Housing data spatialization research based on remote sensing images for rapid loss assessment after earthquakes

doi:10.13474/j.cnki.11-2246.2024.0212

Abstract

Abstract: The convolutional neural network method can efficiently extract housing vector data from high-resolution remote sensing images, quickly obtain spatialization data of housing data, and improve the updating ability of earthquake emergency database. Based on the contour-guided and local structure-aware encoder-decoder network(CGSANet) model and the equal scale grid sampling method on the basis of partition, this paper establishes the spatialization model of housing construction area and housing structure types, and achieves spatialization of multi-type housing data in complex regional backgrounds. Taking Huangmei county as the study area, the model of housing data spatialization(1 km×1 km) is constructed, and the ability to identify housing data of different structural types is achieved. The model of housing data spatialization constructed can be used to update the earthquake emergency database, and is of great significance for improving the accuracy and timeliness of housing data.

Key words: remote sensing image, convolutional neural networks, housing data, spatialization, grid sampling

CLC Number:

P237

ZHANG Ping, LI Bijun, LI Yin, ZHANG Yimei, Temuqile, LIU Ke, LI Zhijun. Housing data spatialization research based on remote sensing images for rapid loss assessment after earthquakes[J]. Bulletin of Surveying and Mapping, 2024, 0(2): 69-73.

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