Remote sensing data in the urban GIS

Abstract

Urban development and management require the handling of complex spatial related objects as well. Nowadays there are data acquisition and data process technologies, which can satisfy such demands. The methods of data acquisition typically provide additional information for GIS beyond geometric data. During the data processing we can get further possibilities. Data from different source can be managed jointly and the information content of the data can be multiplied. Formal times, production of the corresponding high-quality data of the objects was a time consuming and expensive procedure. GIS data acquisition technologies such as Lidar, digital photogrammetry, remote sensing, terrestrial laser scanning and GNSS positioning enable to produce a complex spatial urban database with high resolution and high accuracy. Certainly, the data collection equipment may be still costly, but due to the efficiency of the acquisition method, the measurement time can dramatically be shortened. It is also an important aspect that a complex database enables multiple analysis so that the database can serve multiple disciplines. In this study we present some of the data acquisition and data processing technologies, which are suitable to integrate into an urban database and can provide useful information for a wide range of applications through analysis and visualization. In urban development several factors need to be considered simultaneously, such as infrastructure, environment, heritage preservation, etc. Different spatial display capabilities can illustrate object of interests one-by- one, but also it is possible to model its surroundings as well. This way such tools can be used efficiently for urban planning by the municipal decision makers. In this study, we will present how to collect and how to utilize different resolution data (Lidar, close range and aerial photogrammetry, terrestrial laser scanning) in urban areas, how to create 3 dimensional models of objects or how to generate surface model of the city based on different data. There are examples presented indicating how to use different combinations of geometric, spectral and topographic data in practice.