This super early-alpha package aims to provide a single scalable function for downloading large Sentinel-1 and Sentinel-2 data cubes with integrated cloud detection, snow masking, and temporal compositing, optimized for use with the planetary computer.
Python, Zarr, Sentinel data integration
2024-11-13
- website
Highlight 1
Robust handling of large-scale data allows users to effectively manage datasets that exceed memory capacity.
Highlight 2
The integration of cloud detection and snow masking ensures higher data quality and relevance for remote sensing applications.
Highlight 3
Its focus on producing Zarr data cubes makes it compatible with modern data processing workflows and tools.
Improvement 1
The app could benefit from a user-friendly interface to assist non-technical users in navigating its functionalities.
Improvement 2
Comprehensive documentation and tutorials would enhance user experience, helping them to utilize the app effectively.
Improvement 3
Consider adding additional features or options for users to customize their data retrieval process based on specific needs.
Product Functionality
Enhance functionality by integrating a GUI to facilitate data selection and easiness for users unfamiliar with command-line interfaces.
UI & UX
Improve UI/UX by designing an intuitive dashboard that simplifies the workflow and provides visual cues for processes being executed.
SEO or Marketing
Consider creating content that highlights use cases and best practices to engage potential users and improve search visibility.
MultiLanguage Support
Add multi-language support to cater to a wider audience, allowing non-English speaking users access to documentation and features.
- 1
What types of data can I download using Sentle?
Sentle allows you to download Sentinel-1 and Sentinel-2 data cubes, focusing on large-scale datasets.
- 2
Is there a tutorial available for using Sentle?
Currently, extensive documentation is limited, but it is a crucial area for future improvement.
- 3
What format does Sentle use for data cubes?
Sentle creates data cubes in the Zarr format, which is suitable for large data processing and analytics.