Available big geoscientific data and modern powerful computation hardware have laid a solid foundation for the prevailing deep learning models in the field of image classification, detection and segmentation. In these models, fully convolutional networks achieve unprecedented success in image segmentation tasks [6]. In this paper, we apply the contemporary image segmentation models in the context of extracting buildings and roads from high spatial resolution imagery. We estimate the influence of filter stride, learning rate, input data size, training epoch and fine-tuning on model performance. Selected Massachusetts road and building datasets are used for training, validation, and testing the performance of the models with different parameters. As a result of combining shallow fine-grained pooling layer outputs with the deep final-score layer or abandoning coarse-grained pooling layers, the extraction precision rate of the best modified model improves significantly to over 78%.