The task of identifying and segmenting buildings within remote sensing imagery has perennially stood at the forefront of scholarly investigations. This manuscript accentuates the potency of harnessing diversified datasets in tandem with cutting-edge representation learning paradigms for building segmentation in such images. Through the strategic amalgamation of disparate datasets, we have not only expanded the informational horizon accessible for model training but also manifested unparalleled performance metrics across multiple datasets. Our avant-garde joint training regimen underscores the merit of our approach, bearing significant implications in pivotal domains such as urban infrastructural development, disaster mitigation strategies, and ecological surveillance. Our methodology, predicated upon the fusion of datasets and gleaning insights from pre-trained models, carves a new benchmark in the annals of building segmentation endeavors. The outcomes of this research both fortify the foundations for ensuing scholarly pursuits and presage a horizon replete with innovative applications in the discipline of building segmentation.

翻译：遥感影像中建筑识别与分割的任务始终处于学术研究的前沿。本文强调了利用多样化数据集与先进表征学习范式相结合的力量，以实现此类影像中的建筑分割。通过不同数据集的战略性融合，我们不仅扩展了模型训练可获取的信息范围，还在多个数据集上展现了无与伦比的性能指标。我们的创新联合训练方案彰显了所提方法的优势，在城市基础设施建设、减灾策略及生态监测等关键领域具有重要影响。基于数据集融合与预训练模型知识迁移的方法论，为建筑分割研究领域树立了新的标杆。本研究结果既夯实了后续学术探索的基础，也预示着建筑分割学科中创新应用层出不穷的广阔前景。