  We propose a network for semantic mapping called the Dense Dilated
Convolutions Merging Network (DDCM-Net) to provide a deep learning approach
that can recognize multi-scale and complex shaped objects with similar color
and textures, such as buildings, surfaces/roads, and trees in very high
resolution remote sensing images. The proposed DDCM-Net consists of dense
dilated convolutions merged with varying dilation rates. This can effectively
enlarge the kernels' receptive fields, and, more importantly, obtain fused
local and global context information to promote surrounding discriminative
capability. We demonstrate the effectiveness of the proposed DDCM-Net on the
publicly available ISPRS Potsdam dataset and achieve a performance of 92.3%
F1-score and 86.0% mean intersection over union accuracy by only using the RGB
bands, without any post-processing. We also show results on the ISPRS Vaihingen
dataset, where the DDCM-Net trained with IRRG bands, also obtained better
mapping accuracy (89.8% F1-score) than previous state-of-the-art approaches.
