In biodiversity research sensors already collect numerous data that is difficult if not impossible to evaluate by hand. Examples are camera traps continuously monitoring the environment to evaluate distribution of species over a certain area and time. This leads to a big-data problem where the data contains the knowledge the researcher is gaining for. Computer vision and machine learning can provide methods for (semi-)automatic evaluation and structuring of large data streams. Currently, most of the research in this area is driven by the recently founded Michael Stifel Center for Data Driven and Simulation Science, Jena (http://www.mscj.uni-jena.de). This presentation points to potentials of life-long learning, especially active and incremental learning in such an monitoring application. Individual aspects, i.e. fine-grained recognition, recent methods for active learning, and novelty detection, are described in more details. Experimental evaluation (automatic bird species and moths classification, incremental evaluation of images from camera traps in nature) summarizes first results for automatic analysis of images and videos that keep the human in the loop. Finally, a method for interactive image retrieval to support researchers from biology is demonstrated.