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GeoAI Quiz

Total questions: 10

Worksheet time: 5mins

Name
Class
Date
1.

What best describes Geospatial Reasoning?

a)

Styling basemaps

b)

Using AI + geodata to orchestrate steps that answer spatial questions

c)

Geocoding addresses only

d)

Drawing buffers by hand

2.

You need areas most similar to a known lightindustrial block. Which approach fits best?

a)

Pixel histograms only

b)

Embedding-based similarity search

c)

Manual site visits only

d)

Area-weighted centroids

3.

Pick the correct pairing:

a)

AlphaEarth = open-source LLM; GeoGemma = virtual satellite

b)

AlphaEarth = geospatial foundation model; GeoGemma = open-source GeoLLM for imagery

c)

AlphaEarth = routing API; GeoGemma = DEM

d)

AlphaEarth = SAR sensor; GeoGemma = LiDAR

4.

Grounding with Google Maps primarily helps to

a)

Reduce token cost only

b)

Anchor model outputs to verified places/POIs and facts

c)

Export PDFs

d)

Improve 3D visualization

5.

In a quick sitesuitability index, the most defensible formula is

a)

Sum of raw factors

b)

Weighted sum of normalized factors

c)

Random weights each run

d)

Max of any single factor

6.

Your changedetection flags new rooftops, but images are from different seasons. Best mitigation?

a)

Ignore seasonality

b)

Use multitemporal stacks/embeddings and compare likeforlike windows

c)

Lower the threshold until changes disappear

d)

Clip a smaller AOI

7.

For accurate urban area/distance measurement inside one state, which CRS is most appropriate?

a)

WGS84 (EPSG:4326)

b)

Web Mercator (EPSG:3857)

c)

A suitable UTM zone for the region

d)

Any local datum at random

8.

Which practice most improves auditability of GeoAI decisions?

a)

Hiding weights to avoid bias accusations

b)

Using only global datasets

c)

Maintaining a data dictionary with source/date/license/CRS + versioning

d)

Deleting intermediate layers

9.

A model trained on a coastal metropolis performs poorly in an inland secondary city. This is mainly

a)

Projection error

b)

Domain shift

c)

Class imbalance unrelated to space

d)

Hardware limitation

10.

Your shortlist ignores underground utilities and heritage buffers because those datasets were missing. The primary risk is

a)

Simpsons paradox

b)

Sampleselection/coverage bias leading to unsafe recommendations

c)

Overfitting due to too many layers

d)

CRS mismatch