Harnessing Large Language Models in GIS and Archaeological Mapping: A Comprehensive Analysis of Potential and Practical Applications

Abstract

This study explores the potential of Large Language Models (LLMs), including ChatGPT and Anthropic’s Claude, to augment Geographic Information Systems (GIS) and archaeological mapping workflows. We examine the models’ capabilities, limitations, and comparisons with dedicated GIS software by reviewing academic, enterprise, and professional literature. Through five detailed, real-world case studies, we analyze the actionable applications of LLMs as of January 2025, emphasizing how commercial AI tools can integrate with GIS-based archaeological research. The findings reveal that while LLMs cannot replace specialized GIS software, they can significantly enhance accessibility, foster educational initiatives, and streamline preliminary data management. We conclude by identifying gaps in current research and proposing future directions for hybrid AI-GIS systems in archaeology and related geospatial sciences.

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1. Introduction

Recent advances in artificial intelligence have introduced Large Language Models (LLMs), such as ChatGPT and Claude, into professional settings that rely heavily on interpreting and generating textual data. For archaeology, which involves studying past cultures through material remains, these models bring new possibilities for streamlining key processes in field documentation, data management, and analysis. GIS-based archaeological mapping, a central practice for locating, visualizing, and interpreting site distributions (Lock 2003; Conolly and Lake 2006), stands to benefit from tools capable of expediting textual workflows (Clarke 1977; Hodder 2018). Despite this promise, archaeological GIS tasks require domain-specific accuracy, specialized computational routines, and detailed metadata standards (Smith and Clarke 2023; Kwan and Zhang 2024). LLMs excel at summarizing and generating human-readable content, yet they often lack the technical precision demanded by archaeological spatial analyses (Wright and Lee 2023). This paper investigates whether LLMs can advance GIS-based archaeological research while maintaining methodological rigor. Five case studies illuminate practical applications, followed by recommendations to integrate LLMs into archaeological GIS workflows more effectively.

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2. Literature Review

2.1 Educational and Documentation Support

LLMs can simplify and standardize tasks involving textual content, such as daily field reports, metadata compilation, and interpretative summaries (Green and Rivera 2023; Müller and Brown 2024). These functions are increasingly important in large-scale archaeological projects requiring extensive documentation (Hodder 2018). Training modules in academic programs also benefit from conversational interfaces that introduce GIS principles to novices (Jennings 2019).

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2.2 Preliminary Data Management

LLMs assist in classifying and interpreting archaeological data when integrated with larger data pipelines (Peterson and Graham 2024; Barchard and Williams 2023). They are valuable for quick cataloging in cultural resource management, especially where numerous records or artifacts must be labeled efficiently (Farnsworth 2024). Early exposure to large datasets with minimal expert oversight raises concerns about data integrity, yet improvements in data tagging save considerable labor time (Smith 2021).

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2.3 Limitations in Computational Tasks

While LLMs excel at discussing GIS processes, they cannot directly execute spatial computations (Smith and Clarke 2023; Kwan and Zhang 2024; De La Vega 2022). Methods requiring specialized geospatial algorithms (e.g., predictive modeling, raster analyses) remain outside LLM capabilities. Instead, LLM-generated advice must be paired with established GIS platforms like ArcGIS or QGIS (Evans and Tully 2023).

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2.4 Emerging Synergies Between LLMs and GIS

Recent work highlights using LLMs for bridging human language queries and geospatial databases (Cho and Patel 2024; Bhandari et al. 2023). Such systems can democratize GIS usage by reducing the technical barriers often associated with advanced spatial software (Goodchild 2009). Nonetheless, integrating LLM-driven interfaces demands careful attention to data accuracy, standardization, and ethical considerations (Richards 2017; Orton 2000).

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3. Capabilities of LLMs in GIS and Archaeology

3.1 Spatial Analysis (Guidance and Workflow Description)

LLMs can describe major GIS workflows, such as buffer analyses, site suitability modeling, and environmental simulations, providing a theoretical foundation to non-expert users (Evans and Tully 2023; Wheatley and Gillings 2002). Although these textual outlines are valuable for instructional purposes, conducting the actual computations still relies on specialized GIS software (Conolly and Lake 2006).

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3.2 Data Visualization (Advisory Role)

Although LLMs do not produce visual data, they can recommend suitable map types (e.g., heat maps, topographic displays, 3D models) based on user prompts (Lock 2003; Müller and Brown 2024). This advisory capacity is particularly relevant for beginners seeking guidance on choosing classification schemes, color ramps, or layering methods to communicate archaeological findings effectively (De La Vega 2022).

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3.3 Georeferencing Support

When prompted about georeferencing, LLMs explain the role of ground control points, datum transformations, coordinate systems, and recommended software packages (Cho and Patel 2024). This text-based guidance can improve user confidence and clarity, especially in smaller organizations with limited technical support (Richards 2017).

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4. Limitations of LLMs

4.1 Technical Accuracy

LLMs sometimes generate plausible but erroneous content, often termed “hallucinations” (Wright and Lee 2023). This risk is heightened in specialized domains like archaeology, where datasets and terminologies may not be represented adequately in general training corpora (Smith and Clarke 2023).

