Deep Learning with Spatiotemporal Data: A Deep Dive into GeotorchAI

Kanchan Chowdhury, Mohamed Sarwat

Abstract: In recent years, numerous neural network models have been put forth, with an emphasis on the applications of raster imagery and spatiotemporal non-imagery datasets. Implementing these models using existing deep learning frame-works, such as PyTorch and TensorFlow, requires nontrivial coding efforts from the developers although these deep learning frameworks support the implementation of various state-of-the-art machine learning models, such as neural networks, hidden Markov models, and support vector machines. This is due to the fact that the models emphasized on spatiotemporal applications 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. Considering the limitations of existing deep learning frameworks, we present GeoTorchAI, a framework for deep learning and scalable data processing on raster imagery and spatiotemporal non-imagery datasets. 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 and a DFtoTorch Converter module that enable the formation of trainable spatiotemporal vector datasets and the mapping of preprocessed DataFrames into PyTorch tensors, respectively.