Evaluating AI-Driven Automated Map Digitization in QGIS
This work addresses the need for reduced human involvement in map digitization for GIS users, but it appears incremental as it focuses on evaluating an existing tool rather than introducing new methods.
This research tackled the problem of automating map digitization by evaluating the Deepness AI tool in QGIS, comparing its outputs from Google Earth imagery with OpenStreetMap data to assess performance, though no concrete numbers on accuracy or efficiency were provided in the abstract.
Map digitization is an important process that converts maps into digital formats that can be used for further analysis. This process typically requires a deep human involvement because of the need for interpretation and decision-making when translating complex features. With the advancement of artificial intelligence, there is an alternative to conducting map digitization with the help of machine learning techniques. Deepness, or Deep Neural Remote Sensing, is an advanced AI-driven tool designed and integrated as a plugin in QGIS application. This research focuses on assessing the effectiveness of Deepness in automated digitization. This study analyses AI-generated digitization results from Google Earth imagery and compares them with digitized outputs from OpenStreetMap (OSM) to evaluate performance.