The PoliRural+ tool ecosystem

The PoliRural+ platform: a modular Beta, not one app

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Deliverable D4.2 (PoliRuralPlus Platform Beta) describes the platform as a set of interoperable components that can be combined for different users, pilots and themes, not as one monolithic application. The reason it gives is practical: public authorities, advisors and external developers usually need a few selected functions, not a full deployment.

What the Beta contains

How the parts connect

The components communicate through APIs, standardised metadata profiles and MCP-based integration, so external tools and agents can attach without hard-wired dependencies. Metadata does the connecting: D4.2 concludes that metadata quality directly affects how reliable JackDaw, the dashboards and the analytical tools are.

Each pilot puts its own bundle together. Onboarding has five steps: name the regional planning challenge, select the Beta services, configure workspaces and user roles, prepare or ingest data and metadata, then test in meetings, workshops and real planning tasks (D4.2 §6.1).

What the Beta does not yet do

D4.2 is open about the gaps. The components still feel like separate applications. Interfaces are inconsistent. Multilingual and place-name handling needs work. Outputs must show their sources, data validity and limits more clearly. The roadmap to the final release (D4.3, due at the end of the project) is about consolidation: tighter integration, continuous testing, training material, demos for model builders, and progress towards higher Technology Readiness Levels, with a clearly documented status for each major tool.

The MAA tool comes next, because it shows in numbers what “in pilot use” means.

How-to: Which tool when · Using Hub4Everybody · Finding and publishing data with Magpie and Raven.

How to use Hub4Everybody: find, publish, contribute

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When to use it: you need project documents or service links, or you want to publish your region’s material and map layers where the other tools can reach them.

What you need

• A browser: hub.polirural.eu/home
• For publishing, a project account. Components share one security model: the OAuth2 provider in the Hub environment issues tokens, and the other services trust them (D4.2 §5.4).
• For your own map layers: QGIS with the Layman plugin; for field work, QField on an Android device (D1.1 §3.3).

Steps

1. Browse. The Hub combines page navigation with repository-style access to project documents, outputs, library resources and service links (D4.2 §3.1).
2. Sign in once. The token you receive is sent with requests to the other services, which check its issuer, expiry, scopes and roles before answering (D4.2 §5.4).
3. Find geodata. The Micka catalogue searches metadata, lets you create and edit INSPIRE-compliant records, and harvests from portals that share data through CSW or WMS (D1.1 §3.2).
4. Compose a map. HSLayers NG combines WMS, WFS and vector data in one interactive map (D1.1 §3.2).
5. Publish your layer. In QGIS, the Layman plugin publishes vector or raster layers to the Hub’s Layman server, pushes later edits, sets who may view or edit, and stores metadata. Layman serves the result through WMS and WFS (D1.1 §3.2–3.3).
6. Contribute content. News, articles, blogs and events go through a Wagtail workflow: submission, editor review, approval. New contributors stay invisible until an editor approves them; approved authors publish with a “non-approved” label until review (D1.1 §3.1).
7. Add documents to the library. They are passed through an integration into the retrieval layer behind JackDaw (D4.2 §3.1).

What you get

Curated, searchable access to project material; layers that other GIS software can read through OGC services; pages with embedded interactive maps (the Wagtail map widget); and documents that the AI tools can cite.

Pilot use

D4.2 lists Hub4Everybody among the core services in eight of the nine pilots, from Monaghan to Malta, as the common place for communication, visibility and access to platform resources (§6.2–6.3).

Watch out

• As content grows, its value depends on consistent classification, metadata and curation; without them a larger repository is harder to navigate. D4.2 plans harmonised metadata and taxonomy (§3.1).
• A badly described document or layer is also badly found by the AI tools that read from the Hub.

How to find a dataset with Magpie and publish yours with Raven

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When to use it: you are looking for geodata for a question, or you hold a local dataset that other tools should be able to use.

What you need

• To search: the Magpie demonstrator at jackdaw.avinet.no/magpie (D4.1 §6.5), or simply a dataset question in JackDaw.
• To publish: access to Raven. The pack gives no public address; Raven sits behind the platform’s shared identity provider, so access comes through your pilot’s technical partner.
• A geospatial vector dataset and what you know about it: source, period, meaning of each column.

Steps

1. Search in plain language. Magpie turns your query into an embedding and compares it with embedded ISO 19115/19119 metadata. Results are records with layer names, descriptions, keywords and WMS/WFS endpoints, and it can build a map from them (D4.1 §6.5). In the Beta this function runs inside Raven and usually appears through JackDaw (D4.2 §3.4).
2. Upload what is missing. Raven sends large files in chunks, up to three in parallel; an interrupted upload resumes where it stopped (D4.2 §3.3.3).
3. Describe it. Give a title, a description, classification tags, temporal coverage (a date range, or marked explicitly as unknown) and a semantic description of every column. The interface encourages descriptions in more than one language to improve search recall.
4. Publish. The dataset stays a draft until you publish it to the catalogue.
5. Maintain it. Edit metadata later, attach methodology documents or data dictionaries, grant or revoke access for named users, or replace the geometry layer without a full re-ingest where the state allows it.
6. Test it in JackDaw. Ask about your area; JackDaw’s GeoRAG step finds datasets in the Raven catalogue by semantic matching and then queries them spatially.

What you get

A dataset in PostGIS that can be queried by SQL and by natural-language semantic search, and that platform tools can use without code changes (D4.2 §3.3.3, §6.3).

Pilot use

In Mallusjoki, Raven metadata services ran alongside JackDaw, the Advisor and the MAA tool. D4.2 reports municipal-level spatial analysis that combined open geospatial datasets with local planning needs for the rural events ecosystem (Table 7).

Watch out

• Search cannot fix metadata. Incomplete records, weak keywords and inconsistent classification limit what Magpie or Raven can find (D4.2 §3.4).
• Uneven source quality still causes uneven behaviour across datasets (§3.2).
• Magpie will not grow as a stand-alone product; development continues through Raven.

Desktop, field and data: what plugs into the platform

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The platform does not try to replace established GIS software or data sources. Two of D4.1’s design principles are integration over base development and making use of existing data, such as INSPIRE-compliant geospatial data, PSI Directive resources and Earth observation repositories (D4.1 §3.4).

Desktop and field tools

• QGIS handles detailed editing and spatial analysis. The QGIS Layman plugin publishes vector and raster layers to the Layman server in Hub4Everybody, keeps them in sync, and manages access rights and metadata (D1.1 §3.3).
• QField takes QGIS projects to Android devices for field data collection. It works offline and syncs with Hub4Everybody once a connection is back (D1.1 §3.3).

Components from sister projects

D4.1 lists components brought in from Data4Food2030, THEROS and ALIANCE (§2.1.1):

• an E-Market for Local Food built on PrestaShop, for direct sales of regional food, with IoT monitoring of transport conditions and blockchain traceability;
• cloud-cover detection and “mosaicking” for Sentinel-2 imagery, a validated method for estimating missing data for vegetation analysis and yield prediction;
• localised weather forecasts that combine Sentinel-2, GFS, HadISD and ERA5-Land.

Data named in the objectives

Objective O5 commits the project to using European Research Infrastructures, EOSC, EU Data Spaces, INSPIRE, Copernicus, DIAS, Eurostat, FAO and other data sources (Grant Agreement). Copernicus data explains how satellite layers become local evidence. Partner projects add in-situ sensor data through SensLog and climate products from FOCAL; see the research pillars (D4.2 §9.1–9.2).