The identification and segmentation of buildings in remote sensing imagery
has consistently been a important point of academic research. This work
highlights the effectiveness of using diverse datasets and advanced
representation learning models for the purpose of building segmentation in
remote sensing images. By fusing various datasets, we have broadened the scope
of our learning resources and achieved exemplary performance across several
datasets. Our innovative joint training process demonstrates the value of our
methodology in various critical areas such as urban planning, disaster
management, and environmental monitoring. Our approach, which involves
combining dataset fusion techniques and prompts from pre-trained models, sets a
new precedent for building segmentation tasks. The results of this study
provide a foundation for future exploration and indicate promising potential
for novel applications in building segmentation field.

利用不同数据集和先进的表示学习模型在遥感图像中对建筑物进行识别和分割的研究表明，通过融合各种数据集，我们扩大了学习资源的范围，在多个数据集上取得了可观的性能。我们的创新联合训练过程在城市规划、灾害管理和环境监测等关键领域展示了我们方法的价值，结合数据集融合技术和预训练模型的方法为建筑物分割任务树立了新的先例。此研究的结果为未来的探索奠定了基础，并显示了在建筑物分割领域中创新应用的潜在前景。