mlsanityData to 7 Oct 2026

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

arXiv 2106.11756 ↗First posted 21 Jun 2021cs.SE cs.AI cs.CVcited by 9

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Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 14 of its 34 references that are in the map. First posted on arXiv 21 Jun 2021.

Builds on

14 of 34 references.

References · 14

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2 more
  1. 2018Deep Learning Approach for Building Detection in Satellite Multispectral Imagerycited by 6
  2. 2020The Collective Knowledge project: making ML models more portable and reproducible with open APIs, reusable best practices and MLOpscited by 4

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 2; 2023: 4; 2024: 1; 2025: 1.

9 papers.

  1. 2023End-User Development for Artificial Intelligence: A Systematic Literature Reviewcited by 4
  2. 2023SeMAnD: Self-Supervised Anomaly Detection in Multimodal Geospatial Datasetscited by 4
  3. 2021Reachability Embeddings: Scalable Self-Supervised Representation Learning from Mobility Trajectories for Multimodal Geospatial Computer Visioncited by 3
  4. 2023Self-Supervised Temporal Analysis of Spatiotemporal Datacited by 2
  5. 2025LoCoML: A Framework for Real-World ML Inference Pipelinescited by 2
  6. 2022Scalable Self-Supervised Representation Learning from Spatiotemporal Motion Trajectories for Multimodal Computer Visioncited by 0
  7. 2022GeoAI at ACM SIGSPATIAL: The New Frontier of Geospatial Artificial Intelligence Researchcited by 0
  8. 2023Temporal Embeddings: Scalable Self-Supervised Temporal Representation Learning from Spatiotemporal Data for Multimodal Computer Visioncited by 0
  9. 2024Human-Centered AI Product Prototyping with No-Code AutoML: Conceptual Framework, Potentials and Limitationscited by 0