Alcyone is a machine learning system for object and phenomenon detection. Utilizes state of the art machine learning
models to recognize and dictate objects like ships, oil spills, fire and smoke on satellite image data.
This repo is a Proof of Concept of the Front End Service of Alcyone system.
Alcyone is the senior thesis of Eirini Mitsa and Costas Patsaras, students at Informatics Department of Aristotle University of Thessaloniki.
| # | Eirini Mitsa | Costas Patsaras |
|---|---|---|
| mitsaeirini@csd.auth.gr | patsarask@csd.auth.gr | |
| website | --- | costaspatsaras.me |
| github | mitsaeirini | @codelover96 |
Run ng serve for a dev server. Navigate to http://localhost:4200/. The app will automatically reload if you change
any of the source files. You can specify port by adding --port, followed by the desired port number.
Run ng build to build the project. The build artifacts will be stored in the dist/ directory. Use the --prod flag
for a production build.
Run ng test to execute the unit tests via Karma.
Run ng e2e to execute the end-to-end tests via Protractor.
To get more help on the Angular CLI use ng help or go check out
the Angular CLI README.
Built with Angular CLI version 9.0.2.
- Add responsiveness with flexLayout and custom css (DONE)
- Apply minor tweaks to typography (DONE)
- Optimise css (DONE)
- Integrate with back-end
Contributions to this project are welcome! Please fork this repository and submit a pull request with your proposed changes.