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Assessing Regional Innovation Potential: A Novel Approach Using OpenStreetMaps Data

This segment highlights how rural-urban communities can harness open citizen science data - particularly from OpenStreetMap (OSM) - to better understand, compare, and ultimately enhance their innovation potential. By deploying kernel density estimation (KDE) and other advanced spatial analyses, PoliRuralPlus provides a fine-grained look at the distribution of innovation-related assets - such as research institutions, technology hubs, and digital infrastructure - within and across nine pilot regions ranging from Ireland to Latvia. Traditional methods often rely on broad, aggregated statistics that obscure local nuances. In contrast, PoliRuralPlus’s geospatial approach pinpoints specific innovation “hotspots” and spotlights areas requiring further investment or collaboration. Communities benefit directly from these insights by identifying tangible opportunities - such as clustering similar enterprises, establishing shared R&D facilities, or enhancing connectivity - and pursuing tailored policy interventions based on real-time, crowd-sourced data from OSM. Furthermore, these open datasets pave the way for future AI-driven applications within PoliRuralPlus and beyond. Machine-learning tools, for instance, can draw on these spatial indicators to forecast shifts in regional development, evaluate policy impacts, or suggest new ventures. By merging citizen science data, geospatial analysis, and AI, the video underlines a forward-looking strategy to bridge the innovation gap between urban and rural areas, thereby promoting more balanced and evidence-based territorial growth.

topic: Others
type: Deliverable, Methodology, Video
language: English
Bridging Global Language Models and Local Spatial Data

Large Language Models (LLMs) excel at synthesising globally documented knowledge but lack the fine-grained, real-time awareness required for field-level agricultural and rural-planning decisions. This paper introduces JackDaw, a spatially enabled chat-agent architecture that couples foundation-model reasoning with multi-modal geospatial data streams and a retrieval-augmented generation (RAG) pipeline. JackDaw implements a tool-prefiltering mechanism that selects only those data connectors whose topical, temporal and spatial metadata match the current query, thereby mitigating the diminishing returns observed when LLMs are exposed to large, flat toolsets. Through LangChain-based orchestration the platform dynamically assembles workflows that range from lightweight natural-language processing models to domain-specific analytic kernels, while a value-engineering strategy allocates computationally intensive models (e.g., GPT-4-class) only to tasks that require broad contextual reasoning. Benchmark experiments on forestry-asset discovery and vineyard-site assessment demonstrate that JackDaw delivers location-specific, traceable answers that outperform a standalone proprietary LLM, which provides only generic or spatially misattributed responses. The results confirm that bridging global language models with local spatial intelligence markedly reduces hallucination rates and enhances the operational readiness of AI for sustainable agriculture and rural development. Index Terms—Large language models; geospatial AI; retrieval-augmented generation; context-aware agriculture; spatial decision support; tool prefiltering; JackDaw system; rural planning.

topic: Others
type: Products & prototypes, Publication
language: English
D5.2_RAP_Draft_v1.0

Deliverable D5.2 “Regional Action Plans – Consolidation and Cross-Pilot Synthesis” presents the progress made by the nine PoliRuralPlus pilot regions in 2025 as they moved from the design to the implementation phase of their Regional Action Plans (RAPs). The document consolidates the methodological, governance, and monitoring frameworks developed during the first project period and integrates feedback from the Mid-Term Review. Each RAP has been updated using the harmonised RAP Template 3.0, incorporating Gantt-based roadmaps, a refined KPI framework, and cross-cutting elements such as sustainability, gender and diversity awareness, and policy alignment with the Green Deal, CAP, NEB, and LTVRA. The process has been supported by strong cooperation among work packages—WP2 (foresight and governance), WP3 (methodology), WP4 (digital tools), WP6 (innovation), and WP7 (monitoring and exploitation)—ensuring coherence between regional action, innovation, and impact evaluation. The deliverable also highlights early results in stakeholder engagement, governance integration, and interregional learning. It sets the stage for the final phase of the project, where D5.3 (Effectiveness of the Multi-Actor Approach) and D5.4 (RAP Monitoring – Third Year) will document implementation progress, assess sustainability, and consolidate transferable best practices for replication across Europe.

topic: Agriculture, Forestry, Investment, Policy, Tourism, Others
type: Deliverable, Methodology
language: English
GEO AI - Enhancing rural development decision-making with PoliRuralPlus

