Machine learning for geospatial intelligence (4647

地形测量（OS）管理着许多不同的地理空间数据集，用于解决英国和国际上的重大问题，例如绘制土地利用变化图，估算温室气体排放，规划新的开发和能源设施，以及跟踪生态过程。所持有的数据包括大量的航空摄影和卫星图像，以及其他空间土地利用数据，如建筑物、交通网络、生态特征、水文和拓扑。该项目将开发新的机器学习（ML）工具，以便有效地使用图像和其他数据类型。这些工具将有助于在土地利用、环境管理和可再生能源部署等重要社会目标上做出更好的决策。这将直接影响到政府的政策执行。操作系统的专家目前使用图像数据集作为更新土地利用地图的大部分手动过程的一部分。OS对如何利用机器学习使这一过程更快、更准确，或为地图添加新细节非常感兴趣，例如，屋顶形状的细节或路灯和交通灯等街道家具的细节。所持有的图像数据集是巨大的。例如，常规的航空摄影以25厘米的网格分辨率提供整个英国的图像。这对机器学习工具的开发提出了一个重大挑战，因为数据集太大，无法进行有效处理。例如，训练一个用于土地使用分类的ML模型可能需要长达三周的高性能计算。该项目的目的是探索数据采样和预处理技术，这些技术将通过保留重要信息和减少信息冗余来提高性能。该学生将与埃克塞特大学和地形测量局的世界领先研究人员合作，生成简化的数据集，训练机器学习模型，并建立可靠高效的数据处理管道。该问题的可能解决方案可能涉及数据压缩、特征选择、与备选数据集的链接或提高训练效率。学生将掌握图像处理、神经网络、GPU阵列高性能计算、数据处理和地理空间技术的高级知识。最终，该项目的成功将取决于将这些工具应用于地形测量所面临的现实挑战，为英国政府和其他客户提供咨询服务。因此，学生将获得先进的机器学习和地理空间分析的端到端部署的广泛经验。该学生将在埃克塞特大学（University of Exeter）环境情报博士培训中心（Centre for phd Training in Environmental Intelligence）工作，在一个跨学科的研究生研究团队中学习环境数据科学的不同主题。在项目进行期间，他们亦会与地形测量所的研究小组一起实习。在完成博士论文后，学生将获香港地形测量处聘为博士后，为期12周，以促进知识交流和实施。该奖学金由EPSRC科学与技术工业合作奖（iCASE）计划资助，由地形测量所获得。欲了解更多关于工业案例计划的详细信息，请访问https://epsrc.ukri.org/skills/students/industrial-case/intro/If，您有任何问题或了解更多信息，请发送电子邮件给罗春波博士（c.luo@exeter.ac.uk）。

Ordnance Survey (OS) manage many diverse geospatial datasets used to address significant issues in Great Britain and internationally, such as mapping land use changes, estimating greenhouse gas emissions, planning new development and energy installations, and tracking ecological processes. Data held includes huge amounts of aerial photography and satellite imagery, alongside other spatial land use data such as buildings, transport networks, ecological features, hydrology and topology. This project will develop new machine learning (ML) tools to enable effective use of imagery alongside other data types. These tools will enable better decision-making on important societal goals around land use, environmental stewardship and renewable energy deployment. It will directly influence government policy delivery.Experts at OS currently use the imagery datasets as part of a largely manual process to update their land use maps. OS is greatly interested in how machine learning can be used to make this process faster, or more accurate, or to add new details to the maps, for exampl, details of roof shapes or street furniture such as lampposts and traffic lights.The imagery datasets held are huge. For example, regular aerial photography provides imagery of the whole of Great Britain at 25cm grid resolution. This presents a major challenge to the development of machine learning tools, in that the dataset is too large for efficient processing. For example, it can take up to three weeks of high-performance computation to train a single ML model for land use classification.The aim of this project is to explore techniques for data sampling and pre-processing that will improve performance by retaining important information and reducing information redundancy. The student will work with world-leading researchers at the University of Exeter and Ordnance Survey to generate simplified datasets, train machine learning models, and establish reliable and efficient pipelines for data processing. Possible solutions to the problem might involve data compression, feature selection, linking to alternative datasets, or improving the efficiency of training. The student will develop advanced knowledge of image processing, neural networks, high performance computation with GPU arrays, data handling and geospatial techniques.Ultimately, the success of the project will be determined by application of the tools to real-world challenges faced by Ordnance Survey in its role advising the UK government and other clients. Thus the student will gain broad experience of the end-to-end deployment of advanced ML and geospatial analysis.The student will be based within the Centre for Doctoral Training in Environmental Intelligence at the University of Exeter, within an interdisciplinary cohort of postgraduate researchers studying diverse topics in environmental data science. They will also spend time on placement with the Ordnance Survey research team during the project. After completion of the PhD thesis, the student will be employed by Ordnance Survey on a 12-week postdoctoral contract to facilitate knowledge exchange and implementation.The studentship is funded via the EPSRC Industrial Cooperative Awards in Science & Technology (iCASE) scheme, via a grant awarded to Ordnance Survey. For more details about the Industrial CASE scheme, see https://epsrc.ukri.org/skills/students/industrial-case/intro/If you have questions or for more information, please email Dr Chunbo Luo (c.luo@exeter.ac.uk).