Identifying and Mapping Urban Crime Hotspots Accurately with Artificial Intelligence (IMAGE)
Caption
Identifying location-specific attributes is an important aspect of social artificial intelligence. However, models that are frequently trained on subjective perceptions and still images are unreliable in predicting crime. Now, researchers from GIST in Korea take things to the next level by training a neural network with a geotagged dataset of reported deviant incidents and sequential images of deviant locations to accurately determine unsafe locations by linking the deviant behavior to the visual features of a city.
Credit
Gwangju Institute of Science and Technology
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