The combination of spatial distribution, semantic characteristics, and sometimes temporal dynamics of POIs inside a geographic region can capture its unique land use characteristics. We developed a scalable POI-based land use modeling framework. By combining POIs with a neural network language model, we developed a spatially explicit approach to learn the embedding representation of POIs and AOIs. We trained supervised classifiers using AOI embeddings as input features to predict AOI land use at different semantic granularities.