Name
Abstract | ||
|---|---|---|
ABSTRACTWe 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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3486635.3491072 | GIS |
DocType | Citations | PageRank |
Conference | 1 | 0.36 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| C. V. Krishnakumar Iyer | 1 | 1 | 0.36 |
| Feili Hou | 2 | 1 | 0.36 |
| Henry Wang | 3 | 1 | 0.36 |
| Yonghong Wang | 4 | 1 | 0.36 |
| Kay Oh | 5 | 1 | 0.36 |
| Swetava Ganguli | 6 | 1 | 0.36 |
| Vipul Pandey | 7 | 1 | 0.36 |