Urban forest analysis: species classification using machine learning and remote sensing data

Abstract

Effective management of urban forests requires an integrated approach, starting with a complete inventory of their biodiversity. At the moment, data on the floristic composition of urban forests in Siberian cities is either limited or fragmentary. The purpose of this study is to classify urban forests by species and determine their ontogenetic state using remote sensing materials. This study aims to deeply analyze the structure of urban forests using remote sensing data, in particular the use of unmanned aerial vehicles.