Volunteered Geographic Information (VGI) images are a vital source of visual information and image features in the GIS field. The advancement of artificial intelligence, particularly large language models and generative AI, now enables the generation of seemingly lifelike images from textual prompts (Artificial Intelligence Generated Content - AIGC). This raises a pertinent question: can AIGC image features serve as a viable alternative to VGI features in downstream GIS tasks, especially where VGI is scarce or difficult to obtain? This paper conducts an exploratory study comparing VGI and AIGC image features as inputs for a geographic recommendation model, specifically investigating the impact of image elements, colors, and spatial structures. The results indicate that current AIGC images, generated from general textual descriptions, cannot fully substitute for VGI images. This is primarily due to AIGC’s challenges in accurately replicating the specific elements, colors, and spatial relationships inherent in real-world VGI. However, the study suggests AIGC images hold significant potential. When provided with more specific information about elements and particularly their colors, AIGC’s performance approached, and in some color-focused tests, even slightly surpassed that of VGI images. This implies AIGC’s main deficiency is its current understanding of real-world object characteristics and their visual representation, notably the basic knowledge of elements and their associated colors. We propose these shortcomings could be addressed by integrating geographic knowledge bases in future AIGC development. These findings aim to guide AIGC’s application in GIS by identifying current limitations and areas for focused improvement.