geo3D. Harnessing OpenStreetMap for Community-Led 3D City Modelling and Sustainable Development Education in Africa

Abstract

While OpenStreetMap (OSM) has become central to participatory mapping across Africa, tools for extending 2D mapping into spatial modelling remain fragmented. This paper addresses: How can open geospatial data and free software enable reproducible, interactive, and meaningful local-scale 3D modelling for education and community empowerment in Africa?

We present geo3D: an openly licensed tool integrating OSM data, Jupyter Notebook, and 3D urban modelling for spatial reasoning and SDG-aligned learning. Rather than claiming educational effectiveness, we present geo3D as a foundational framework enabling communities to progress from descriptive mapping to analytical modelling for evidence-based planning and spatial literacy development.

Problem Statement

Participatory mapping has expanded rapidly across Africa through OSM, HOTOSM, and YouthMappers, contributing 6.2M building footprints between 2015-2021. However, engagement remains largely descriptive, focused on feature collection rather than analytical modelling or scenario exploration.

Key gaps identified include: Fragmented tools (Existing 3D tools, such as QGIS 3D, OSM2World and 3dfier address discrete pipeline stages without integration), Missing reproducible workflows (Participatory mapping remains project-based with undocumented processes), Educational limitations (Activities confined to digitization rather than higher-order spatial analysis), Technical barriers (No openly licensed framework links OSM data validation, 3D model generation, and spatial analysis)

This fragmentation reflects broader pedagogical challenges where mapping is introduced as an end rather than a pathway to spatial reasoning and problem-solving.

geo3D Framework (Technical Architecture / Methodology)

Implemented in Python within Jupyter Notebook, geo3D is guided by four principles:

1. Integration: Unified preprocessing, 3D generation, and spatial analysis workflows designed to execute at a community (local) level to facilitate hands-on, grassroots learning.
2. Reproducibility: Documented, replicable notebook-based pipelines
3. Standards compliance: ISO 19107-compliant topologically valid models with Jupyter Notebooks that conform to Computing Best Practice.
4. Accessibility: Openly licensed, cloud-deployable for low-resource contexts

geo3D operates under two primary Processing Options, determined by the scale of analysis:
- Village: Optimised for areas with fewer than 2500 buildings.
- Suburb: Tailored for areas with more than 2500 buildings.

Following this, users select a Processing Strategy based on their needs:
1. osm_LoD1_3DCityModel: A high-quality, lightweight LoD1 3D City Model adhering to Open Geospatial Consortium (OGC) and International Standards Organization (ISO) spatial schema for 3D primitives (ISO 19107). This model enables quantitative analyses for urban planning, noise propagation, energy demand, and wind comfort factors. The strategy requires a raster Digital Elevation Model (DEM).
2. interactiveOnly: A pseudo-3D HTML-based interactive visualisation tool for user engagement, navigation, and sharing.

Spatial Analysis Capabilities
Integrated analytical functions include: Population estimation based on building footprints and local knowledge, Building Volume per Capita (BVPC) spatial equity metric linking residential volume to population, Topology error detection creating feedback loops for OSM data improvement.

Cape Town Case Study

Testing across four diverse sites demonstrated technical feasibility:

Performance results:
Processing time varied from 8 seconds (50 buildings, 25m DEM) to 45 minutes (2,187 buildings, 5m DEM) with all models validated using val3dity for ISO 19107 compliance. Proof-of-concept demonstrates alignment between grassroots learning and neighbourhood-scale suitability .

Spatial analysis outcomes:
Population for all areas where estimated based on local knowledge and OSM data. Where areas had an existing census metric; a population growth rate and projected (future) population estimate was possible. These values yield: Mamre (peri-urban): 10,736 current population, 1.43% growth → 12,368 by 2034 and Salt River (urban): 9,023 current population, 2.63% growth → 11,703 by 2034

BVPC inequality analysis highlighted housing and economic disparity across all areas. Walmer Estate (affluent): 190.1m³/person, Salt River (urban): formal: 86.1m³/person and social housing: 96.6m³/person, Mamre (rural): formal 83.1m³/person and informal: 42.6m³/person (indicating overcrowding) and CPUT dormitories: 99.4m³/person

Results correspond with recent spatial inequality studies, providing quantitative evidence for SDG 11 discussions.

Discussion

By bridging descriptive and analytical practice geo3D addresses the mapping-to-modelling gap (highlighted in the Problem Statement) through:
- Integrated learning: Students learn programming while practicing scientific computing
- Place-based inquiry: Operating where users have local knowledge and experience
- Dual pedagogical tracks: Analytical rigour (LoD1 models) and community communication (pseudo-3D visualizations)
- Reproducible workflows: All steps documented in openly licensed Jupyter notebooks

SDG Alignment
While untested without validation; the geo3D framework supports: SDG 4 (Quality Education): Integrating digital literacy and computational thinking, SDG 8 (Decent Work): Building geospatial data science skills and SDG 11 (Sustainable Cities): Enabling communities to model and assess built environments

Scope Limitations and Future Research

While demonstrating technical feasibility, several critical aspects require further investigation:
- Educational effectiveness: No empirical evidence that geo3D improves spatial literacy or student engagement compared to traditional approaches. Educational value claims remain theoretical.
- Community empowerment impact: Assumptions that analytical tools lead to empowerment lack demonstration. Whether communities use geo3D outputs for advocacy or decision-making requires investigation.
- Meaningfulness: BVPC and population metrics reflect researcher rather than demonstrated community priorities. Indicator relevance needs community validation.
- Contextual transferability: Evidence limited to Cape Town. Applicability across diverse African contexts—varying infrastructure, resources, linguistic and cultural approaches—remains untested.

This positioning establishes geo3D as a proven technical foundation while acknowledging the empirical work needed to validate its educational and empowerment potential. In other words: the research question (how) has been satisfied but the paper has exposed several avenues of further research that must investigate the tools effectiveness.

Research Opportunities and Implementation

Community Research Pathways
- Participatory validation: Feedback loops between analysis and OSM editing
- Educational research: Investigating how 3D modelling influences spatial literacy
- Interdisciplinary collaboration: Linking mapping with public health, climate adaptation, housing policy

Technical Requirements
- Python 3.9 with Jupyter Notebook
- Cloud deployment eliminates installation barriers
- Optimized for neighbourhood scales (50-5,000 buildings)
- Modular design adaptable to schools, universities, NGOs, community organizations

Contribution and Future Development

This tool paper contributes:
1. Methodological innovation: Linking grassroots mapping with analytical urban modelling
2. Educational framework: Demonstrating reproducible spatial workflows as pedagogical tools
3. Technical foundation: Providing tested, extensible pipeline for diverse contexts
4. Community platform: Enabling progression from descriptive cartography to interactive modelling

Future development focuses on partnerships with OSM Africa networks and responding to the needs of the community

Conclusion

geo3D addresses how open geospatial data and openly licensed software can enable meaningful local-scale modelling by providing a reproducible foundation for participatory spatial education. The framework transforms the question from "what is mapped" to "what can be modeled and interrogated," supporting evolution from descriptive cartography to analytical spatial reasoning.

As proven technical capability rather than demonstrated social impact, geo3D establishes a platform for collaborative research addressing African urban development challenges while acknowledging the empirical work needed to validate educational and empowerment potential.

A lightning talk is available at: https://adriankriger.github.io/geo3D/