@inproceedings{Contreras19_AEF,
    type = {inproceedings},
    key = {Contreras19_AEF},
    title = {Automatically Estimating Forestal Characteristics in 3D Point Clouds using Deep Learning},
    author = {Jhonatan Contreras and Sven Sickert and Joachim Denzler},
    booktitle = {iDiv Annual Conference},
    year = {2019},
    month = {August},
    note = {Poster},
    abstract = {Biodiversity changes can be monitored using georeferenced and multitempo-ral data. Those changes refer to the process of automatically identifying differ-ences in the measurements computed over time. The height and the Diameterat Breast Height of the trees can be measured at different times. The mea-surements of individual trees can be tracked over the time resulting in growthrates, tree survival, among other possibles applications. We propose a deeplearning-based framework for semantic segmentation, which can manage largepoint clouds of forest areas with high spatial resolution. Our method divides apoint cloud into geometrically homogeneous segments. Then, a global feature isobtained from each segment, applying a deep learning network called PointNet.Finally, the local information of the adjacent segments is included through anadditional sub-network which applies edge convolutions. We successfully trainand test in a data set which covers an area with multiple trees. Two addi-tional forest areas were also tested. The semantic segmentation accuracy wastested using F1-score for four semantic classes:leaves(F1 = 0.908),terrain(F1 = 0.921),trunk(F1 = 0.848) anddead wood(F1 = 0.835). Furthermore,we show how our framework can be extended to deal with forest measurementssuch as measuring the height of the trees and the DBH.},
    groups = {3dpcl_semseg},
    url = {https://elib.dlr.de/133241/},
}