A Demonstration of GeoTorchAI: A Spatiotemporal Deep Learning Framework

Abstract

This paper demonstrates GeoTorchAI, a spatiotemporal deep learning framework. In recent years, many neural network models have been proposed focusing on the applications of raster imagery and spatiotemporal non-imagery datasets. Implementing these models using existing deep learning frameworks, such as PyTorch and TensorFlow, requires nontrivial coding efforts from the developers because these models 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, a mandatory step for converting spatiotemporal datasets into trainable tensors. GeoTorchAI enables machine learning practitioners to implement spatiotemporal deep learning models with minimum coding efforts on top of PyTorch. It provides state-of-the-art neural network models, ready-to-use benchmark datasets, and transformation operations for raster imagery and spatiotemporal non-imagery datasets. Besides deep learning, GeoTorchAI contains a data preprocessing module that allows preparing trainable spatiotemporal vector datasets and the transformation of raster images in a cluster computing setting.