GeoAgentic-RAG: A Multi-Agent framework for autonomous geospatial reasoning and visual insight generation with LLM

Conventional Retrieval-Augmented Generation (RAG) systems have limited effectiveness in geospatial question answering because text-based similarity retrieval cannot adequately represent spatial semantics such as topology and spatial context. To overcome this limitation, we propose GeoAgentic-RAG, a...

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Ngā kaituhi matua:	Chao Liang, Yuanzheng Cui, Run Shi, Guixiang Zha, Xin Yin, Mingzhong Xiao, Dong Xu, Xuejun Duan, Bo Huang
Hōputu:	Artigo
Reo:	Inglês
I whakaputaina:	Elsevier 2026-03-01
Rangatū:	International Journal of Applied Earth Observations and Geoinformation
Ngā marau:	Geospatial Analysis Multi-Agent Spatial Reasoning Spatial Semantic Understanding Retrieval-Augmented Generation
Urunga tuihono:	http://www.sciencedirect.com/science/article/pii/S1569843226001111
Ngā Tūtohu:	Tāpirihia he Tūtohu Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!