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![]() Title:ContextGeo: a Context Engineering Framework for Multi-Agent Geospatial AI Systems Conference:ACIIDS2026 Tags:Context Engineering, Geospatial Artificial Intelligence, Multi-Agent Systems, Retrieval-Augmented Generation and Spatial Databases Abstract: Context management remains a critical bottleneck in multi- agent Geospatial Artificial Intelligence systems, limiting task completion rates and causing context overflow in complex workflows. We propose ContextGeo, a systematic Context Engineering framework that integrates four Retrieval-Augmented Generation strategies through five specialized agents coordinated by a Spatial Context Manager. The framework introduces three novel mechanisms: Hierarchical Spatial Memory for structured multi-scale retrieval, geospatial-aware compression that preserves topological relationships, and Temporal Context Versioning for maintaining spatial-temporal consistency across workflow stages. Through comprehensive experiments on 150 real-world geospatial tasks spanning three complexity levels, ContextGeo demonstrates substantial performance improvements with 91.2% Task Completion Rate (+34.7 percentage points over state-of-the-art), 87.6% Spatial Analysis Accuracy (+28.5 percentage points), and 42.3% token reduction, all statistically significant at p < 0.001. These results provide empirical evidence that systematic Context Engineering can address fundamental limitations in multi-agent Geospatial Artificial Intelligence, with immediate implications for urban planning, environmental monitoring, and disaster response applications. ContextGeo: a Context Engineering Framework for Multi-Agent Geospatial AI Systems ![]() ContextGeo: a Context Engineering Framework for Multi-Agent Geospatial AI Systems | ||||
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