The Role of Artificial Intelligence–Powered Tools in Translating English Maps Produced by Geographic Information Systems (GIS) into Arabic: A Case Study of Baghdad City

Abstract

In recent years, Geographic Information Systems (GIS) applications have witnessed significant advancements as a result of integrating Artificial Intelligence (AI) techniques into spatial analysis processes and digital map production. With the growing global use of digital maps, the need has emerged to translate map elements originally produced in English into other languages, particularly Arabic. However, this process faces several technical and linguistic challenges, including the complexity of geographical terminology, right-to-left text orientation, and the preservation of the cartographic structure of spatial data.

This study aims to analyze the role of AI-powered tools in improving the quality of translating GIS-generated digital maps from English into Arabic. To achieve this objective, three translation approaches were compared: traditional manual translation, translation using Neural Machine Translation (NMT) systems, and a hybrid approach combining artificial intelligence with specialized geographical dictionaries. In addition, a quantitative model was developed to evaluate cartographic translation quality, termed the Map Translation Quality Index (MTQI). The index is based on three main criteria: terminological accuracy of geographical terms, preservation of cartographic structure, and readability after translation.

The model was applied to a set of digital maps of Baghdad City, including road network maps, land-use maps, and administrative unit maps. The results revealed that the AI-supported hybrid model achieved the highest level of translation quality compared with the other approaches. Furthermore, it significantly reduced the time required to complete the translation process. The study recommends integrating AI technologies into GIS environments within Arab institutions and developing a standardized AI-supported Arabic geographical lexicon to enhance the efficiency and accuracy of digital map translation in the future.