Trustable AI generated Mapping (TAIM)

可信人工智能生成地图 (TAIM)

• 批准号: 10064920
• 金额: $ 5.44万
• 依托单位: EOLAS INSIGHT LTD
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10064920
• 关键词: Trustable; AI; generated; Mapping; TAIM

Artificial Intelligence and Machine Learning based mapping based on remote sensing data presents a significant opportunity within the environmental field, removing the need for manually digitising features in imagery and for mapping larger areas to a finer granularity than is possible using traditional techniques such as Geospatial Information System (GIS) based analysis. As with any new technology however, there are reservations to its use 'on the ground'. This scepticism is to some degree warranted, due to the lack of standardised methods for comparing AI algorithms to ground results in a methodical manner.To address this challenge, EOLAS and partner organisation Scotland's Rural College's (SRUC's) Trustable Credit scheme will define a framework and candidate standard which allows for direct comparison of AI / ML generated mapping algorithms. This includes the definition of standard classes to be used by algorithms depending on the application, and baseline sites mapped to a high degree of accuracy. Using these organisations with an active interest in the field can run these trial sites through their algorithms to determine key performance metrics, comparing their results to pre-ground truthed data. The initial use case will be the carbon credit markets, selected due to the high requirement for trust in the outputs, emerging best practice, and its position as a high growth market suitable for SME involvement.The key approach to the project will be the definition of a consortium with interests in AI derived data products and wider engagement to ensure that a common methodology is defined and agreed. Here existing schemes such as Trusted Credit will be leveraged, due to their established networks of interested parties. A large part of this project will be engagement with the geospatial community more widely, ensuring a collaborative approach aimed at overcoming a challenge faced by all operators: user trust in the technology. This consortium will, through working groups, explore the issue and present solutions for standardised quality metrics.Through engagement and standard definition we will mature the use of emerging AI technologies within the carbon credit use case, serving as an example for techniques for wider adoption within the environmental and geospatial sectors. By providing open and transparent methodologies for assessing the quality of AI / ML derived data products this project will increase the overall levels of trust in AI as a mechanism for mapping features in combination with remote sensing data, benefitting the wider community.

基于遥感数据的人工智能和基于机器学习的映射为环境领域提供了一个重要的机会，从而消除了与使用基于地理空间信息系统（GIS）的分析等传统技术相比，需要手动数字化图像中的特征，并将更大的区域映射到更细的粒度。但是，与任何新技术一样，它的使用“地面”也有保留。 This scepticism is to some degree warranted, due to the lack of standardised methods for comparing AI algorithms to ground results in a methodical manner.To address this challenge, EOLAS and partner organisation Scotland's Rural College's (SRUC's) Trustable Credit scheme will define a framework and candidate standard which allows for direct comparison of AI / ML generated mapping algorithms.这包括算法根据应用程序使用的标准类的定义，以及映射到高度准确性的基线位点。使用对该领域有积极兴趣的这些组织可以通过其算法运行这些试验站点来确定关键性能指标，并将其结果与预先实现的数据进行比较。最初的用例将是碳信贷市场，因为对产出的信任，新兴的最佳实践以及其作为适合中小型企业参与的高增长市场的地位而选择的。由于其既定的感兴趣的人网络，将在这里利用诸如可信赖信用的现有计划。该项目的很大一部分将是与地理空间社区的互动，以确保一种协作方法，旨在克服所有操作员面临的挑战：用户对技术的信任。该财团将通过工作组探讨问题并提供标准化质量指标的解决方案。通过参与和标准定义，我们将在碳信用式用例中使用新出现的AI技术的使用，以作为在环境和地理领域内广泛采用的技术的示例。通过提供开放和透明的方法来评估AI / ML得出数据产品的质量，该项目将增加AI的总体信任水平，作为与遥感数据结合使用的映射功能的机制，从而使更广泛的社区受益。