DO-UNet, DO-LinkNet: UNet, D-LinkNet with DO-Conv for the Detection of Settlements without Electricity Challenge

Abstract

In this paper, two semantic segmentation models, DO-UNet and DO-LinkNet, are presented for the detection of human settlements, and a threshold-based model is proposed to detect areas with electricity. In DO-UNet and DO-LinkNet, the conventional convolutional layer is replaced with depthwise over-parameterized convolutional layer. Also, an extra pooling operation is carried out in the last layer since the size of the input images is different from that of the labels. Depthwise over-parameterized convolutional layer enhances the convolutional layer with an additional depthwise convolution. Pooling operation can accelerate training speed, increase the receptive field in feature extraction, and reduce the requirement of network complexity. In the detection of settlements without electricity challenge track, our best F1-score on the validation set and the test set are 0.8820 and 0.8798, respectively.