Centered around strengthening rural-urban linkages, this video demonstrates how integrated data analysis - powered by geospatial and AI components - can help policymakers and local communities gain a more holistic view of regional development prospects. At the core of the approach is JackDaw, a geo-enabled chat agent that merges location-specific data with large language model (LLM) reasoning. Key themes and features Multi-dimensional spatial insights: By gathering structured data on a variety of spatial attributes - ranging from population distribution to the presence of cultural sites - JackDaw helps users assess the interdependencies between rural and urban areas, highlighting mutual benefits such as tourism flows or service accessibility. Rural-urban linkage analysis: One exemplary scenario shows how to evaluate a location’s weekend tourism potential, taking into account the number of nearby urban residents, local amenities, and driving distances. This data-driven lens underscores the extent to which rural areas can attract visitors (and thus investment) from surrounding cities. Real-time, context-aware decision support: Unlike generic AI solutions, JackDaw dynamically retrieves up-to-date geospatial information before advising on policy or strategic investments. This ensures the conversation remains contextually grounded in current rural-urban realities, whether in planning new facilities or aligning local services with urban demand. Bridging analytical gaps: The video illustrates how bridging the information asymmetry between rural and urban territories can spur proactive solutions - like improved transport connections or marketing campaigns - ultimately enhancing the well-being of both communities. By demonstrating how robust data analysis can expose latent development opportunities - particularly in the context of rural-urban synergies - this video reinforces PoliRuralPlus’s broader goal of empowering decision-makers to adopt targeted, evidence-based measures that foster balanced, sustainable growth in Europe’s diverse regions.

topic: Policy, Others
type: Deliverable, Policy, Products & prototypes, Video
language: English
GeoAI meets LLMs – Intelligent agents for enhanced decision-making

This video compares pure large language model (LLM) outputs, such as those generated by ChatGPT, with the JackDaw (agentic tool-calling) approach. Through three real-world use cases, viewers see how incorporating domain-specific data and specialized tools yields more accurate, context-aware, and actionable answers: Weather Query Pure LLM: Provides only a generic explanation or refers to external sources, lacking direct real-time data. JackDaw (Agentic System): Dynamically calls a weather API tied to a specific area on the map, integrating real-time geospatial information. This ensures precise, location-focused forecasts rather than vague or outdated responses. Agricultural Suitability Analysis Pure LLM: Offers broad advice for potato farming, without factoring in specific local attributes like climate or soil characteristics. JackDaw: Merges real-time environmental data (land cover, elevation, weather) and LLM reasoning to assess whether a particular plot is suitable for potatoes. By pinpointing topographical and meteorological conditions, it delivers targeted, evidence-based recommendations. Identifying Water Bodies Pure LLM: May produce incorrect or incomplete references (e.g., citing the wrong river or distant city). JackDaw: Leverages land cover tools to examine hydrological layers in the specified area, accurately detecting water features. This results in data-driven, localized insights and avoids errors that arise when relying on a general model alone. Overall, the video illustrates how the JackDaw approach outperforms a standard LLM in terms of reliability, specificity, and practical utility. By integrating specialized data sources and real-time analysis into the decision-making workflow, the agent-based system consistently delivers more relevant, location-aware responses—demonstrating a clear advantage over purely text-based AI outputs.

topic: Others
type: Deliverable, Methodology, Products & prototypes, Video
language: English
Info4Agro Comprehensive tutorial

This document provides a practical and user-oriented tutorial for the Info4Agro web-based Decision Support System (DSS) for precision agriculture. It is intended to support farmers, agronomists, and advisors in understanding and effectively using the platform’s key functionalities. The document introduces Info4Agro as an integrated system combining IoT sensor data, satellite remote sensing, weather and climate information, and agronomic analytics to support data-driven agricultural decision-making. It describes the project-based workflow, including project creation, management of field boundaries, and the configuration of temporal parameters that influence data processing. Core sections of the document focus on sensor data visualization, explaining how users access real-time and historical meteorological data, explore time series, compare multiple sensors, and interpret basic statistical indicators. The tutorial also covers satellite imagery management, including image selection, handling of cloud-affected scenes, and the calculation of key vegetation indices such as NDVI, EVI, and SAVI. A dedicated part is devoted to the generation of Variable Rate Application (VRA) fertilization maps, outlining the required inputs, zoning workflow, and export of results in standard formats for precision farming machinery. In addition, the document describes climate-based planning tools, which enable users to analyze historical climate conditions, precipitation, temperature patterns, and water balance for strategic agricultural planning. The tutorial concludes with notes on user experience and interface design, emphasizing usability, clarity of visualization, and workflow efficiency. Overall, the document serves as a concise training and reference guide that supports the practical adoption of advanced digital technologies in precision agriculture.

topic: Agriculture
type: Workshops & webinars
language: English
Mallusjoki Rural Event Industry Strategy 2040 and Ecosystem

