Deep-learning-based extraction of the animal migration patterns from weather radar images

Continental coverage and year-round operation of the weather radar networks provide an unprecedented opportunity for studying large-scale airborne migration. The broad and local-scale airborne information collected by these infrastructures can answer many ecological questions. However, extracting and interpreting the biological information from such massive weather radar data remains an intractable problem. Recently, many big-data problems have been solved using the deep learning technology. In this study, the biological information in the weather radar data is identified using the advanced deep learning method. The proposed method consists of two main parts, i.e., a rendering and casting procedure and an image segmentation procedure based on a convolutional neural network. The biological data are automatically extracted by rendering and mapping, image segmentation, and result masking. By analyzing the typical radar data from single and multiple stations, we partly reveal the intensity and speed of the migration pattern. We present the first feasibility study of the extraction of local and large-scale biological phenomena from the Chinese weather radar network data.