Deep learning frameworks, such as PyTorch and TensorFlow, support the implementation of various state-of-the-art machine learning models such as neural networks, hidden Markov models, and support vector machines. In recent years, many extensions of neural network models have been proposed in the literature targeting the applications of raster and spatiotemporal datasets. Implementing these models using existing deep learning frameworks requires nontrivial coding efforts from the developers because these extensions either are hybrid combinations of various categories of neural network models or differ extensively from state-of-the-art models supported by existing deep learning frameworks. Moreover, existing deep learning frameworks lack the support for scalable data preprocessing required to form trainable tensors from raw spatiotemporal datasets. To enable easy implementation of these neural network extensions, we present GeoTorch, a framework for deep learning and scalable data processing on raster and spatiotemporal datasets. Along with the state-of-the-art spatiotemporal models and ready-to-use benchmark datasets, we propose a data preprocessing module that allows the processing and transformation of spatiotemporal datasets in a cluster computing setting.