%0 Journal Article
%T 基于深度学习的景观感知评价研究现状及趋势分析<br>Research Status and Trend Analysis of Landscape Perception Evaluation Based on Deep Learning
%A 徐展鹏
%A 唐真
%A 沈剑
%J Design
%P 503-509
%@ ****-****
%D 2024
%I Hans Publishing
%R 10.12677/design.2024.92238
%X 深度学习技术已在风景园林领域证明了其重要价值，尤其是在景观感知评价和数字化规划设计中显示出广泛的研究和应用前景。该技术能够通过多层次、多阶段的表示学习从大规模数据集中提取复杂特征，有助于构建先进的景观评价和分析模型，从而大幅提升模型性能和适用性。然而，并行的是，技术与实践的发展将可能带来新的挑战，需要研究者和从业者保持对新发展方向的关注和适应。<br />
Deep learning technology has demonstrated its significant value in the field of landscape architecture, particularly in terms of landscape perception evaluation and digital planning and design, showing a broad range of research and application prospects. The technology is capable of extracting complex features from large-scale datasets through multi-level, multi-stage representation learning, which aids in constructing advanced landscape evaluation and analysis models, thereby greatly enhancing model performance and applicability. However, parallel to this progress, the development of technology and practice may bring new challenges, necessitating that researchers and practitioners maintain their focus on and adapt to emerging directions of development.
%K 深度学习，机器学习，人工智能，景观格局，景观评价，景观感知<br>Deep Learning
%K Machine Learning
%K Artificial Intelligence
%K Landscape Pattern
%K Landscape Evaluation
%K Landscape Perception
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=85196