Vegetation density estimation in the wild

Abstract

Remote sensing has revolutionized the efficiency of vegetation mapping, but such techniques remain impractical for mapping some types of flora over relatively limited spatial extents. We propose a deep-learning based framework for automated detection and planar mapping of an epiphytic plant in a forest from geotagged static imagery using inexpensive cameras. Our pipeline consists of two steps: segmentation and spatial distribution estimation. We evaluate several segmentation methods on a novel dataset of roughly 375 outdoor images with per-pixel labels indicating the presence of Spanish moss. We also evaluate the accuracy of the spatial distribution estimates with respect to field measurements by ecologists for Spanish moss.