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In recent years, deep learning techniques (e.g., U-Net, DeepLab) haveachieved tremendous success in image segmentation. The performance of thesemodels heavily relies on high-quality ground truth segment labels.Unfortunately, in many real-world problems, ground truth segment labels oftenhave geometric annotation errors due to manual annotation mistakes, GPS errors,or visually interpreting background imagery at a coarse resolution. Suchlocation errors will significantly impact the training performance of existingdeep learning algorithms. Existing research on label errors either modelsground truth errors in label semantics (assuming label locations to be correct)or models label location errors with simple square patch shifting. Thesemethods cannot fully incorporate the geometric properties of label locationerrors. To fill the gap, this paper proposes a generic learning framework basedon the EM algorithm to update deep learning model parameters and infer hiddentrue label locations simultaneously. Evaluations on a real-world hydrologicaldataset in the streamline refinement application show that the proposedframework outperforms baseline methods in classification accuracy (reducing thenumber of false positives by 67% and reducing the number of false negatives by55%).

Deep Learning for Earth Image Segmentation based on Imperfect Polyline Labels with Annotation Errors