英文摘要 |
Visual Landscape Classification (VLC) is the categorization of landscape resources based on visual features. A good classification system can facilitate subsequent planning and design and make increase resource management efficiency more efficient. Litton has conducted a series of studies in the U.S. Forest Service since from 1968, to establish establishing a visual landscape classification and evaluation method, and its classification system is quite representative. This study attempts to train the an artificial intelligence model of of Litton’s visual landscape classificationVLC system with using deep learning. The use of , with the deep learning aims to of reduceing the manpower requirements of associated with visual landscape resource surveyance and as well asand to increaseing the consistency of judgment standards. The training method uses transfer learning to train the model, and the. The results show that indicate a model the accuracy of the model reaches up to 80%, which is a classification model that can be indicating that the model can be practically applied in the field. This model, named Litton7 (https://github.com/lichihho/Litton7.git), has the potential for future improvements by incorporating multi-class training, making it more amenable to environment classifications. In the future, the model can be improved to encompass multi-class training, so that it can be more in line with themaking it more amenable to human habit of classifying the environment classification. Litton7 can be obtained from the following website: (https://github.com/lichihho/Litton7.git). |