In view of the characteristics of UHV transmission and transformation projects such as long lines,many construction sections, scattered disturbed areas,serious human-induced soil erosion and difficult supervision and management,in order to explore the disturbed areas of construction projects and consider the rapid monitoring methods of soil erosion,this paper took Yuheng-Weifang Transmission and Transformation Project area as the research area and used Convolutional Neural Network Algorithm and Gaofen-2 satellite remote sensing image to study the method of fast and automatic identification of disturbed areas of UHV tower foundation and accurate extraction of disturbed areas.33 sampling points (tower foundations) such as plain crop area,mountain forest area,hilly grassland and plain grassland were selected for identification and classification calculation.The results show that the Convolutional Neural Network Algorithm and Gaofen-2 remote sensing image can be used to quickly identify the construction disturbance area (range) and accurately extract the construction disturbance area.The results are basically consistent with the visual interpretation results.Compared with the measured values of disturbance area,the maximum relative error is 11.77% and the minimum value is 1.20%.
development and construction projects
monitoring of soil erosion
UHV tower foundation
Convolutional NeuralNetwork Algorithm