Geo-Spatial Analysis with Large Language Model
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Keywords:
Spatial Data Processing, Geographic Information System (GIS), Large Language Model (LLM), Natural Language Processing (NLP), Spatial Context Understanding, Geospatial Information Extraction, Spatial Relationship Inference, Location based Analysis, Geocoding Accuracy, Spatial Data Integration, Textual Spatial Data, GIS Decision SupportAbstract
In order to improve the effectiveness and precision of geospatial data analysis and interpretation,
this research investigates the use of large language models in Geographic Information System (GIS)
processing. In order to enable natural language interactions for accessing, analysing, and visualising
geographical data, the technique entails combining cutting-edge language models, including GPT-3 and
BERT, with GIS software. By bridging the gap between textual information and geographical insights, this
study is significant because it gives users additional skills for information retrieval and decision-making in
GIS applications.
The use of large language models in GIS processing can increase data understanding, expedite processes,
and aid in knowledge discovery in spatial datasets, according to key results. With the improved natural
language interfaces, people may engage with GIS systems more naturally, opening up geospatial analysis
to a larger audience. All things considered, the incorporation of massive language models into GIS
processing exhibits encouraging promise for revolutionising the processing, sharing, and use of
geographical data across a range of fields.
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