High-Resolution Land-Use Mapping in Beijing-Tianjin-Hebei Region Based on Convolutional Neural Network

The land-use mapping of Beijing-Tianjin-Hebei region (Jingjinji) covers an area of over 200,000 km2. Faced with a large number of objects in tiny size, which is quite difficult for land-use mapping, the land-use mapping of Jingjinji requires a certain design of convolutional neural network (CNN) [1] (Lecun et al. Neural Comput 1(4):541–551, [2]. In this paper, we propose a feature pyramid fusion network with attention mechanism. The module is designed based on the pyramid scene parsing network (PSPNet) (Zhao et al. [3]), and two improvements are made for the large-scale high-resolution land-use mapping task: (1) Since PSPNet concatenate features in a non-selective way at the center of the module, we propose the attention feature pyramid fusion (AFPF) block, which can selectively fuse features with different scales; (2) In order to make the border of results more precise and the tiny objects more accurate, we use an encode–decode structure to merge low-level features and high-level features at the upsample stage. During each fusion operation, the attention mechanism is employed. The final experiment proves that the network designed in this paper performs better in the accuracy of small objects and precise border with respect to the original PSPNet in the land-use mapping task of Jingjinji.