tested publicly available pretrained foundation Large Language Models (LLMs) for retrieving a subset of documents with varying lengths and topics.

Forest fires play a crucial role in shaping the Mediterranean biome while posing a significant threat to highly fire-prone Southern European countries. Factors such as prolonged dry periods and fuel accumulation increase the frequency and intensity of wildfires. Fire risk in the Mediterranean Basin is exacerbated by climate change and future climate projections highlight the need for advanced fire risk mapping, monitoring, and management strategies.The EU-funded FirEUrisk project addresses these challenges through an integrated approach to wildfire risk monitoring, leveraging Earth Observation (EO) data and remote sensing techniques. EO data, particularly from satellites, can effectively monitor fire risk parameters over extensive areas in a resource-efficient manner. Fuel type and model mapping are essential for wildland fire risk monitoring and emergency management, yet existing global and continental-scale thematic maps lack sufficient spatial detail, particularly in regions with heterogeneous vegetation.This study focuses on developing a methodology to classify fuel types in Sardinia, Italy, a fire-prone pilot site for the FirEUrisk project. Sardinia, the second-largest island in the Mediterranean Basin, experiences prolonged wildfire seasons triggered by human activities and sustained by intense droughts.Fuel type classification was achieved using machine learning (ML) models trained on Sentinel-2 time series. Input datasets included digital terrain models, vegetation indices, and canopy height estimates. Training and testing samples were collected via an on purpose developed web application, enabling experts to label 10 m x 10 m pixels using orthophotos, Google Street View, and vegetation indices time series. The ML models were trained with 80% of the dataset and tested with 20% and performance metrics such as precision, recall, and F1-score were computed.This study demonstrated the feasibility of producing high-resolution (10m) fuel type maps for Sardinia using Sentinel-2 time series. However, the classification task remains challenging due to the structural complexity of vegetation in Mediterranean regions, leading to diverse fire behaviours and impacts. Future improvements include additional training samples collection, validation of the resulting classification, and the integration of vertical vegetation structure data, such as RADAR or LiDAR.

Approaches that are transdisciplinary and participatory can help to address complex socio-ecological issues by integrating multiple disciplinary perspectives while taking into account the different needs and experiences of community members and other stakeholders. Despite this promise, such approaches are rarely applied within the scientific community, as researchers and public actors often lack the training, practice and reference cases required to handle the working relationships and translations of terminology, ideas and values across multiple bodies of knowledge. A case study described in this manuscript depicts a group of researchers, artists and citizens consciously engaged in the construction of a transdisciplinary process as part of a 40-day ‘citizen science’ experiment focussed on assessing soil fertility in the urban area of Milan, Italy. The group drew from recognised scientific approaches, applied agronomic methodologies, artistic practices and technological tools, integrating them into a hybrid process of collective and participatory inquiry. As a quantitative outcome of the experiment, a dataset of bio-chemical parameters was generated, which was enriched by agronomic interpretations but also by artistic and reflective materials. Importantly, the process developed transdisciplinary and participatory skills, as it created a potentially replicable procedure of engagement, analysis and presentation for use in other citizen science settings. This article presents the context, the multiple objectives of the research and the applied approach and its timeline. Described in detail are the process of designing and conducting the experiment by involving an extended research community—including both junior and senior researchers—in progressive steps. Quantitative and qualitative results are provided. The findings are meant to contribute case material and methods to inform the advancement of transdisciplinary research approaches within the scientific community as well as examples of ways to transcend the boundaries of science to include artists and community stakeholders. The aspiration is to inform and inspire concrete application of transdisciplinary and participatory methods in concert to address complex socio-environmental challenges.

This paper reviews recent literature on Spatial Data Infrastructure (SDI) for remote sensing, aiming to provide a comprehensive analysis of the state-of-the-art practices, challenges, and future directions. It also tries to find out if current projects and initiatives involving the exploitation of satellite remote sensing are still framed in the SDI concepts.

The current practice of users searching for different types of geo-resources in a geographic area and wishing to identify the most convenient routes for visiting the most relevant ones, requires the iterative formulation of several queries: first to identify the more interesting resources and then to select the best route to visit them. In order to simplify this process, in this paper a novel functionality for a geographic information retrieval (GIR) system is proposed, which retrieves and ranks several routes for visiting a number of relevant georeferenced resources as a result of a single query, named flexible trip-planning query. An original retrieval model is defined to identify the relevant resources and to rank the most convenient routes by taking into account personal user preferences. To this end, a graph-based algorithm is defined, exploiting prioritized aggregation to optimize the routes’ identification and ranking. The proposed algorithm is applied in the proof-of-concept of a Smart cOmmunity-based Geographic infoRmation rEtrievAl SysTem (SO-GREAT) designed to strengthen local communities: it collects and manages open data from regional authorities describing categories of authoritative territorial resources and services, such as schools, hospitals, etc., and from volunteered geographic services (VGSs) created by citizens to offer services in their neighbourhood.

The term Geospatial Artificial Intelligence (GeoAI) is quite cumbersome, and it has no single, shared definition

The paper presents a critical and comprehensive analysis of recent developments, trends and challenges of Flexible Query Answering Systems (FQASs).Flexible query answering is a multidisciplinary research field at the crossroad of several disciplines among which Information Retrieval (IR), databases, knowledge based systems, Natural Language Processing (NLP) and the semantic web, which aims to provide powerful means and techniques for better reflecting human preferences and intentions to retrieve relevant information.The analysis follows the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines characterized by a top-down process, starting with relevant keywords for the topic of interest to retrieverelevant articles from meta-sources, and complementing them with relevant articles from seed sources identified by a bottom-up process.To mine the retrieved publication data a network analysis is performed which allows to present in a synthetic way intrinsic topics of publications by revealing aspects of interest. Issues dealt with are related to both query answering methods,both model-based and data-driven, the latter based on Machine Learning, and to their needs for explainability and fairness, and big data, notably by taking into account data veracity.Conclusions point out trends and challenges to help better shaping the future of the FQAS field.

The paper proposes a multi-criteria and data driven fusion approach whose semantics can be explained in terms of attitude towards decisions. It is exemplified to assess environmental status from remote sensing images in order to identify hot spot of critical situations and anomalies induced by wildfires, floods, desertification, erosion etc. by fusing multiple factors defined by experts knowledge. The fusion function is an Ordered Weighted Averaging (OWA) operator, whose behaviour is here characterized by degrees of pessimism and democracy. The paper proposes to explain the semantics of the fusion function learnt from few ground truth data available, i.e., the OWA operator, by computing its degrees of pessimism/optimism and democracy/monarchy, which are defined as semantic interpretations of both orness and dispersions respectively. Pessimism indicates if the fused map is more prone to commission (overestimation) or omission (underestimation) errors, while democracy indicates how many factors contribute to the generation of the map. The approach is exemplified to map the flooded areas from remote sensing by considering different models based on distinct spectral indexes and domain experts.