Locating Waterfowl Farms from Satellite Images with Parallel Residual U-Net Architecture

For the epidemic prevention of avian influenza, there exist lots of differences between ideality and reality. This is why the epidemic is usually out of control. One of the reasons is that many illegal waterfowl farms are built without government registration. In this work, we proposed a new method trying to directly locate waterfowl farms, including both registered and unregistered ones without the need of human labeling. This will not only save human labors, but also update the location and size information of waterfowl farms regularly due to the computing speed of computers. In this work, we proposed a new method for satellite image augmentation. The layers of the model we proposed are not deeper than the other deep neural network models. However, we show that using the existing simple U-Net combined with residual blocks has better performance than the other deep models in this task.