Research on urban open space behavior extraction based on semantic segmentation technology

Using semantic segmentation technology based on a convolutional neural network (CNN) environment, urban open space orthophotos with typical feature segments collected by UAVs are used as the basis of neural network training data, and the U-net semantic segmentation algorithm research framework is used to import remotely sensed images into the algorithm model, encode the data with characterization, strengthen its behavioral features, and finally output the data with behavioral feature information This is used to build a training set of behavioral elements of the urban open space environment. Based on this training set, the training set can be used to classify and identify urban open spaces with similar environmental characteristics, thus quickly building a digital information model of environmental behavior elements in urban open spaces, improving the digital efficiency of environmental behavior research and saving a lot of time and cost for subsequent analysis.