An artificial intelligence method for text placement evaluation in maps
Abstract: A map is a combinatorial presentation of map objects and labels providing users with sufficient and useful information. Text placement is the most time-consuming and cumbersome work in a map production process, and many methods have been proposed to automate this process. However, text placement evaluation lacks exploration, especially when artificial intelligence (AI) methods are experiencing prosperous development. To bridge the gap, this master thesis proposed an AI method for a text placement evaluation. Firstly, London street maps were simplified and rasterized based on cartographic rules formulated by experienced cartographers. Secondly, a text placement quality measurement schema was designed. Thirdly, datasets were prepared by manually classified rasterized maps into different quality levels. Fourthly, GoogLeNet, a widely accepted convolutional neural network, was trained, and its performance was validated. The results showed the GoogLeNet model’s poor training accuracy and validation accuracy in the training process and bad performance in evaluating text placement. All the test images were classified as having bad text placement. These pieces of evidence indicated that the trained GoogLeNet model was not reliable and qualified for evaluating text placement at this stage. The negative results might be caused by the capacity issue, dataset issues and model issues. These issues were discussed in the latter part of the report. Finally, some limitations and perspectives of using AI for text evaluation were listed in the end for further enlightenment.
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