research-article

Trinity: A No-Code AI platform for complex spatial datasets

Authors:

C. V. Krishnakumar Iyer,

Swetava Ganguli,

Vipul PandeyAuthors Info & Claims

GEOAI '21: Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery

Pages 33 - 42

https://doi.org/10.1145/3486635.3491072

Published: 08 November 2021 Publication History

Abstract

We present a no-code Artificial Intelligence (AI) platform called Trinity with the main design goal of enabling both machine learning researchers and non-technical geospatial domain experts to experiment with domain-specific signals and datasets for solving a variety of complex problems on their own. This versatility to solve diverse problems is achieved by transforming complex Spatio-temporal datasets to make them consumable by standard deep learning models, in this case, Convolutional Neural Networks (CNNs), and giving the ability to formulate disparate problems in a standard way, eg. semantic segmentation. With an intuitive user interface, a feature store that hosts derivatives of complex feature engineering, a deep learning kernel, and a scalable data processing mechanism, Trinity provides a powerful platform for domain experts to share the stage with scientists and engineers in solving business-critical problems. It enables quick prototyping, rapid experimentation and reduces the time to production by standardizing model building and deployment. In this paper, we present our motivation behind Trinity and its design along with showcasing sample applications to motivate the idea of lowering the bar to using AI.

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https://doi.org/10.1109/ICPCSN62568.2024.00177
Cao YGanguli SPandey V(2023)Self-Supervised Temporal Analysis of Spatiotemporal DataIGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium10.1109/IGARSS52108.2023.10282482(4856-4859)Online publication date: 16-Jul-2023
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Published In

cover image ACM Conferences

GEOAI '21: Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery

November 2021

77 pages

ISBN:9781450391207

DOI:10.1145/3486635

Editors:
Dalton Lunga
Oak Ridge National Laboratory, TN, USA
,
Lexie Yang
Oak Ridge National Laboratory, TN, USA
,
Song Gao
University of Wisconsin, Madison, WI, USA
,
Bruno Martins
University of Lisbon, Portugal
,
Yingjie Hu
University at Buffalo, NY, USA
,
Xueqing Deng
University of California, Merced, CA, USA
,
Shawn Newsam
University of California, Merced, CA, USA

Copyright © 2021 ACM.

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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New York, NY, United States

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Published: 08 November 2021

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SIGSPATIAL '21

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SIGSPATIAL '21: 29th International Conference on Advances in Geographic Information Systems

November 2 - 5, 2021

Beijing, China

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Overall Acceptance Rate 17 of 25 submissions, 68%

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Cited By

Guresci ETekinerdogan BBabur ÖLiu Q(2024)Feasibility of Low-Code Development Platforms in Precision Agriculture: Opportunities, Challenges, and Future DirectionsLand10.3390/land1311175813:11(1758)Online publication date: 25-Oct-2024
https://doi.org/10.3390/land13111758
Sv SK SS PG S(2024)Democratizing Data Science:Using Language Models for Intuitive Data Insights and Visualizations2024 4th International Conference on Pervasive Computing and Social Networking (ICPCSN)10.1109/ICPCSN62568.2024.00177(1065-1069)Online publication date: 3-May-2024
https://doi.org/10.1109/ICPCSN62568.2024.00177
Cao YGanguli SPandey V(2023)Self-Supervised Temporal Analysis of Spatiotemporal DataIGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium10.1109/IGARSS52108.2023.10282482(4856-4859)Online publication date: 16-Jul-2023
https://doi.org/10.1109/IGARSS52108.2023.10282482
Pidó SPinoli PCrovari PIeva FGarzotto FCeri S(2023)Ask Your Data—Supporting Data Science Processes by Combining AutoML and Conversational InterfacesIEEE Access10.1109/ACCESS.2023.327250311(45972-45988)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3272503
Jauhar SJani SKamble SPratap SBelhadi AGupta S(2023)How to use no-code artificial intelligence to predict and minimize the inventory distortions for resilient supply chainsInternational Journal of Production Research10.1080/00207543.2023.216613962:15(5510-5534)Online publication date: 24-Jan-2023
https://doi.org/10.1080/00207543.2023.2166139
Graaff ASmit DEybers S(2023)Let’s Play Games: Using No-Code AI to Reduce Human Cognitive Load During AI Solution DevelopmentArtificial Intelligence Research10.1007/978-3-031-49002-6_7(86-99)Online publication date: 29-Nov-2023
https://doi.org/10.1007/978-3-031-49002-6_7
Esposito ACalvano MCurci ADesolda GLanzilotti RLorusso CPiccinno A(2023)End-User Development for Artificial Intelligence: A Systematic Literature ReviewEnd-User Development10.1007/978-3-031-34433-6_2(19-34)Online publication date: 6-Jun-2023
https://dl.acm.org/doi/10.1007/978-3-031-34433-6_2
Roig PAlcaraz SGilly KBernad CJuiz C(2022)Modeling an Edge Computing Arithmetic Framework for IoT EnvironmentsSensors10.3390/s2203108422:3(1084)Online publication date: 30-Jan-2022
https://doi.org/10.3390/s22031084
Lunga DHu YNewsam SGao SMartins BYang LDeng X(2022)GeoAI at ACM SIGSPATIALSIGSPATIAL Special10.1145/3578484.357849113:3(21-32)Online publication date: 23-Dec-2022
https://dl.acm.org/doi/10.1145/3578484.3578491
Ganguli SIyer CPandey V(2022)Reachability Embeddings: Scalable Self-Supervised Representation Learning from Mobility Trajectories for Multimodal Geospatial Computer Vision2022 23rd IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM55031.2022.00028(44-53)Online publication date: Jun-2022
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