绘制绿色城市:对绿色基础设施绘制材料和学习方法的全面审查
Dino Dobrinić1, Mario Miler1, Damir Medak1
1Chair of Geoinformatics, Faculty of Geodesy, University of Zagreb, 10 000 Zagreb, Croatia.
Sensors (Basel, Switzerland)
|January 25, 2025
概括
本综述探讨了绿色基础设施 (GI) 地图化技术,强调了越来越多地使用深度学习方法进行准确的分类和细分. 它分析了55篇论文,强调了改进城市发展战略的自动验证.
科学领域:
- 环境科学 环境科学
- 城市规划 城市规划
- 遥感 遥感 遥感 遥感
背景情况:
- 绿色基础设施 (GI) 对可持续城市发展至关重要.
- 有效地绘制GI需要了解材料和先进的方法.
- 当前的绘图方法需要对传感器,数据和算法进行详细的分析.
研究的目的:
- 审查绿色基础设施绘图的最新情况.
- 分析机器学习和深度学习在GI绘图中的应用.
- 提出一个新的方法,用于在地理标识地图绘制中进行自动验证.
主要方法:
- 使用PRISMA指南进行系统的文献审查.
- 自然语言处理和大型语言模型用于自动选.
- 分析了55篇论文,重点是GI绘图材料和学习方法.
主要成果:
- 与机器学习 (22 篇论文) 相比,在GI绘制 (33 篇论文) 中向深度学习方法的显著转变.
- 在GI绘图中使用的各种传感器和图像数据的识别.
- 展示一种用于地理标识地图的新型自动化验证方法.
结论:
- 深度学习正在成为绿色基础设施制图的统治者.
- 自动化验证方法有望提高GI绘图的准确性.
- 需要进一步的研究来完善自动化验证技术并解决当前的局限性.
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