LEARNED MODEL FOR BUILDING REGION EXTRACTION

摘要

To solve the problem in which: boundaries of building regions are likely to be unclear when applying a learned model configured with neural networks using extended convolution operation to building region extraction.SOLUTION: A building determination model is a learned model that causes a computer to be functioned to extract a building region where a building exists from an image taken from the sky. The building determination model comprises an input layer which is an image, and a feature extraction layer. In the feature extraction layer, a plurality of types of convolution layers having different expansion coefficients are stacked, wherein the convolution layers respectively perform an extended convolution operation. The building determination model is configured with a neural network that outputs a building probability image. The building probability image has a pixel value which is the building existence probability. The feature extraction layer is a plurality of convolution layers following the input layer. The feature extraction layer comprises a front end unit whose expansion coefficient increases to the maximum value in the feature extraction layer according to the arrangement order of the convolution layer. The feature extraction layer comprises a local feature extraction unit which is a plurality of convolution layers following the front end unit, wherein the expansion coefficient decreases according to the arrangement order of the convolution layer.SELECTED DRAWING: Figure 6