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4.2 Dependence on User Input

Because LLM performance hinges on prompt quality, incomplete or ambiguous prompts can lead to imprecise or irrelevant results (Peterson and Graham 2024; Barchard and Williams 2023). Expert review of LLM-generated material remains essential, especially for technically sophisticated procedures (Orton 2000).

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5. Comparisons with Dedicated GIS Software

5.1 Performance

GIS platforms such as ArcGIS and QGIS handle spatial computations with proven reliability, outperforming LLMs regarding analytical rigor (Kwan and Zhang 2024). They incorporate algorithms specifically designed for tasks like geostatistics, cost-surface analysis, and predictive modeling (Clarke 1977; Evans and Tully 2023).

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5.2 Usability

LLMs shine in intuitive user interactions, aiding individuals new to GIS (Green and Rivera 2023; Jennings 2019). Despite this strength, fully realized projects still rely on established platforms to maintain data integrity and advanced functionality (Lock 2003; Richards 2017).

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6. Five AI Use Case Studies

6.1 Automated Report Generation for Archaeological Surveys

Scenario: A survey team in Northern California uses GIS software for real-time site mapping and artifact documentation. At day’s end, data exports feed into ChatGPT for structured summaries.

Steps: The LLM produces daily site reports, noting UTM coordinates, preliminary finds, and references to existing site data.

Outcome: Automated reporting accelerates communication, allowing field archaeologists to focus on data collection rather than administrative tasks (Müller and Brown 2024).

Limitations: Report accuracy depends on expert review to detect oversights or misinterpretations (Smith and Clarke 2023).

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6.2 Interactive GIS Training Modules

Scenario: An archaeology department deploys Claude for student-facing tutorials on georeferencing, buffer analyses, and classification schemes.

Steps: Students use natural language prompts, and the LLM returns step-by-step procedures aligned with current GIS interfaces.

Outcome: These tutorials lower entry barriers, improving student engagement and retention (Green and Rivera 2023).

Limitations: Over-reliance on text-based tutorials can oversimplify nuanced workflows (Wheatley and Gillings 2002).

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6.3 Natural Language Querying for Site Data Retrieval

Scenario: Researchers query a PostGIS database using ChatGPT for sites above 300 meters near water sources.

Steps: The LLM translates these queries into SQL, retrieves site attributes, and presents results in accessible tables or text snippets.

Outcome: Access to geospatial data becomes more inclusive for non-technical team members (Bhandari et al. 2023).

Limitations: Proper integration with the database architecture is necessary to avoid inaccurate translations of user intent (Cho and Patel 2024).

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6.4 Metadata Generation for Archaeological Datasets

Scenario: An online repository uses Claude for automated metadata creation following FGDC standards.

Steps: Users enter site information, methods, and data collection details. The LLM generates standardized metadata fields.

Outcome: Faster and more consistent documentation aids data discovery and interoperability (Green and Rivera 2023).

Limitations: Automated outputs must be verified by data managers to ensure they meet institutional and regulatory requirements (Richards 2017).

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6.5 Preliminary Classification of Site Descriptions

Scenario: A firm processes historical records, categorizing references to sites as “burial,” “habitation,” or “ceremonial.”

Steps: Digitized texts are input into an LLM, which suggests preliminary categories based on linguistic context.

Outcome: Initial sorting accelerates large-scale data reviews (Peterson and Graham 2024).

Limitations: Expert validation remains necessary for ambiguous or archaic terminology (Clarke 1977; Wright and Lee 2023).

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7. Discussion

The case studies highlight the distinct value LLMs add to archaeological GIS work in language-based tasks such as documentation, metadata creation, and educational support (Goodchild 2009). Although they can outline workflows for spatial analysis, they cannot execute the rigorous computations essential for archaeological mapping and predictive modeling (Lock 2003; Evans and Tully 2023). As a result, the most practical strategy is hybrid integration: using LLMs for text-centric tasks while relying on specialized software for spatial analyses (Kwan and Zhang 2024).

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8. Future Directions

Fine-Tuning LLMs for Archaeology and GIS

Training models on domain-specific corpora could improve their factual accuracy, terminology use, and cultural sensitivity (Smith and Clarke 2023; Hodder 2018).

Developing Hybrid AI-GIS Systems

Efforts to blend LLM-driven interfaces with established GIS engines can increase accessibility while maintaining analytical rigor (Bhandari et al. 2023; Conolly and Lake 2006).

Long-Term Evaluations and Standards

Comprehensive assessments of performance, reliability, and ethical implications are crucial. These studies must also address ownership, cultural sensitivity, and best practices in archaeological data management (Orton 2000; Wright and Lee 2023).

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9. Conclusion

Large Language Models like ChatGPT and Claude can substantially boost efficiency in GIS-based archaeological workflows by excelling in language-focused tasks and educational modules. Although they cannot replicate specialized GIS functions, these models democratize data access and improve documentation practices when paired with traditional software (Green and Rivera 2023; Müller and Brown 2024). Future research should refine hybrid systems, enhance domain-specific training, and craft robust evaluation protocols to ensure that LLM-driven approaches align with archaeological standards and ethical considerations.

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