Machine learning for geospatial intelligence (4647

Orlando Survey（OS）管理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，例如绘制土地使用变化，估计温室气体排放，规划新的开发和能源设施，以及跟踪生态过程。所掌握的数据包括大量的航空摄影和卫星图像，以及其他空间土地使用数据，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，以有效使用图像和其他数据类型。这些工具将有助于围绕土地使用、环境管理和可再生能源部署等重要社会目标做出更好的决策。这将直接影响政府政策的实施。OS的专家目前使用图像数据集作为主要手动过程的一部分来更新他们的土地利用地图。OS非常感兴趣的是如何使用机器学习来使这个过程更快，或更准确，或为地图添加新的细节，例如，屋顶形状或街道设施（如灯柱和交通灯）的细节。例如，常规航空摄影提供了25厘米网格分辨率的整个英国的图像。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，训练一个用于土地利用分类的ML模型可能需要长达三周的高性能计算。本项目的目的是探索数据采样和预处理技术，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Orlando Survey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。这个问题的可能解决方案可能包括数据压缩、特征选择、链接到替代数据集或提高训练效率。学生将学习图像处理、神经网络、GPU阵列高性能计算、数据处理和地理空间技术等方面的高级知识。最终，项目的成功将取决于Orchant Survey在为英国政府和其他客户提供咨询服务时所面临的现实挑战。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。学生将在埃克塞特大学环境智能博士培训中心工作，在研究生研究人员的跨学科队列中学习环境数据科学的各种主题。他们还将在项目期间花时间与Orbidity Survey研究团队一起工作。在完成博士论文后，学生将被Orphinian Survey聘用，为期12周的博士后合同，以促进知识交流和实施。该学生奖学金通过EPSRC工业合作奖科学与技术（iCASE）计划资助，通过授予Orphinian Survey的赠款。有关工业案例计划的更多详细信息，请参阅https://epsrc.ukri.org/skills/students/industrial-case/intro/If您有问题或更多信息，请发送电子邮件给罗春波博士（c. 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).