Studies and analysis

This repository consolidates various studies and analysis related with EarthDaily Agro services and capabilities.
Who we are

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Table of Contents

About The Project
Getting Started
- Prerequisites
- Installation
Content
Support development
License
Contact
Copyrights

About The Repository

EarthDaily Agro is the agricultural analysis division of EartDaily Analytics. Learn more about Earth Daily at EarthDaily Analytics | Satellite imagery & data for agriculture, insurance, surveillance. EarthDaily Agro uses satellite imaging to provide advanced analytics to mitigate risk and increase efficiencies – leading to more sustainable outcomes for the organizations and people who feed the planet.

Throught our <geosys/> platform, we make geospatial analytics easily accessible for you to be browsed or analyzed, within our cloud or within your own environment. We provide developers and data scientists both flexibility and extensibility with analytic ready data and digital agriculture ready development blocks. We empower your team to enrich your systems with information at the field, regional or continent level via our API or Apps.

We have a team of experts around the world that understand local crops and ag industry, as well as advanced analytics to support your business.

The current project aims to provide samples, snippets and examples on how to integrate data and analytics but expand and enrich capabilites by creating your own analytic based satellite imagery, weather and many others available inputs.

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Content

Cloud Mask Performance

This analysis will compare public and EarthDaily Agro Cloud masks to a reference dataset manually digitized to highlight over and under detection performance of each model. [More details here](https://github.com/GEOSYS/Studies-and-Analysis/tree/main/Cloud-Mask-Performance)

Auto Cloud Mask Accuracy

This analysis will compare EarthDaily Agro Cloud masks to a reference dataset manually digitized to highlight Earthdaily Agro ACM accuracy (more than 90% per month on Sentinel-2 images). [More details here](https://github.com/GEOSYS/Studies-and-Analysis/tree/main/Auto-Cloud-Mask-Accuracy)

Publication and conference contribution

Here is various content shared during conferences and tradeshows

Content available:

AWS Summit Toronto 2023 See presentation
Estimation cover crop biomass publication
More content coming soon...

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License

Distributed under the MIT License.

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Contact

For any additonal information, please email us

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Copyrights

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