GeoTorch: a spatiotemporal deep learning framework

Kanchan Chowdhury, Mohamed Sarwat

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publication30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2022
EditorsMatthias Renz, Mohamed Sarwat, Mario A. Nascimento, Shashi Shekhar, Xing Xie
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450395298
DOIs
StatePublished - Nov 1 2022
Externally publishedYes
Event30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL GIS 2022 - Seattle, United States
Duration: Nov 1 2022Nov 4 2022

Publication series

NameGIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems

Conference

Conference30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL GIS 2022
Country/TerritoryUnited States
CitySeattle
Period11/1/2211/4/22

Keywords

  • apache spark
  • satellite images
  • spatiotemporal deep learning

ASJC Scopus subject areas

  • Earth-Surface Processes
  • Computer Science Applications
  • Modeling and Simulation
  • Computer Graphics and Computer-Aided Design
  • Information Systems

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