AI-Accelerated Decision Making in Forest Management

Abstract This paper is a continuation of the research on application artificial intelligence in counting trees with use methods for automatic analysis photogrammetric data forests continental region. devoted to AI accelerating decision making processes forest management. It also discusses how RGB imagery from drones could replace aerial and satellite hyperspectral automatically detect unhealthy dead trees. Experimental was conducted verify whether Faster R-CNN can classify snag weakened by diseases data, enabling quick response forest-threatening factors. The based taken areas districts subordinate Regional Directorate State Forests Zielona Góra. Non-metric collected small airplanes container postprocessed respect constraints. results show that specific cases be replaced orthomosaics order decrease time needed forestry treatments.