AI in Satellite Imagery Analysis Quiz

Answers and explanations appear as you take the quiz.

1. What can AI help analysts do with large collections of satellite images?
   • Find patterns and changes across imagery for human interpretation
   • Replace all field observations with certain diagnoses
   • Guarantee that every land-cover label is correct
   • Create satellite observations where no sensor data exist
2. What does 'change detection' do in satellite analysis?
   • Counts the satellites in orbit
   • Compares images of the same place over time to flag meaningful differences
   • Changes the color of the satellite
   • Detects new satellites being launched
3. Why is synthetic aperture radar (SAR) especially valuable?
   • It only works on sunny days
   • It takes color photos for tourists
   • It can see through clouds and operate at night
   • It replaces the need for any AI
4. What does the USGS describe NDVI as measuring?
   • A complete diagnosis of plant health
   • The amount of rainfall in a watershed
   • The concentration of minerals in soil
   • The contrast between near-infrared and red reflectance used as a vegetation index
5. What is 'semantic segmentation' in image analysis?
   • Labeling every pixel by its class, such as water, road, or forest
   • Deleting parts of an image
   • Compressing the image to a smaller file
   • Translating image captions into other languages
6. What PlanetScope coverage did Planet document for its Dove fleet?
   • Continuous video of every location
   • Near-daily multispectral coverage over landmasses
   • Daily imagery of every ocean depth
   • A guaranteed clear image of every land parcel each day
7. Why are large satellite scenes often divided into smaller image tiles before model processing?
   • It turns radar data into visible-light photographs
   • It guarantees every tile shows a different land-cover class
   • Tiling makes a very large scene manageable for computation and localized analysis
   • It removes the need to align images from different dates
8. What can combining multiple spectral or radar bands help an analyst infer beyond visible color?
   • The exact identity of every person on the ground
   • The satellite's remaining fuel
   • A guaranteed forecast of next year's weather
   • Properties such as vegetation condition, soil moisture, or flooding