<![CDATA[- A shooter game is a popular game genre with various components. To make a shooter game more attractive, some power-ups items can support players to achieve their goals. Power-ups items provide more power to players, some of which include ammo, extra lives, and invulnerability. The location of power-ups items should be in a special place so that it neither too easy to find nor too difficult to find. Item placement could be done manually by a human or a technical artist. It will need a relatively long time and high cost. In this paper, we try to mimic technical artist vision when placing an item. Visual images have been collected by scanning spatially the forest terrain by using a virtual camera on top. Each image data comply with the item placement rules according to the Tomb Raider and Uncharted 4 games. Convolutional Neural Network (CNN) is used to find out which images can be occupied by power-up items or not. From several experimental scenarios, the use of the Global Average Pooling layer is proven to produce a model that is not overfitting. The best CNN models are developed and got an accuracy of 90.5% with an architecture that includes the Global Average Pooling layer. That model is applied to the new forest terrain so that power-up items can automatically be placed in an appropriate location.]]>
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