Geospatial Image Classification and Recognition Based on Remote Sensing Images

This study aims to explore methods for geographic spatial image classification and recognition based on remote sensing images. Experiments were conducted using the EuroSAT dataset, combining traditional pixel and object-based classification methods with deep learning techniques such as Convolutional Neural Networks (CNNs) to classify and recognize remote sensing images. During the experiments, four models—VGGNet16, MobileNetV2, DenseNet121, and ResNet50—were used. Optimization techniques, such as adjusting the learning rate and modifying activation functions, were applied to improve the models' performance and accuracy. The final results indicate that deep learning methods have significant advantages in geographic spatial image classification and recognition. Through reasonable optimization strategies, the classification effect can be significantly enhanced. This study provides effective methods for geographic spatial information extraction and offers scientific evidence for resource management, environmental monitoring, and urban planning.