Investigations of Generative AI Applicability to Error Corrections in Map Information Collection Tool

Kadek Suarjuna Batubulan¹, Nobuo Funabiki¹, Komang Candra Brata¹, I Nyoman Darma Kotama¹, Yan Watequlis Syaifudin², Wen-Chung Kao³, Yi-Fang Lee³

1. ¹ Okayama University
2. ² Politeknik Negeri Malang
3. ³ National Taiwan Normal University

International Conference on Computational Intelligence and Intelligent Systems (CIIS), 2025

Currently, we are developing a map information collection tool to assist a pedestrian navigation system. It collects building names and room numbers using Optical Character Recognition (OCR), geolocation techniques, and web scraping to extract necessary data from websites. Unfortunately, collected data is often incomplete, unstructured, or outdated, which can reduce the overall accuracy and reliability. In this paper, we investigate the accuracy of collected data by this tool through comparisons with reference data obtained from Google Maps API, and the applicability of generative AI models in detecting and correcting errors. For evaluations, we examine classification-based metrics such as precision, recall, and F1-score to measure the consistency and correctness of the collected data.

DOI10.1145/3787256.3787275Copy