Trustable AI generated Mapping (TAIM)

基于遥感数据的基于人工智能和机器学习的制图在环境领域提供了一个重要的机会，消除了手动数字化图像中特征的需要，并将更大的区域映射到比使用传统技术（如基于地理空间信息系统（GIS）的分析）更精细的粒度。然而，与任何新技术一样，人们对“在地面上”使用它持保留态度。这种怀疑在某种程度上是有道理的，因为缺乏标准化的方法来有条不紊地将AI算法与地面结果进行比较。为了解决这一挑战，EOLAS和合作组织苏格兰农村学院（SRUC）的可信信贷计划将定义一个框架和候选标准，允许直接比较AI / ML生成的映射算法。这包括根据应用程序定义算法所使用的标准类别，以及高精度绘制的基线站点。利用这些对该领域感兴趣的组织，可以通过他们的算法运行这些试验站点，以确定关键的性能指标，将其结果与地面前的真实数据进行比较。最初的用例将是碳信用额市场，由于对产出的信任要求很高，新兴的最佳实践，以及其作为适合中小企业参与的高增长市场的地位，因此被选中。该项目的关键方法将是定义一个对人工智能衍生数据产品感兴趣的联盟，并进行更广泛的参与，以确保定义和商定通用方法。在这里，现有的计划，如可信信贷，将被利用，由于其建立的网络感兴趣的各方。该项目的很大一部分将是与地理空间界更广泛地接触，确保采取合作方法，以克服所有运营商面临的挑战：用户对技术的信任。该联盟将通过工作组探索问题并提出标准化质量指标的解决方案。通过参与和标准定义，我们将在碳信用用例中成熟新兴人工智能技术的使用，作为环境和地理空间领域广泛采用技术的范例。通过提供开放和透明的方法来评估人工智能/机器学习衍生数据产品的质量，该项目将提高对人工智能的整体信任水平，作为与遥感数据相结合的映射功能的机制，使更广泛的社区受益。

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.