Review of AI-Driven Chatbot Solutions for Remote Sensing in Archaeology

Abstract

This study reviews AI-powered chatbot solutions for remote sensing archaeology, emphasizing its capacity to transform data interpretation and accessibility. The study reviews methods that integrate large language models (LLMs), like those of GPT-4, with machine learning algorithms, such as convolutional neural networks (CNNs), to handle and evaluate vast remote sensing datasets. The incorporation of these AI technologies allows archaeologists to query and engage with satellite images, LiDAR, and multispectral data via intuitive, conversational interfaces. The literature review findings demonstrate that the utilization of AI chatbots markedly enhances the efficiency of data retrieval and analysis, rendering intricate geographical information more accessible to non-experts and facilitating real-time site detection and monitoring. The combination of artificial intelligence and remote sensing has gained interest as archaeologists encounter the difficulty of analyzing extensive datasets generated by contemporary imaging technology. Conventional approaches, however efficacious, are resource-demanding and necessitate specialized training, hence constraining wider participation in archaeological data. AI-driven chatbots serve as a -recent- revolutionary approach by automating data processing and offering natural language interfaces for enhanced accessibility and interpretation [2]. Platforms like those of Google Earth Engine (GEE) have proven the viability of cloud-based geospatial analysis, so reinforcing the contribution of AI to the progression of archaeological research [1].