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Automatic building extraction from aerial imagery has several applications in urban planning, disaster management, and change detection. In recent years, several works have adopted deep convolutional neural networks (CNNs) for building extraction, since they produce rich features that are invariant against lighting conditions, shadows, etc. Although several advances have been made, building extraction from aerial imagery still presents multiple challenges. Most of the deep learning segmentation methods optimize the per-pixel loss with respect to the ground truth without knowledge of the context. This often leads to imperfect outputs that may lead to missing or unrefined regions. In this work, we propose a novel loss function combining both adversarial and cross-entropy losses that learn to understand both local and global contexts for semantic segmentation. The newly proposed loss function deployed on the DeepLab v3+ network obtains state-of-the-art results on the Massachusetts buildings dataset. The loss function improves the structure and refines the edges of buildings without requiring any of the commonly used post-processing methods, such as Conditional Random Fields. We also perform ablation studies to understand the impact of the adversarial loss. Finally, the proposed method achieves a relaxed F1 score of 95.59% on the Massachusetts buildings dataset compared to the previous best F1 of 94.88%.
Convolutional neural networks for semantic segmentation suffer from low performance at object boundaries. In medical imaging, accurate representation of tissue surfaces and volumes is important for tracking of disease biomarkers such as tissue morpho
Semantic segmentation for aerial imagery is a challenging and important problem in remotely sensed imagery analysis. In recent years, with the success of deep learning, various convolutional neural network (CNN) based models have been developed. Howe
Food resources face severe damages under extraordinary situations of catastrophes such as earthquakes, cyclones, and tsunamis. Under such scenarios, speedy assessment of food resources from agricultural land is critical as it supports aid activity in
Histopathology has played an essential role in cancer diagnosis. With the rapid advances in convolutional neural networks (CNN). Various CNN-based automated pathological image segmentation approaches have been developed in computer-assisted pathologi
We propose a 4D convolutional neural network (CNN) for the segmentation of retrospective ECG-gated cardiac CT, a series of single-channel volumetric data over time. While only a small subset of volumes in the temporal sequence is annotated, we define