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Natural disasters such as earthquakes and extreme hydrological events often cause severe damage to transportation lifelines and trigger secondary geological disasters including landslides. The rapid and accurate acquisition of disaster information is of crucial importance to emergency rescue efforts. However, current research on intelligent remote sensing interpretation for disasters is severely constrained by the scarcity of post-disaster damaged samples and the poor generalization ability of landslide detection models in low-vegetation areas. To address these challenges, this study constructs a high-resolution remote sensing dataset for detecting damaged transportation elements and landslides, covering typical regions such as the China-Pakistan Economic Corridor and Southeast Asia. This dataset integrates three core disaster elements—damaged roads, damaged bridges and landslides—into a unified framework: to tackle the shortage of damaged samples for roads and bridges, a Stable Diffusion model based on topological constraints is proposed to generate high-fidelity synthetic images; for landslides in low-vegetation areas, a sample set for complex arid mountainous areas is established through multiple rounds of cross visual interpretation, based on high-resolution post-disaster satellite images and historical vector data. In addition, authentic post-disaster images are used to supplement the datasets of the three types of elements. After standardization processing including unified sizing and mask binarization, as well as stringent quality control, a high-quality dataset is finally formed, which contains 9102 pairs of road samples, 6061 pairs of bridge samples and 7614 pairs of landslide samples. This dataset fills the gap in high-quality annotated samples of post-disaster damaged transportation elements, supplements landslide samples in low-vegetation areas, and provides reliable multi-scenario data support for the training and validation of intelligent remote sensing interpretation algorithms for disasters and the implementation of post-disaster emergency assessment.