As high-grade gold deposits are progressively depleted, the proportion of refractory gold ores in total reserves is continuously increasing, making gold recovery from refractory ores an inevitable trend in the future development of the gold industry. This study briefly analyzes the challenges faced during the leaching process of refractory gold ores under ambient conditions, and provides a detailed discussion on two acidic pretreatment technologies—pressure oxidation and bio-oxidation—as well as three acidic gold recovery technologies—thiosulfate leaching process, halogen leaching process, and thiocyanate leaching process. Additionally, this paper compares and analyzes the advantages and limitations of these acidic pretreatment and hydrometallurgical gold recovery technologies. The goal is to provide a comprehensive review of pretreatment technologies and leaching agents for refractory gold ores under acidic conditions (pH = 1–5), offering a reference for selecting appropriate treatment processes in the future, and to explore the potential development of acidic pretreatment and recovery technologies for refractory gold ores.

Remote Sensing Image Change Captioning (RSICC) aims to generate natural language descriptions of changes in bi-temporal remote sensing images, providing more semantically interpretable results than conventional pixel-level change detection methods. However, existing approaches often rely on stacked Transformer modules, leading to suboptimal feature discrimination. Moreover, direct difference computation after feature extraction tends to retain task-irrelevant noise, limiting the model’s ability to capture meaningful changes. This study proposes a novel cross-spatial Transformer and symmetric difference localization network (CTSD-Net) for RSICC to address these limitations. The proposed Cross-Spatial Transformer adaptively enhances spatial-aware feature representations by guiding the model to focus on key regions across temporal images. Additionally, a hierarchical difference feature integration strategy is introduced to suppress noise by fusing multi-level differential features, while residual-connected high-level features serve as query vectors to facilitate bidirectional change representation learning. Finally, a causal Transformer decoder creates accurate descriptions by linking visual information with text. CTSD-Net achieved BLEU-4 scores of 66.32 and 73.84 on the LEVIR-CC and WHU-CDC datasets, respectively, outperforming existing methods in accurately locating change areas and describing them semantically. This study provides a promising solution for enhancing interpretability in remote sensing change analysis.