Marco Heurich

Vanessa Suessle, Marco Heurich, Colleen T. Downs et al.

The bilateral asymmetry of flanks of animals with visual body marks that uniquely identify an individual, complicates tasks like population estimations. Automatically generated additional information on the visible side of the animal would improve the accuracy for individual identification. In this study we used transfer learning on popular CNN image classification architectures to train a flank predictor that predicts the visible flank of quadruped mammalian species in images. We automatically derived the data labels from existing datasets originally labeled for animal pose estimation. We trained the models in two phases with different degrees of retraining. The developed models were evaluated in different scenarios of different unknown quadruped species in known and unknown environments. As a real-world scenario, we used a dataset of manually labeled Eurasian lynx (Lynx lynx) from camera traps in the Bavarian Forest National Park to evaluate the model. The best model, trained on an EfficientNetV2 backbone, achieved an accuracy of 88.70 % for the unknown species lynx in a complex habitat.

Jacquelyn A. Shelton, Przemyslaw Polewski, Wei Yao et al.

The stability and ability of an ecosystem to withstand climate change is directly linked to its biodiversity. Dead trees are a key indicator of overall forest health, housing one-third of forest ecosystem biodiversity, and constitute 8%of the global carbon stocks. They are decomposed by several natural factors, e.g. climate, insects and fungi. Accurate detection and modeling of dead wood mass is paramount to understanding forest ecology, the carbon cycle and decomposers. We present a novel method to construct precise shape contours of dead trees from aerial photographs by combining established convolutional neural networks with a novel active contour model in an energy minimization framework. Our approach yields superior performance accuracy over state-of-the-art in terms of precision, recall, and intersection over union of detected dead trees. This improved performance is essential to meet emerging challenges caused by climate change (and other man-made perturbations to the systems), particularly to monitor and estimate carbon stock decay rates, monitor forest health and biodiversity, and the overall effects of dead wood on and from climate change.

Przemyslaw Polewski, Jacquelyn Shelton, Wei Yao et al.

In this paper, we introduce a framework for segmenting instances of a common object class by multiple active contour evolution over semantic segmentation maps of images obtained through fully convolutional networks. The contour evolution is cast as an energy minimization problem, where the aggregate energy functional incorporates a data fit term, an explicit shape model, and accounts for object overlap. Efficient solution neighborhood operators are proposed, enabling optimization through metaheuristics such as simulated annealing. We instantiate the proposed framework in the context of segmenting individual fallen stems from high-resolution aerial multispectral imagery. We validated our approach on 3 real-world scenes of varying complexity. The test plots were situated in regions of the Bavarian Forest National Park, Germany, which sustained a heavy bark beetle infestation. Evaluations were performed on both the polygon and line segment level, showing that the multi-contour segmentation can achieve up to 0.93 precision and 0.82 recall. An improvement of up to 7 percentage points (pp) in recall and 6 in precision compared to an iterative sample consensus line segment detection was achieved. Despite the simplicity of the applied shape parametrization, an explicit shape model incorporated into the energy function improved the results by up to 4 pp of recall. Finally, we show the importance of using a deep learning based semantic segmentation method as the basis for individual stem detection. Our method is a step towards increased accessibility of automatic fallen tree mapping, due to higher cost efficiency of aerial imagery acquisition compared to laser scanning. The precise fallen tree maps could be further used as a basis for plant and animal habitat modeling, studies on carbon sequestration as well as soil quality in forest ecosystems.