  Change detection is the study of detecting changes between two different
images of a scene taken at different times. By the detected change areas,
however, a human cannot understand how different the two images. Therefore, a
semantic understanding is required in the change detection research such as
disaster investigation. The paper proposes the concept of semantic change
detection, which involves intuitively inserting semantic meaning into detected
change areas. We mainly focus on the novel semantic segmentation in addition to
a conventional change detection approach. In order to solve this problem and
obtain a high-level of performance, we propose an improvement to the
hypercolumns representation, hereafter known as hypermaps, which effectively
uses convolutional maps obtained from convolutional neural networks (CNNs). We
also employ multi-scale feature representation captured by different image
patches. We applied our method to the TSUNAMI Panoramic Change Detection
dataset, and re-annotated the changed areas of the dataset via semantic
classes. The results show that our multi-scale hypermaps provided outstanding
performance on the re-annotated TSUNAMI dataset.
