---
id: ch10-geoai-research
type: spine
title: "Open research problems the GeoAI Beta exposed"
readingTime: 3
standalone: true
core: false
teaser: "Retrieval that is not analysis, metadata that limits the AI, place names in nine languages: D4.2's limitations read as a research agenda."
voice: thinker
parent: null
diagram: fig-geoai-open-problems
recallQ: "Name three open research problems for GeoAI in rural planning that the PoliRural+ Beta exposed."
recallA: "For example: RAG retrieves only a few top-ranked chunks, so it does not analyse the whole corpus; AI quality is limited by how complete the metadata is; and local languages and place names need robust handling. Others are GeoRAG integration, provenance and explanation, and performance under rural connectivity."
highlights:
  - "Retrieval is not analysis: 10–20 chunks per answer"
  - "Metadata completeness is the upper limit on answer quality"
  - "Language and place names: adapting to Swahili takes more than translation"
  - "Explanations and safety: the reasoning-grounded explanations paper and SHIELD"
status: accepted
concept: grounded-ai-planning
lang: en
lens: generic
depth: research
formalism: none
visuality: balanced
lengthBand: standard
genre: explainer
state: edited
carriers: prose|diagram
---

D4.2 treats its limitations as inputs for the final development phase, not as failures. Read as a research agenda, they fall into eight problems.

## 1. Retrieval is not analysis

A RAG engine returns a small top-ranked set of chunks, often 10 to 20. That is a relevance match against a subset of documents, not an analysis of the whole corpus, and broad questions return general statements (D4.2 §3.6). **Open question:** how can multi-step conversations, such as Boxes & Arrows agent graphs, make the coverage and depth of an answer checkable?

## 2. Metadata sets the ceiling

Generic tools are only as good as the descriptions they run on. Heterogeneous source quality makes the same tool behave unevenly across datasets (§3.2). Natural-language search "cannot fully compensate for incomplete records, weak keywords or inconsistent classification" (§3.4). **Open question:** how can metadata gaps be detected and repaired before they reach a user's answer?

## 3. Languages and place names

Pilots work in national and regional languages, and D4.2 asks for stronger multilingual and place-name handling (§6.4, §11.1). In KijaniSpace, adapting JackDaw to languages such as Swahili "requires more than translation". The assistant must respect local crops, farming practices, institutional constraints and literacy levels (§9.5).

## 4. Text plus space

GeoRAG needs text retrieval, metadata discovery and access to geospatial services to work together, and D4.2 says this needs stronger harmonisation (§3.7). Many regional questions cannot be answered from prose alone.

## 5. Provenance and explanation

The planned Knowledge Graph would link answers to entities, documents and datasets (§3.7). FAIR2Adapt contributes RO-Crate packages with JSON-LD metadata to document evidence chains, and the I-ADOPT framework to describe variables. Without such descriptions, terms like soil moisture, land surface temperature or flood exposure may mean different things in different projects (§9.4). On the side of model explanations, a CVUT paper on **reasoning-grounded explanations** (predict-and-explain with compressed reasoning) was presented at the XAI World Conference (project fact sheet).

## 6. Safety before the answer

**SHIELD**, released by CVUT on GitHub, is a safety framework that filters unsafe or irrelevant queries before the AI responds (project fact sheet).

## 7. Rural infrastructure

Map interaction must cope with large spatial layers and limited rural connectivity (§6.4). In the Lake Victoria Basin, the same components face limited internet, uneven data and users who need offline, mobile-first and multilingual access, which D4.2 calls a useful stress test (§9.5).

## 8. Evaluation itself

Validation so far has been case-by-case and qualitative. For D4.3 it moves to systematic consultation with WP5 (§11.1), with continuous testing that includes the quality of AI responses. How to measure whether an AI answer was *useful for a planning decision* is still an open question.

**Sources:**
- D4.2 PoliRuralPlus Platform Beta, §3.2; §3.4; §3.6; §3.7; §6.4; §9.4 FAIR2Adapt; §9.5 KijaniSpace; §11.1. [PDF](https://www.poliruralplus.eu/docs/421/D4.2_PoliRuralPlus_Platform_Beta.pdf)
- PoliRural+ public fact sheet, publications with a public URL: Reasoning-grounded explanations, [cahlik.net/…](https://cahlik.net/reasoning-grounded-explanations-paper/) ; SHIELD, [github.com/flpspacek/SHIELD](https://github.com/flpspacek/SHIELD)