Accessing and Processing Landsat Data on Google Cloud

2 min read

📅 2023-08-20 — Session: Accessing and Processing Landsat Data on Google Cloud

🕒 01:35–02:19
🏷️ Labels: Google Cloud, Landsat, Data Processing, Python, Satellite Imagery
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal: The session aimed to explore methods for accessing and processing Landsat satellite imagery data using Google Cloud services, including Google Cloud Storage and BigQuery.

Key Activities:

  • Suppressed warnings in multi-indexed DataFrame operations using Python’s warnings module.
  • Provided detailed instructions for accessing satellite datasets via Google Cloud Storage and BigQuery.
  • Guided on using Google Cloud Console for managing Google Cloud Storage, including tasks like creating buckets and managing IAM policies.
  • Outlined methods for accessing public Landsat data on Google Cloud Storage, including using gsutil, Python libraries, and direct URL access.
  • Installed the google-cloud-storage package in Python and set up Application Default Credentials for authentication.
  • Detailed the process for downloading and processing Landsat data for Argentina (2020) using Google Cloud Storage and Python.

Achievements:

  • Successfully accessed and downloaded Landsat data from Google Cloud Storage.
  • Implemented Python scripts for processing Landsat data, including filtering and downloading data for specific regions and years.

Pending Tasks:

  • Further optimize the data processing pipeline for Landsat imagery to improve efficiency and reduce processing time.
  • Explore additional methods for visualizing processed satellite data.

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