Land use classification is a central remote sensing task with a broad range of applications. Typically this is represented as a supervised learning problem, the first step of which is to develop a taxonomy of discrete labels. However, such categories are restricted in the range of uses they can convey and arbitrary decisions are often required when defining the categories. Instead, we argue that the abstract notion of land use can be indirectly characterized by the types and quantities of common objects found in an area. To capture the presence of such objects, we propose an implicit approach to defining and estimating land use that relies on sparsely distributed social media imagery but retains the benefits of dense coverage provided by satellite imagery. Our method is formulated as a convolutional neural network that operates on satellite imagery and outputs a probability distribution over quantities of objects common in social media imagery at that location. We show that the learned feature representation is discriminative for existing land use categories.