  Precision mapping of landslide inventory is crucial for hazard mitigation.
Most landslides generally co-exist with other confusing geological features,
and the presence of such areas can only be inferred unambiguously at a large
scale. In addition, local information is also important for the preservation of
object boundaries. Aiming to solve this problem, this paper proposes an
effective approach to fuse both local and non-local features to surmount the
contextual problem. Built upon the U-Net architecture that is widely adopted in
the remote sensing community, we utilize two additional modules. The first one
uses dilated convolution and the corresponding atrous spatial pyramid pooling,
which enlarged the receptive field without sacrificing spatial resolution or
increasing memory usage. The second uses a scale attention mechanism to guide
the up-sampling of features from the coarse level by a learned weight map. In
implementation, the computational overhead against the original U-Net was only
a few convolutional layers. Experimental evaluations revealed that the proposed
method outperformed state-of-the-art general-purpose semantic segmentation
approaches. Furthermore, ablation studies have shown that the two models
afforded extensive enhancements in landslide-recognition performance.
