[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ skip to main content
10.1145/3555041.3589734acmconferencesArticle/Chapter ViewAbstractPublication PagesmodConference Proceedingsconference-collections
short-paper

A Demonstration of GeoTorchAI: A Spatiotemporal Deep Learning Framework

Published: 05 June 2023 Publication History

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.

Supplemental Material

MP4 File
This video is about GeoTorchAI, a deep learning framework for raster imagery and spatiotemporal non-imagery datasets. GeoTorchAI has two modules - deep learning module and data preprocessing module. Deep learning module contains various state-of-the-art datasets, models, and transforms in a ready-to-use format from the literature of raster imagery and spatiotemporal non-imagery datasets, which can be utilized in exactly PyTorch way during deep learning. The data preprocessing module supports performing transformations of raster images before training and converting raw spatiotemporal non-imagery datasets into a trainable tensor for deep learning. The data preprocessing module runs on a scalable distributed environment and can be utilized in a Pythonic way.

References

[1]
Kanchan Chowdhury and Mohamed Sarwat. 2022. GeoTorch: A Spatiotemporal Deep Learning Framework (SIGSPATIAL '22). Article 100.
[2]
Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth. 2019. Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. (2019).
[3]
Qun Liu, Saikat Basu, Sangram Ganguly, Supratik Mukhopadhyay, Robert DiBiano, Manohar Karki, and Ramakrishna R. Nemani. 2019. DeepSat V2: feature augmented convolutional neural nets for satellite image classification. Remote Sensing Letters 11 (2019), 156 -- 165.
[4]
Benedek Rozemberczki, Paul Scherer, Yixuan He, George Panagopoulos, Alexander Riedel, Maria Astefanoaei, Oliver Kiss, Ferenc Beres, Guzmán López, Nicolas Collignon, and Rik Sarkar. 2021. PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models. In CIKM '21. 4564--4573.
[5]
Junbo Zhang, Yu Zheng, and Dekang Qi. 2017. Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction. In AAAI'17. 1655--1661.

Cited By

View all
  • (2024)Deep Learning with Spatiotemporal Data: A Deep Dive into GeotorchAI2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00387(5156-5169)Online publication date: 13-May-2024

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences
SIGMOD '23: Companion of the 2023 International Conference on Management of Data
June 2023
330 pages
ISBN:9781450395076
DOI:10.1145/3555041
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 June 2023

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. apache spark
  2. satellite images
  3. spatiotemporal deep learning

Qualifiers

  • Short-paper

Data Availability

This video is about GeoTorchAI, a deep learning framework for raster imagery and spatiotemporal non-imagery datasets. GeoTorchAI has two modules - deep learning module and data preprocessing module. Deep learning module contains various state-of-the-art datasets, models, and transforms in a ready-to-use format from the literature of raster imagery and spatiotemporal non-imagery datasets, which can be utilized in exactly PyTorch way during deep learning. The data preprocessing module supports performing transformations of raster images before training and converting raw spatiotemporal non-imagery datasets into a trainable tensor for deep learning. The data preprocessing module runs on a scalable distributed environment and can be utilized in a Pythonic way. https://dl.acm.org/doi/10.1145/3555041.3589734#SIGMOD23-modde71.mp4

Conference

SIGMOD/PODS '23
Sponsor:

Acceptance Rates

Overall Acceptance Rate 785 of 4,003 submissions, 20%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)85
  • Downloads (Last 6 weeks)2
Reflects downloads up to 19 Dec 2024

Other Metrics

Citations

Cited By

View all
  • (2024)Deep Learning with Spatiotemporal Data: A Deep Dive into GeotorchAI2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00387(5156-5169)Online publication date: 13-May-2024

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media