AI remote sensing identification model for artificially disturbed patches in Longyan,Fujian province

doi:10.13474/j.cnki.11-2246.2026.0520

Abstract

Abstract: [Purposes]In response to the monitoring needs of artificially disturbed land in Longyan,Fujian province,optimize the architecture of the AI remote sensing recognition model for artificially disturbed land and apply it to promote sustainable development of the ecological environment.[Methods]This research constructs a artificially disturbed sample based on 2 m high-resolution imagery from Longyan,Fujian,using six model architectures:DeepLabV3+,PAN,SegFormer,U-Net++,SCSE-UNet,and TransUNet,to develop AI recognition models and conduct remote sensing intelligent recognition and analysis.[Findings]TransUNet has the best overall performance,with an intersection over union of 0.75 and an F1-Score of 0.84,SegFormer follows closely.In the recognition application in Longyan city in 2024,the overall accuracy (OA)of TransUNet is 0.99,the Kappa coefficient is 0.87,the producer's accuracy (Q_pa) is 0.95,and the user's accuracy (Q_ua) is 0.81,achieving superior recognition accuracy and contour detail depiction.[Conclusions]The identification results of TransUNet align with the actual human activity space,with disturbances concentrated in urbanization construction,mining,and along road construction lines,providing efficient and accurate technical support for dynamic monitoring of soil and water conservation in Longyan city.

Key words: AI, high resolution, artificial disturbance, model recognition

CLC Number:

GU Zhujun, LIU Jia, MAI Xianzhi, WU Jiasheng, YUE Hui, LIN Gengen, HE Yanzi, CAO Zhengjin, LIAO Guanghui. AI remote sensing identification model for artificially disturbed patches in Longyan,Fujian province[J]. Bulletin of Surveying and Mapping, 2026, 0(5): 122-127,142.

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