Integrating Natural Language Processing With Vision Transformer for Landscape Character Identification

Landscape character identification (LCI) has traditionally relied on manual methods to integrate and visually interpret multiple layers of natural and cultural data across regions. While effective, these methods are constrained by subjectivity in manual categorization and face challenges in scalability and consistency when applied to larger regions. In this article, we propose a novel deep learning-based LCI method that leverages natural language processing (NLP) to enable greater flexibility in identifying landscape types through natural language guidance. Focusing on the Western Sichuan Plains, our approach integrates nine geographic information system-derived landscape elements with natural language descriptions that specify desired styles of landscape characteristics. The model features three key components: a transformer-based module for extracting natural language features, a vision transformer (ViT) for spatial feature extraction, and a feature pyramid network for decision-making. Through multimodal information fusion across the feature extraction and decision-making stages, natural language inputs effectively guide the prioritization of landscape attributes and control the granularity of the generated landscape character maps. Visualization experiments demonstrate the model's capability to produce accurate and detailed landscape character maps, with a particular emphasis on identifying agricultural landscapes in the Western Sichuan Plains. This study validates the potential of integrating NLP into LCI, offering a significant advancement in precision and adaptability for landscape characterization.