Urban high-density area outdoor thermal comfort evaluation method based on deep learning three-dimensional reconstruction

Abstract

The invention discloses an urban high-density area outdoor thermal comfort evaluation method based on deep learning three-dimensional reconstruction. The method comprises the steps of acquiring urbanhigh-density area remote sensing image data; preprocessing the urban high-density area remote sensing image data under a Python integration platform to obtain a remote sensing image data set; establishing a neural network model based on CNN-LSTM, and training the model by adopting a stochastic gradient descent method; inputting the remote sensing image data set into a trained neural network modelto obtain an urban high-density region three-dimensional reconstruction model; performing computer simulation on the urban high-density area three-dimensional information model to generate an urban high-density area environment information model; and carrying out simulation analysis on the urban high-density area environment information model by virtue of ENVI-met, and carrying out outdoor thermalcomfort evaluation according to a data simulation analysis result. The method solves the bottleneck problems of building environment information modeling precision and efficiency in urban high-density area outdoor thermal comfort evaluation.

Description

technical field [0001] The invention relates to the field of deep learning technology, in particular to a method for evaluating outdoor thermal comfort in urban high-density areas based on three-dimensional reconstruction of deep learning. Background technique [0002] With the acceleration of urbanization, the heat island effect in urban high-density areas is intensified. Efficient and accurate evaluation of outdoor thermal comfort in urban high-density areas plays an important role in guiding urban planning and urban design. Existing thermal comfort evaluation methods in urban high-density areas need to establish an outdoor thermal comfort evaluation model based on urban building approval data and field measurement data, which has low modeling efficiency, easy loss of building surrounding environment information, and delayed update of existing building renovation and expansion These problems affect the accuracy and efficiency of thermal comfort evaluation in urban high-de...

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