How to accurately extract large-scale urban land? Establishment of an improved fully convolutional neural network model

Abstract

Realizing accurate perception of urban boundary changes is conducive to the formulation of regional development planning and researches of urban sustainable development. In this paper, an improved fully convolution neural network was provided for perceiving large-scale urban change, by modifying network structure and updating network strategy to extract richer feature information, and to meet the requirement of urban construction land extraction under the background of large-scale low-resolution image. This paper takes the Yangtze River Economic Belt of China as an empirical object to verify the practicability of the network, the results show the extraction results of the improved fully convolutional neural network model reached a precision of kappa coefficient of 0.88, which is better than traditional fully convolutional neural networks, it performs well in the construction land extraction at the scale of small and medium-sized cities.

Keywords

improved fully convolutional neural network / remote sensing image classification / city boundary / precision evaluation.

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