Identification of traditional settlement landscape areas in Hainan based on unmanned aerial remote sensing imagery

Abstract

Abstract To enhance the accuracy of recognizing traditional settlement landscapes in Hainan, this study introduces a landscape recognition model predicated on a full convolutional neural network (FCN). The research delineates the selection of specific types and sensors for Unmanned Aerial Vehicles (UAVs), employing UAV remote sensing technology to capture image data of Hainan's traditional settlement landscapes. The collected initial image data, laden with interference information, underwent a preprocessing step. On this foundation, the landscape recognition model for Hainan's traditional settlements was developed utilizing the FCN. The empirical results derived from deploying the model reveal the presence of ancient buildings within a 400-meter radius of the principal river. Notably, these structures predominantly span a distance ranging from 100 to 350 meters. The spatial distribution pattern of these ancient edifices notably centers around the Zong ancestral hall. Furthermore, when compared to other benchmark models, the proposed FCN model exhibits superior performance in recognizing forest, grassland, and farmland within the Hainan settlement landscape, achieving average recognition rates of 88.66% and 84.91%, respectively. This investigation underscores the significant potential for applying UAV remote sensing technology in identifying traditional settlement landscapes. It provides pivotal technical support and a reference point for the survey of forest resources and ecological monitoring, thereby enhancing the applicability and dissemination of UAV technology in landscape recognition tasks.