新疆阜康市草地生态赤字及成因分析

应用生态足迹法研究了新疆阜康市总体生态安全以及草地生态安全状况,剖析了阜康市草地生态赤字的成因,并在此基础上提出了应对策略。研究结果表明:1981年到2004年,阜康市总的可利用人均生态承载力降低了13%,而总的人均生态足迹升高了180%,生态赤字逐年加大,总体生态安全形势严峻。其中,草地生态赤字问题特别突出,草地人均生态承载力从1981年的0.2486 hm2减少到2004年的0.1374 hm2,减少44.8%,而草地人均生态足迹则从1981年的0.2456 hm2增加到2004年的1.2186 hm2,增加500%,草地生态严重赤字,已成为最主要的生态不安全因素。造成草地生态严重赤字的原因是工业化和城市化的迅猛发展,必须通过提高草地资源利用效率、增加草地生产用地的面积、提高草地的质量、控制人口数量等措施最终实现草地生态系统的可持续利用

CSCL: Critical Semantic-Consistent Learning for Unsupervised Domain Adaptation

Unsupervised domain adaptation without consuming annotation process for unlabeled target data attracts appealing interests in semantic segmentation. However, 1) existing methods neglect that not all semantic representations across domains are transferable, which cripples domain-wise transfer with untransferable knowledge; 2) they fail to narrow category-wise distribution shift due to category-agnostic feature alignment. To address above challenges, we develop a new Critical Semantic-Consistent Learning (CSCL) model, which mitigates the discrepancy of both domain-wise and category-wise distributions. Specifically, a critical transfer based adversarial framework is designed to highlight transferable domain-wise knowledge while neglecting untransferable knowledge. Transferability-critic guides transferability-quantizer to maximize positive transfer gain under reinforcement learning manner, although negative transfer of untransferable knowledge occurs. Meanwhile, with the help of confidence-guided pseudo labels generator of target samples, a symmetric soft divergence loss is presented to explore inter-class relationships and facilitate category-wise distribution alignment. Experiments on several datasets demonstrate the superiority of our model. © 2020, Springer Nature Switzerland AG