TL;DR
Experimental results on the ISPRS Potsdam and Vaihingen benchmark datasets show that the proposed CNN-Transformer model attains highly competitive segmentation performance while maintaining computational efficiency.
These findings validate the effectiveness of combining CNNs and Transformer mechanisms in advancing automatic land use recognition and provide a promising pathway for scalable applications in large-scale remote sensing analysis.
High-resolution remote sensing images present considerable challenges for semantic segmentation due to their complex object structures and extensive spatial distribution. Effective segmentation requires capturing fine-grained local details while simultaneously modeling long-range dependencies. Convolutional Neural Netw...
With the rapid development of high-resolution remote sensing platforms and the growing demand for intelligent Earth observation, remote sensing image semantic segmentation has become a key technology for road extraction, urban–rural planning, disaster monitoring, and ecological assessment. However, complex remote sensi...
Cropland semantic change detection (CSCD) is crucial for monitoring agricultural land dynamics by identifying pixel-level “from-to” transitions in bitemporal high-resolution remote sensing (RS) images. Unlike general semantic change detection (SCD), CSCD requires distinguishing genuine land cover changes from pseudocha...
Road extraction from high-resolution remote sensing images is crucial for urban planning and geographic information systems (GIS). However, complex background interference, severe occlusions, and the inherent morphological complexity of roads often lead to discontinuities and insufficient accuracy in extraction results...
This research introduces an advanced deep learning framework, GeoFusion-ChangeNet, for high-resolution land cover mapping and automatic change detection, which integrates hierarchical convolutional feature encoder with a residual deep feature backbone to effectively capture rich multi-scale spatial features.
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