Optimum-Path Forest Ensembles to Estimate the Internal Decay in Urban Trees

Giovani Candido, Luis Henrique Morelli, Danilo Samuel Jodas, Giuliana Del Nero Velasco, Reinaldo Araújo de Lima, Kelton Augusto Pontara da Costa, João Paulo Papa

Abstract: Research on urban tree management has recently grown to include various studies using machine learning to address the tree’s risk of falling. One significant challenge is to assess the extent of internal decay, a crucial factor contributing to tree breakage. This paper uses machine and ensemble learning algorithms to determine internal trunk decay levels. Notably, it introduces a novel variation of the Optimum-Path Forest (OPF) ensemble pruning method, OPFsemble, which incorporates a “count class” strategy and performs weighted majority voting for ensemble predictions. To optimize the models’ hyperparameters, we employ a slime mold-inspired metaheuristic, and the optimized models are then applied to the classification task. The optimized hyperparameters are used to randomly select distinct configurations for each model across ensemble techniques such as voting, stacking, and OPFsemble. Our OPFsemble variant is compared to the original one, which serves as a baseline. Moreover, the es

External IDs:dblp:conf/visigrapp/CandidoMJVLCP25