📅 2023-08-20 — Session: Accessing and Processing Landsat Data on Google Cloud
🕒 01:35–03:00
🏷️ Labels: Google Cloud, Landsat, Data Processing, Python
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal:
The aim of this session was to explore and execute various methods for accessing, downloading, and processing Landsat satellite imagery data using Google Cloud services.
Key Activities:
- Implemented a solution to suppress warnings in multi-indexed DataFrame operations using Python’s warnings module.
- Provided detailed instructions for accessing satellite datasets via Google Cloud Storage (GCS) and BigQuery, including navigation and querying processes.
- Guided on using Google Cloud Storage through the Google Cloud Console, covering tasks like bucket creation and data management.
- Outlined methods to access the public Landsat data bucket on Google Cloud Storage using various tools and libraries.
- Installed and authenticated the
google-cloud-storage
package in Python for accessing Google Cloud services. - Set up Application Default Credentials for Google Cloud to facilitate authentication in cloud environments.
- Detailed steps for downloading and processing Landsat data for Argentina in 2020, including filtering and decompressing data using Python and Pandas.
Achievements:
- Successfully accessed and processed Landsat data using Google Cloud services.
- Implemented efficient data handling and processing techniques using Python and Pandas.
Pending Tasks:
- Further exploration of advanced data processing techniques for Landsat data.
- Optimization of data access and processing workflows for scalability and performance.