This paper presents SILVIA (Sovereign Information Learning Virtual Intelligent Architecture), a federated Digital Twin framework designed for sovereign territorial data governance. Unlike conventional Digital Twin platforms that model constructed objects within institutional BIM workflows, SILVIA addresses living territories where the primary users are communities — including indigenous peoples, informal settlement residents, and conservation stakeholders — rather than technical professionals. The framework introduces three core contributions: (1) the Territorial Regeneration Index (TRI), a composite metric quantifying ecological, cultural, and socio-institutional regeneration grounded in Berkes (2008), Mang (2) a modular community-plugin architecture that allows the same core infrastructure to serve radically different territorial contexts through interchangeable knowledge bases; and (3) an offline-first WhatsApp interface that enables data collection in environments without stable connectivity. A proof-of-concept implementation demonstrates the framework across two case studies: Kanaimö (Pemón indigenous territory, Gran Sabana, Venezuela) and Caracas Metropolitan Area (informal urban settlements). The system processes natural language observations via LLM, calculates TRI scores with community-specific weights, and generates an Obsidian-based knowledge graph whose density serves as a visual indicator of Digital Twin maturity. SILVIA operationalizes the CARE Principles for Indigenous Data Governance by embedding information sovereignty as a quantifiable variable (IIS) within the TRI formula itself, rather than treating data ethics as an external policy layer. Keywords: Digital Twin, federated architecture, territorial governance, CARE Principles, indigenous data sovereignty, Territorial Regeneration Index, knowledge graph, informal settlements, WhatsApp, LLM, Pemón, Gran Sabana
Josue Fernando Mendes Vieras (Sat,) studied this question.