Accessing and Processing Landsat Data on Google Cloud

• Day: 2023-08-20
• Time: 01:35 to 02:19
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Google Cloud, Landsat, Data Processing, Python, Satellite Imagery

Description

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.

Evidence

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• event_ids: []