In various life science applications, the acquisition of spatial data can be labor intensive or costly such that in practice one is interested in an efficient design for data collection, e.g. for the acquisition of species observations to model species distribution or for the acquisition of weather observations for the study of climate extremes. Although a rich literature is available on spatial survey designs, less is known on how to develop optimum designs for integrating field surveys with modern acquisition techniques such as citizen science where data is collected by volunteers with different levels of expertise and in an unstandardized manner or remote sensing techniques where a parameter of interest is inferred from high-dimensional data. In this PhD project, we will test and adapt principles from the theory of optimum design to integrate optimum survey design of in-situ data with other types of sensed data.