Mallusjoki Rural Event Industry Strategy 2040 and Ecosystem This result presents the strategic outcome of the PoliRuralPlus Mallusjoki pilot, demonstrating how a rural community can strengthen long-term vitality through an event-based ecosystem approach. Developed collaboratively by Mallusjoki Youth Association, regional stakeholders and Smart & Lean Hub within the Horizon Europe PoliRuralPlus project, the strategy combines community-led development, ecosystem thinking and territorial planning into a practical implementation framework. The document introduces the Mallusjoki Rural Event Industry Ecosystem, identifying the actors, interaction flows and enabling conditions that together create a resilient rural event economy. It also presents a Regional Action Plan (RAP) extending to 2040, outlining phased actions for strengthening cultural tourism, volunteer engagement, digital transformation, sustainable mobility, green transition and local entrepreneurship. Rather than viewing individual events as isolated activities, the strategy demonstrates how recurring cultural events can become drivers of regional attractiveness, community wellbeing, economic development and rural resilience. The model highlights governance through distributed leadership, ecosystem orchestration and continuous monitoring instead of hierarchical management. The result is intended for rural communities, municipalities, regional development organisations, LEADER groups, cultural associations, policymakers and researchers seeking practical approaches to community-led rural development and event ecosystem planning. Although developed for Mallusjoki in Finland, the framework is transferable to other rural regions aiming to strengthen local identity, visitor economy and long-term territorial competitiveness through collaborative ecosystem development.

topic: Tourism, Others
type: Products & prototypes, Others
language: Finnish
OpenStreetMap as the Data Source for Territorial Innovation Potential Assessment

Abstract: This study explores a methodology for assessing territorial innovation potential using OpenStreetMap (OSM) data and geoinformation technologies. Traditional assessment methods often rely on aggregated statistical data, which provide a generalized view but overlook the spatial heterogeneity within regions. To address this limitation, the proposed methodology utilizes open, up-to-date OSM data to identify key infrastructure elements, such as universities, research institutions, and data centers, which drive regional innova- tion. The methodology includes data extraction, harmonization, and spatial analysis using tools like QGIS and kernel density estimation. Results from the PoliRuralPlus project pilot regions highlight significant differences in innovation potential between urban centers and rural areas, emphasizing the importance of detailed spatial data in policy making and regional development planning. The study concludes that OSM-based assessments provide spatially detailed targeted, flexible, and replicable insights into regional innovation potential compared to traditional methods. However, the limitations of crowdsourced data, such as variability in quality and completeness, are acknowledged. Future devel- opments aim to integrate OSM with official statistical data and other data resources to support more efficient and fair resource allocation and strategic investments in regional innovation ecosystems.

topic: Others
type: Products & prototypes, Publication
language: English
Pilot 2: Slovakia

A central objective of the pilot is to transform the “Vision for More Attractive Rural Areas in Slovakia, 2040” from a strategic document into a functioning platform for coordinated territorial action. This requires stronger alignment between national ministries, self-governing regions, municipalities, civil society organisations, youth representatives, academia, businesses and sectoral actors around shared development priorities. The pilot seeks to overcome persistent policy fragmentation and institutional silos while strengthening the connection between rural needs and decision-making, financing mechanisms and governance reforms. Particular attention is given to the everyday realities experienced by rural communities, especially regarding access to public services, sustainable mobility, demographic resilience, opportunities for young people, social inclusion and territorial cohesion. The pilot also contributes to the broader debate on the future governance of rural areas in Slovakia by promoting more participatory, evidence-based and place-sensitive policymaking. Through foresight methods, stakeholder engagement and multi-level cooperation, it aims to create stronger links between long-term visioning, practical implementation and public investment planning. The process supports the emergence of a more coherent national narrative on rural development, capable of connecting economic competitiveness, environmental sustainability, quality of life and social resilience within a shared territorial framework.

topic: Policy
type: Video
language: English
Pilot 3: Central Greece

How the Greek Pilot connects Agriculture, Tourism and Digital Innovation? Central Greece is defined by its vibrant agricultural sector, abundant natural resources, varied landscapes, and significant cultural heritage. This video introduces the Greek Pilot, definied within the concept of PoliRuralPlus project, demonstrating how sustainable agritech, smart agriculture, digital skills, and agritourism can come together to build a more resilient rural–urban ecosystem. Through field demonstrations, collaborative events, and digital innovation initiatives, the pilot unites farmers, researchers, advisors, policy-makers, technology providers, tourism stakeholders, and young professionals. It showcases practical solutions to key challenges, including skills gaps, youth migration away from rural areas, and limited digital capacity in agriculture, while opening up new opportunities for local products, rural entrepreneurship, and sustainable agritourism. Their input gathered from the stakeholder, feeds into the development of the Regional Action Plan, ensuring that local needs, priorities and opportunities are reflected in future actions for the region. The Greek Pilot illustrates how place-based collaboration and digital tools, including the GeoAI chatbot Jackdaw, can strengthen evidence-based decision-making and help rural areas become more connected, inclusive, and future-ready. 🔗 More about the Greek pilot: https://www.poliruralplus.eu/pilots/r...

topic: Agriculture, Tourism
type: Video
language: English