Integration of GIS and Artificial Intelligence Algorithms in Rural Landscape Protection and Planning

Abstract

This article explored the application of the integration technology of GIS and artificial intelligence (AI) algorithms in rural landscape protection and planning. By analyzing the problems existing in traditional methods, the article elaborated on the necessity and feasibility of combining GIS (Geographic Information System) and CNN (Convolutional Neural Network) algorithms to improve data processing capabilities and strengthen comprehensive analysis capabilities. Through case studies and empirical analysis, the article demonstrated the practical application effect and potential of this fusion technology, providing a new perspective and method for the scientific planning and effective protection of rural landscapes. In the experimental stage, four experiments were designed to evaluate the performance of GIS and CNN fusion. In the first landscape basic feature extraction experiment, the CNN algorithm achieved an accuracy of 95% in extracting features from rural landscape images; the Multi-layer Perceptron algorithm achieved 85%; the RF (Random Forest) achieved an accuracy of 80%; the Support Vector Machine (SVM) achieved 82%. Although the CNN algorithm achieved a processing time of 2 seconds, it had a high accuracy advantage. In the second landscape diversity assessment experiment, the method of integrating GIS and CNN improved species richness by 15%, landscape heterogeneity by 20%, and landscape connectivity by 25%. In landscape change detection experiments, the fusion technology of GIS and CNN has significant advantages in capturing subtle landscape changes. In the experimental data conclusion, the fusion technology of GIS and CNN had a high performance advantage in improving rural planning and management processes.