Abstract
When a landslide happens, it is important to recognize the hazard-affected bodies surrounding the landslide for the risk assessment and emergency rescue. In order to realize the recognition, the spatial relationship between landslides and other geographic objects such as residence, roads and schools needs to be defined. Comparing with semantic segmentation and instance segmentation that can only recognize the geographic objects separately, image captioning can provide richer semantic information including the spatial relationship among these objects. However, the traditional image captioning methods based on RNNs have two main shortcomings: the errors in the prediction process are often accumulated and the location of attention is not always accurate which would lead to misjudgment of risk. To handle these problems, a landslide image interpretation network based on a semantic gate and a bi-temporal long-short term memory network (SG-BiTLSTM) is proposed in this paper. In the SG-BiTLSTM architecture, a U-Net is employed as an encoder to extract features of the images and generate the mask maps of the landslides and other geographic objects. The decoder of this structure consists of two interactive long-short term memory networks (LSTMs) to describe the spatial relationship among these geographic objects so that to further determine the role of the classified geographic objects for identifying the hazard-affected bodies. The purpose of this research is to judge the hazard-affected bodies of the landslide (i.e., buildings and roads) through the SG-BiTLSTM network to provide geographic information support for emergency service. The remote sensing data was taken by Worldview satellite after the Wenchuan earthquake happened in 2008. The experimental results demonstrate that SG-BiTLSTM network shows remarkable improvements on the recognition of landslide and hazard-affected bodies, compared with the traditional LSTM (the Baseline Model), the BLEU1 of the SG-BiTLSTM is improved by 5.89%, the matching rate between the mask maps and the focus matrix of the attention is improved by 42.81%. In conclusion, the SG-BiTLSTM network can recognize landslides and the hazard-affected bodies simultaneously to provide basic geographic information service for emergency decision-making.
Highlights
Landslides occurring in different places will cause different levels of hazard
We input these samples into the well-trained SG-BiTLSTM network to predict landslides and their hazard-affected bodies, so that a data support can be provided to the emergency decision-maker
A key differentiation between classic pixel-based approaches and Geographic object-based image analysis (GeoBIA) is that GeoBIA incorporates the wisdom of the user into its frameworks, i.e., it uses semantics to translate image-objects into real-world features [71], so we believe that the proposed network absorbs the idea of GeoBIA
Summary
Landslides occurring in different places will cause different levels of hazard. For example, landslides which occur in densely populated areas are more harmful than those in uninhabited areas (Figure 1). The objects around landslides are named hazard-affected bodies, which geographic objects. The objects around landslides are named hazard-affected bodies, which are are recognized through the spatial relationship between landslides other geographic objects. On the basis of the above researches, we method proposed a the remote sensing the basis of the above researches, we proposed a novel novel method to recognize landslides and hazard-affected bodies simultaneously. An to recognize landslides and hazard-affected bodies simultaneously In this method, an LSTM network network was employed to extract the relationship among the geographic objects, thena combined was LSTM employed to extract the relationship among the geographic objects, combined it with mask it with a mask of landslides generated from a.
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