论文标题
朝空间变异性意识到深神经网络（SVANN）：结果摘要
Towards Spatial Variability Aware Deep Neural Networks (SVANN): A Summary of Results
论文作者
论文摘要
在许多地质酚类中都观察到了空间可变性，包括气候区，USDA植物野性区和陆地栖息地类型（例如，森林，草原，湿地和沙漠）。但是，当前的深度学习方法遵循一种空间上的大小适中（OSFA）方法，以训练无法解释空间可变性的单个深神网络模型。在这项工作中，我们提出并研究了空间变异性意识到深度神经网络（SVANN）方法，其中为每个地理区域构建了独特的深层神经网络模型。我们使用来自两个地理区域的空中图像来评估这种方法，以绘制城市花园的任务。实验结果表明，Svann在精确度，召回和F1得分方面提供了比OSFA更好的性能，以识别城市花园。
Spatial variability has been observed in many geo-phenomena including climatic zones, USDA plant hardiness zones, and terrestrial habitat types (e.g., forest, grasslands, wetlands, and deserts). However, current deep learning methods follow a spatial-one-size-fits-all(OSFA) approach to train single deep neural network models that do not account for spatial variability. In this work, we propose and investigate a spatial-variability aware deep neural network(SVANN) approach, where distinct deep neural network models are built for each geographic area. We evaluate this approach using aerial imagery from two geographic areas for the task of mapping urban gardens. The experimental results show that SVANN provides better performance than OSFA in terms of precision, recall,and F1-score to identify urban gardens.
