Mathematically-Grounded, Explainable Multi-Agent Systems for Terrain Hyperspectral Analysis and Triage

Nasrulla Khan K; Sakthivel C R

Mathematically-Grounded, Explainable Multi-Agent Systems for Terrain Hyperspectral Analysis and Triage

Author(s)	Mr. Nasrulla Khan K, Dr. Sakthivel C R
Country	India
Abstract	Agentic systems, in which massive language models manage analytical tools and large foundation models that are trained on data from earth observation systems are two concurrent trends that are transforming the field of remote sensing. A literature review of 2025-2026 shows that both of them have more powerful and independent analysis but neither of them can meet the needs of high-stakes terrain analysis. The agentic remote-sensing systems of today are characterized by high tool-orchestration error, shallow non-replayable memory, hyperspectral and spectral-physics integration is an open challenge and reasoning about red-green-blue and multispectral tools. Foundation models generate strong embeddings but do not have statistical guarantees and decision provenance. While optimal transport is already a widely used and established mathematical tool for solving the problem, there is also rapid development of other tools such as tensor and block-term decomposition, topological data analysis, graph diffusion and conformal prediction, which are each emerging as a stand-alone tool rather than as a common analytical substrate over which an agent can reason. In this survey, 20 representative papers from the year 2025-2026 related to geospatial and agentic artificial intelligence, Earth foundation models, hyperspectral unmixing and classification, topological and optimal-transport techniques, uncertainty quantification, explainable AI and change and tamper detection are discussed. It organizes them by the research topic that the theoretically inspired, explainable, LLM-independent multi-agent framework TerraXIA tackles. Unlike in other systems, each agent in TerraXIA generates uncertainty-bounded, verifiable triage based on a common mathematical representation of the world-state of a terrain hyperspectral scene. The review provides a consensus scientific basis for the recommended work program by identifying a consistent set of gaps and the relationship of each gap to the four research objectives and 12 innovations in the framework
Keywords	Hyperspectral Imaging, AgenticAI, Multi-Agent Systems, Explainable AI, Optimal Transport, Topological Data Analysis, Conformal Prediction, Tensor Decomposition, Remote Sensing, Terrain Analysis, Triage
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 17, Issue 3, July-September 2026
Published On	2026-07-15

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Mathematically-Grounded, Explainable Multi-Agent Systems for Terrain Hyperspectral Analysis and Triage

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