Earth Index
  • Welcome
  • Getting Started
    • Create an account
    • Starting a new project
    • Feature Requests and Bug Reports
  • Concepts
    • Brainstorming
    • Navigating the toolbar
    • Assigning labels
    • Running predictions
    • Tips & tricks
  • Advanced
    • Quick validation
    • Exporting or deleting labels
  • Case Studies
    • Logging roads on Malaita Island
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  • Create multiple projects
  • Quality over Quantity
  • Basemap note

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  1. Concepts

Tips & tricks

Create multiple projects

To find all examples of what you are looking for, create multiple projects, each with different input labels. While you could label all on one map, the overall utility of search results will decline. For example:

  1. Input labels for single logging roads may behave differently than labels for jumbled logging roads (near the actual current/past clearing site vs. road to access).

  2. Input labels for roads in a village may produce different searches than roads in a forest.

  3. Mining - creating different projects for large mines, small mines, and skinny mines on small tributaries.

Quality over Quantity

In looking through the predictions, find a couple instances of positive and negative labels within the predictions you think will help teach Earth Index to better find what you are looking for, instead of going through all of the predictions. Then try the search again and see if it improves. Labelling is a process of trial and error, so don't get discouraged if it takes a while to find the right dataset.

Basemap note

When exploring the toolbar, you have the ability to select which "Basemap" you would like to display on the interface. There are three options for Basemap including: Mapbox Satellite, Mapbook Outdoors, and Sentinel-2. The two Mapbox maps are intended to be a helpful reference for exploring the Earth, but Sentinel-2 is the primary satellite imagery. This imagery has been indexed into embeddings that are the core essence of Earth Index, allowing you to search across different tiles and find objects. Thus, when labeling tiles and running predictions, our algorithms use the embeddings based on Sentinel-2 imagery.

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Last updated 5 months ago

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