Virtual reality technology provides a significant clue to understanding the human visual perception process by enabling the interaction between humans and computers. In addition, deep learning techniques in the visual field provide analysis methods for image classification, processing, and segmentation. This study reviewed the applicability of gaze movement and deep learning-based satisfaction evaluation on the landscape using an immersive virtual reality-based eye-tracking device. To this end, the following research procedures were established and analysed. First, the gaze movement of the test taker is measured using an immersive virtual environment-based eye tracker. The relationship between the gaze movement pattern of the test taker and the satisfaction evaluation result for the landscape image is analysed. Second, using the Convolutional Neural Networks (CNN)-based Class Activation Map (CAM) technique, a model for estimating the satisfaction evaluation result is constructed, and the gaze pattern of the test taker is derived. Third, we compare and analyse the similarity between the gaze heat map derived through the immersive virtual environment-based gaze tracker and the heat map generated by CAM. This study suggests the applicability of urban environment technology and deep learning methods to understand landscape planning factors that affect urban landscape satisfaction, resulting from the three-dimensional and immediate visual cognitive activity.