TY - GEN
T1 - GeoTorch
T2 - 30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL GIS 2022
AU - Chowdhury, Kanchan
AU - Sarwat, Mohamed
N1 - Publisher Copyright:
© 2022 Owner/Author.
PY - 2022/11/1
Y1 - 2022/11/1
N2 - 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.
AB - 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.
KW - apache spark
KW - satellite images
KW - spatiotemporal deep learning
UR - http://www.scopus.com/inward/record.url?scp=85143591814&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85143591814&partnerID=8YFLogxK
U2 - 10.1145/3557915.3561036
DO - 10.1145/3557915.3561036
M3 - Conference contribution
AN - SCOPUS:85143591814
T3 - GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
BT - 30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2022
A2 - Renz, Matthias
A2 - Sarwat, Mohamed
A2 - Nascimento, Mario A.
A2 - Shekhar, Shashi
A2 - Xie, Xing
PB - Association for Computing Machinery
Y2 - 1 November 2022 through 4 November 2022
ER -