Abstract
One significant assessing criteria of climate change is geometric evolution. The rate of evolution reveals the speed that environment worsens. Advanced space mirror monitors that and generates images timely. However, it might be difficult for human to deal with collected numerous image-related data. In previous research, convolutional neural network is regarded to have specific advantage in resolving image recognition tasks. Hence, a new type of convolutional neural network model is applied to identify different kinds of landscape. Virtually, this model is called Efficient Net which based on landscape recognition dataset with 5 classes of landscapes. The study also introduces the fine-tuning to further improve the performance of the model. To evaluate the model, the precision, recall, F1 score, accuracy and loss are adopted as assessing criteria. The results shows that the model predicts the target dataset to a great extent. However, it has been tested that the class of mountain might not be suitable for predicting because of vague criterion. That is helpful in real-condition geographical applications and environmental governance.
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