arXiv Computer Vision
Bayesian Fusion of Active Contour Models and ConvNet Priors for Standing Dead Tree Segmentation
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SilvaScenes: Tree Detection and Species Classification from Under-Canopy Images in Natural Forests
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Segmentation of Bovid Dentition Under Imperfect Annotations: A Comparative Study of Convolutional and Attention Models
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FSANet: Frequency-Spatial Aware Network for Image Segmentation
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Learning Woody Clearing With Loss Alignment for Zero-Shot Regrowth and Woody Segmentation
The paper presents a deep learning approach for detecting woody clearing using bitemporal Sentinel‑2 imagery from New South Wales, Australia. By introducing a loss‑scaling coefficient, the authors align the model’s objective with end‑user metrics, boosting precision and recall. They further demonstrate zero‑shot transfer to woody regrowth and segmentation tasks, achieving significant error reductions and high F1 scores through image augmentation and generation techniques.
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