A Transformer Network for Settlement Generalization Integrating Raster Maps and Remote Sensing Images

To address the challenges of reliance on manual expertise and insufficient automation and intelligence in settlement generalization during scale transformation, this paper proposes a multi-scale settlement generalization model (DSG-Trans) based on the fusion of dual-source data: raster maps and remote sensing images. Leveraging the cartographic correspondence between remote sensing image resolution and map scale, this model integrates source-scale maps with specific low-resolution remote sensing imagery to automatically generate target-scale raster maps that adhere to cartographic specifications. The model employs a bidirectional cross-attention mechanism and a Transformer architecture to facilitate the semantic integration of dual-source data, while a progressive upsampling strategy is utilized to reconstruct spatial details. Additionally, a composite loss function incorporating four constraints is designed to enhance the quality of settlement generalization. Experiments were conducted in New York City, USA, using a multi-source dataset constructed from 3D-GloBFP building footprint data and multi-source remote sensing imagery, focusing on two scale transformation scenarios: 1:5000 to 1:10,000 and 1:5000 to 1:25,000. The results indicate that the proposed method effectively learns and simulates expert generalization knowledge, significantly improving the automation level and scale adaptability of map generalization while preserving geometric morphology and spatial distribution patterns.