Review of Image Classification Algorithms Based on Graph Convolutional Networks

Abstract

In recent years, graph convolutional networks (GCNs) have gained widespread attention and applications in image classification tasks. While traditional convolutional neural networks (CNNs) usually represent images as a two-dimensional grid of pixels when processing image data, the classical model of graph neural networks (GNNs), GCNs, can effectively handle data with graph structure, such as social networks, recommender systems, and molecular structures. In this paper, we will introduce the problems that graph convolutional networks have had, such as over-smoothing, and the methods to solve them, and suggest some possible future directions.

Advances in Deep Learning Recognition of Landslides Based on Remote Sensing Images

“…The advent of AlexNet-30 made CNNs the mainstream models for landslide identification [81]. The overall structure of the network model consists of five convolutional layers, three pooling layers, and a fully connected layer [82]. Xia et al [83] used the Wenchuan earthquake landslide area as the study area and used deep learning methods such as seven-layer CNN, AlexNet, ResNet4V152, DenseNet2, InceptionV201, Xception, and Inception ResNetV3 to detect landslides.…”