In the context of climate stress, urbanisation and population growth, design and planning tools that assist in decentralised and environmental infrastructural planning are becoming more common. In order to support the design of increasingly complex urban water infrastructure systems; accurate and easily obtainable spatial databases describing land cover types are crucial. Accordingly, a methodology categorizing land covers that supplements these tools is proposed. Utilizing GIS imagery of high spatial accuracy that is easily obtainable from flyover techniques, radiometric and geometric data is generated to create a multi-functional classification of urban land cover, designed to be applicable to various urban planning tools serving different purposes, e.g. urban water management. The methodology develops 13 individual land cover categories based on the complete capabilities of the NDVI and nDSM imagery, which is then adapted to suit planning tool requirements. Validation via a case study application at Innsbruck (Austria), an overall classification accuracy of 89.3 % was achieved. The accuracy of the process was limited in differentiating certain categories (e.g. Dry Grass and Concrete, Trees and Irrigated Grass, etc.), which could yield limitations subject to intended model applications. Despite this, the classification results yielded high accuracy, demonstrating the methodology can be utilised by various software to improve urban water management analysis.

在气候压力，城市化和人口增长的背景下，有助于分散和环境基础设施规划的设计和规划工具变得越来越普遍。为了支持日益复杂的城市水基础设施系统的设计；准确，易于获得的描述土地覆盖类型的空间数据库至关重要。因此，提出了一种对土地覆盖物进行分类的方法，以补充这些工具。利用易于从立交桥技术获得的高空间精度GIS图像，生成辐射度和几何数据以创建城市土地覆盖物的多功能分类，旨在应用于服务于不同目的的各种城市规划工具，例如城市水管理。该方法根据NDVI和nDSM影像的完整功能开发了13种单独的土地覆被类别，然后将其进行调整以适合规划工具的要求。通过在奥地利因斯布鲁克的案例研究应用进行验证，总体分类准确度达到了89.3％。在区分某些类别（例如干草和混凝土，树木和灌溉草等）时，该过程的准确性受到限制，这可能会受到预期模型应用的限制。尽管如此，分类结果仍具有很高的准确性，表明该方法可以被各种软件用来改善城市水管理分析。通过在奥地利因斯布鲁克的案例研究应用进行验证，总体分类准确度达到了89.3％。在区分某些类别（例如干草和混凝土，树木和灌溉草等）时，该过程的准确性受到限制，这可能会受到预期模型应用的限制。尽管如此，分类结果仍具有很高的准确性，表明该方法可以被各种软件用来改善城市水管理分析。通过在奥地利因斯布鲁克的案例研究应用进行验证，总体分类准确度达到了89.3％。在区分某些类别（例如干草和混凝土，树木和灌溉草等）时，该过程的准确性受到限制，这可能会受到预期模型应用的限制。尽管如此，分类结果仍具有很高的准确性，表明该方法可以被各种软件用来改善城市水管理分析。