In earthquake-prone areas, it is extremely important to carry out risk analyses of existing buildings and to take proactive measures in advance of potential earthquakes. Despite the availability of Rapid Seismic Assessment Methods (RSAMs), prioritising the seismic risk of buildings is a significant challenge due to the large number of residential buildings in the building stock. In RSAMs, many factors are taken into consideration to determine the earthquake risk priority. While specific construction conditions determine the risk parameters for the considered structures, one of them is the possible pounding effects (collision) of adjacent buildings. The fact that RSAMs have many evaluation parameters makes it difficult in site survey for technical experts to make decisions in some cases. Therefore, it is very important to perform these operations with software support. Based on this motivation, this study aims to perform pre-earthquake risk analysis of residential reinforced concrete buildings by assisting expert engineers (or facilitating the decision-making process in the absence of technical expertise) and to estimate the adjacent building parameter using building facade images for risk prioritisation. To achieve these objectives, a novel deep learning Convolutional Neural Network (CNN) model, named Adjacent-Net, is designed and developed to classify building facade images into adjacent or non-adjacent categories. The performance of Adjacent-Net is compared with various state-of-the-art CNN models such as DarkNet-53, EfficientNet, Inception ResNetV2, NasNet Large, ResNet-101, ShuffleNet, SqueezeNet, VGG-19, and Xception. For evaluation purposes, a dataset comprising 6170 building facade images is collected, and the results indicate that Adjacent-Net can accurately extract building adjacency parameters from images with an accuracy rate of approximately 98 %. This underscores the potential of intelligent systems in detecting collision scenarios, assessing the seismic risk of structures, and determining critical geometric parameters of buildings.