📅 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.