This thesis presents the implementation of data fusion techniques for sustainable urban development. Recently, increasingly more geospatial data have been made easily available for no cost. The immeasurable quantities of geospatial data are mainly from four kinds of sources: remote sensing satellites, geographic information systems (GIS) data, citizen science, and sensor web. Among them, satellite images have been mostly used, due to the frequent and repetitive coverage, as well as the data acquisition over a long time period. However, the rather coarse spatial resolution of e.g. 30 m for Landsat 8 multispectral images impairs the application of satellite images in urban areas. Even though image fusion techniques have been used to improve the spatial resolution, the existing image fusion methods are neither suitable for sharpening one band thermal images nor for hyperspectral images with hundreds of bands. Therefore, simplified Ehlers fusion was developed. It adds the spatial information of a high-resolution image into a low-resolution image in the frequency domain through fast Fourier transform (FFT) and filter techniques. The developed algorithm successfully improved the spatial resolution of both one band thermal images as well as hyperspectral images. It can enhance various images, regardless of the number of bands and the spectral coverage, providing more precise measurement and richer information. To investigate the performance of simplified Ehlers fusion in practical use, it was applied for urban heat island (UHI) analysis. This was done by sharpening daytime and nighttime thermal images from Landsat 8, Landsat 7, and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). The developed algorithm effectively improved the spatial details of the original images so that the temperature differences between agricultural, forest, industrial, transportation, and residential areas could be distinguished from each other. Based on that, it was found that in the study city the causes of UHI are mainly anthropogenic heat from industrial areas as well as high temperatures from the road surface and dense urban fabric. Based on this analysis, corresponding mitigation strategies were tailored. Remote sensing images are useful yet not sufficient to retrieve land use related information, despite high spatial resolution. For sustainable urban development research, remote sensing images need to be incorporated with data from other sources. Accordingly, image fusion needs to be extended to broader data fusion. Extraction of urban vacant land was therefore taken as a second application case. Much effort was spent on the definition of vacant land as unclear definitions lead to ineffective data fusion and incorrect site extraction results. Through an intensive study of the current research and the available open data sources, a vacant land typology is proposed. It includes four categories: transportation-associated land, natural sites, unattended areas or remnant parcels, and brownfields. Based on this typology, a two-level data fusion framework was developed. On the feature level, sites are identified. For each type of vacant land, an individual site extraction rule and data fusion procedure is implemented. The overall data fusion involves satellite images, GIS data, citizen science, and social media data. In the end, four types of vacant land features were extracted from the study area. On the decision level, these extracted sites could be conserved or further developed to support sustainable urban development.