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Google earth with high-resolution imagery basically takes months to process new images before online updates. It is a timeconsuming and slow process especially for post-disaster application. The objective of this research is to develop a fast and effectivemethod of updating maps by detecting local differences occurred over different time series; where only region with differences willbe updated. In our system, aerial images from Massachusetts’s road and building open datasets, Saitama district datasets are used asinput images. Semantic segmentation is then applied to input images. Semantic segmentation is a pixel-wise classification of imagesby implementing deep neural network technique. Deep neural network technique is implemented due to being not only efficient inlearning highly discriminative image features such as road, buildings etc., but also partially robust to incomplete and poorlyregistered target maps. Then, aerial images which contain semantic information are stored as database in 5D world map is set asground truth images. This system is developed to visualise multimedia data in 5 dimensions; 3 dimensions as spatial dimensions, 1dimension as temporal dimension, and 1 dimension as degenerated dimensions of semantic and colour combination dimension. Next,ground truth images chosen from database in 5D world map and a new aerial image with same spatial information but different timeseries are compared via difference extraction method. The map will only update where local changes had occurred. Hence, mapupdating will be cheaper, faster and more effective especially post-disaster application, by leaving unchanged region and only updatechanged region

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

SEMANTIC SEGMENTATION AND DIFFERENCE EXTRACTION VIA TIME SERIES AERIAL VIDEO CAMERA AND ITS APPLICATION