Urban Structure Generalization in Multi‐Agent Process by Use of Reactional Agents

This article proposes an improvement of automated cartographic generalization using multi-agent sytems in urban areas. Indeed the AGENT model, whose robustness has been tested and approved through the European project AGENT, gives very good results in generalizing dense urban areas by means of enlargement, removal and displacement of buildings. But this model does not tackle the question of including particular structures like building alignments in the process, which is a crucial issue. The problem is that integrating such structures does not fit into the accurate top-down hierarchy of urban agents. In order to face this problem, we propose to partly re-engineer the model by introducing the concept of reactional agents whose behavior is very different from hierarchical agents of the original model as they use bottom-up activation. In this view, urban alignment is considered to be a reactional agent activated only by its inner buildings, which generalizes the aligned buildings together into one entire structure. Associating reactional alignment behavior with new generalization actions on alignments significantly improves the model and gives better results in dense urban areas. Moreover, the idea could probably be used for other applications.