相关实验视频
Updated: Jul 22, 2026
13:19
Deep Neural Networks for Image-Based Dietary Assessment
Published on: March 13, 2021
9.0K
基于深度学习的生态-农业-城市空间的评估方法
Anqi Li1, Zhenkai Zhang2, Zenglin Hong3,4,5
1School of Land Engineering, Chang'an University, Xi'an, 710054, China.
Scientific reports
|May 18, 2024
概括
本研究引入了一个深度学习模型 (SARes-NET) 来评估生态-农业-城市空间,这对可持续发展和土地利用规划至关重要.
科学领域:
- 环境科学 环境科学
- 城市规划 城市规划
- 农业科学 农业科学
背景情况:
- 全球人口增加和土地退化需要平衡城市发展,粮食安全和生态保护.
- 协调这些要素对于实现可持续发展目标至关重要.
- 评估综合生态 - 农业 - 城市 (E-A-U) 空间对于有效的土地管理至关重要.
研究的目的:
- 开发和验证一个先进的深度学习模型来评估E-A-U空间.
- 将模型应用于中国的林市,作为一个代表性的案例研究.
- 证明该模型在空间评估中的优越性,而不是传统方法.
主要方法:
- 开发了一个基于"双重评估"框架的自我注意残留神经网络 (SARes-NET) 模型.
- 应用了SARes-NET模型来评估玉林市的EAU空间.
- 针对物流回归,天真贝叶斯,GBDT,RF和ANN模型进行了比较验证.
主要成果:
- 与其他五种模型相比,SARes-NET模型表现出优异的模拟性能.
- 该模型有效地捕获了E-A-U空间数据中的复杂非线性关系.
- 空间分析显示,林市西北部和东南部的城市/生态地区有着明显的农业主导地位.
结论:
- 以深度学习为指导的E-A-U空间评估为国家空间规划提供了一种创新的方法.
- SARes-NET提供了一种可靠的方法来评估土地使用适宜性和生态-城市-农业相互作用.
- 这种方法对国家一级的领土评估和可持续发展战略有重大影响.
相关概念视频
01:17Design Example: Analyzing Capacity Contours for Flood Risk Assessment
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
01:21Manipulation and Analysis
GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...