概率方法融合人工智能生成的地下公用事业映射
Kunle Sunday Oguntoye1, Simon Laflamme1,2, Roy Sturgill1
1Department of Civil, Construction and Environmental Engineering, Iowa State University, Ames, IA 50011, USA.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
这项研究引入了准确的地下公用事业绘图的新框架,将已建的数据与自动生成的地图融合在一起,以降低成本和提高精度. 它强调需要进一步调查的领域,尽量减少挖掘过程中的风险.
科学领域:
- 地理空间工程是什么?
- 地质物理学 地质物理学
- 数据科学数据科学数据科学
背景情况:
- 随着建造的公用事业计划往往含有不准确性,导致在挖掘过程中意外撞击.
- 现有的公用事业调查方法在数据解释复杂性和高成本方面扎,特别是在大型项目中.
- 数据融合提供了更好的准确性,但在实际应用中面临着局限性.
研究的目的:
- 开发一个新的框架,用于准确和具有成本效益的大规模公用事业地图.
- 解决数据解释方面的挑战,并降低实用调查的成本.
- 为确定需要进一步研究的高不确定性区域生成概率推理.
主要方法:
- 自动初始地图创建使用实用推断规则对已识别的相应物.
- 最初地图的数据与即建数据或历史卫星图像的数据融合.
- 使用信心值估计器进行不确定性评估,以生成概率效用图.
主要成果:
- 为快速,低成本的公用事业基础设施绘制一个新的框架.
- 可能的公用事业地图显示埋藏的公用事业位置的可信度水平.
- 确定高不确定性地区作为针对性调查的目标.
结论:
- 拟议的框架通过提供概率推断来提高地下公用事业绘图的准确性.
- 它通过将详细调查限制在关键领域,显著降低了成本并提高了效率.
- 该框架的动态性质允许自动更新,确保长期数据的相关性,并尽量减少过时性.
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