Restoration as recovery: participatory urbogeosystemic pedagogy of Karazin University for the Erasmus+ project FutureLand
Abstract
Introduction and previous works done. The 2024 European Nature Restoration Law (NRL) introduces binding restoration targets that – for the first time – explicitly include urban habitats. It challenges higher education to prepare practitioners who can bridge technical diagnostics and community priorities. The FutureLand Erasmus+ project answers that call through MOOCs, micro-credentials and participatory Learning Labs. Within FutureLand the authors propose a participatory restoration pedagogy tailored to post-war urban recovery, building on prior urbogeosystemic research with urban remote sensing and open geospatial toolkits. Thus, the main research objective of this paper is to introduce a transferable, trauma‑informed participatory pedagogy.
Exposition of the main research materials. KKNU’s pedagogy rests on a dual representation of the city within the frameworks of the urbogeosystemic approach. The conceptual framework demonstrates how the urbanistic environment (UE), urbogeosystem (UGS), and urban ecological system (UES) are structurally connected: raster diagnostics, vector governance, and socio‑ecological processes converge into a coherent model. All three constituents enable restoration pedagogy to translate spatial evidence into socially legitimate and ecologically grounded urban futures. Together, UE and UGS translate pixel- and point-cloud signals into place-based narratives usable by municipalities. Methodologically, we follow an “Open-Data-First” principle: OpenStreetMap, global DEMs and available municipal LiDAR are combined with pragmatic 2.5D typological heuristics to produce LOD1/1.5 proxies and conditional volume estimates. Multi-method change detection (vector footprint differencing, hybrid volumetric proxies, and airborne LiDAR comparison) generates candidate urban changes that are then verified in participatory annotation workshops. Lightweight, explainable artificial neural networks, trained on community-annotated datasets and interpreted with Grad-CAM and SHAP, support urban pattern recognition, while keeping model decisions transparent and trustworthy. Pedagogically, the Learning Lab – with trauma-informed facilitation, participatory mapping, memory walks and mixed technical–social assignments – yields evidence packages that balance scientific rigor, civic legitimacy and NRL reporting needs. In these labs students, municipal officers, NGOs and residents co-collect, annotate and validate spatial and narrative data. Pilots in Kharkiv show that student–community teams can produce usable restoration scenarios, data-stewardship templates, syllabi, teacher-training modules and containerized software stacks that lower technical barriers for partners.
Conclusion. Our model shows how universities can catalyze ecological recovery and social renewal in urban areas by pairing open, reproducible technical workflows with trauma-aware, community-centered pedagogy. By aligning urbogeosystemic reasoning universities can serve as convenors and translators – producing governance-ready, community-endorsed evidence even in resource-constrained, post-war settings. Our approach is intentionally pragmatic and scalable: open data, modular lab units and clear documentation enable transfer to other cities lacking LiDAR or extensive municipal data. The pedagogical model helps bridge a gap between spatial science and equitable restoration practice, that gap the NRL now requires us to close.
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