Multi-label Contextual Operator Inference in Knowledge Acquisition for Building Generalization: A Multi-modal Learning Approach

Abstract

In traditional map generalization, knowledge acquisition refers to formalizing individual generalization operators and chaining multiple operators into an overall process. It is an essential step towards automating map generalization. To date, machine learning, including advanced deep learning (DL), has been frequently applied to implement individual operators or end-to-end solutions, reducing or even bypassing the intensive effort required to elicit generalization knowledge from expert cartographers. For the example of the generalization of buildings in topographic maps, we focus on the inverse process, that is, how cartographic knowledge acquisition can be performed in DL-based map generalization, a task that has received little attention so far. In particular, we address process recognition and modeling and formulate the decision of whether or not multiple contextual generalization operators, including enlargement, aggregation, typification, and displacement, should be applied to a particular building as a multi-label contextual operator inference task. Instead of using either raster or vector maps to learn map generalization, we propose a multi-modal learning approach to explore the capacity of integrating both map data modalities. We evaluated the approach with national multi-scale map production data in Switzerland, for the transition from 1:25,000 to 1:50,000. The results show that trained DL models achieved F1 scores greater than 0.80 for operator inference except for typification, indicating the promising capacity of DL approaches to acquire procedural knowledge on building generalization. Furthermore, a closer evaluation by operator combinations demonstrates that the multi-modal approach particularly outperforms uni-modal learning in process recognition and modeling involving multiple operators.