Land-cover information reflects basic Earth’s surface environments and is critical to human settlements. As a well-established deep learning architecture, the fully convolutional network has achieved impressive progress in various land-cover mapping tasks. However, most research has focused on designing powerful encoders, ignoring the exploration of decoders. The existing handcrafted decoders are relatively simple and lack flexibility, limiting the generalizability for complex remote sensing scenes. In this paper, we propose a Land-cOVEr mapping Neural Architecture Search framework (LoveNAS) to automatically find efficient decoders that are compatible with the encoders and tasks. Specifically, LoveNAS introduces a hierarchical dense search space, including densely connected layer-level and multi-scale operation-level search spaces. The search spaces contain independent connection and operation fusion strategies, facilitating sufficient interaction of multi-scale features. After searching based on large-scale datasets, a series of pre-trained encoders and adaptive decoders are obtained. These can be smoothly applied to multi-scene tasks using weight-transfer network training. Experimental results on normal and disaster scenes shows that LoveNAS outperforms 16 handcrafted architectures and NAS methods. Some searched structures coincide with the existing advanced artificial designs, revealing the potential value of LoveNAS in network design and guidance. Group’s website: . GitHub page: .

中文翻译：

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LoveNAS：通过分层搜索自适应网络实现多场景土地覆盖测绘

土地覆盖信息反映了地球表面的基本环境，对人类住区至关重要。作为一种成熟的深度学习架构，全卷积网络在各种土地覆盖测绘任务中取得了令人印象深刻的进展。然而，大多数研究都集中在设计强大的编码器上，而忽略了对解码器的探索。现有的手工解码器相对简单且缺乏灵活性，限制了复杂遥感场景的通用性。在本文中，我们提出了一种 Land-cOVER 映射神经架构搜索框架（LoveNAS）来自动找到与编码器和任务兼容的高效解码器。具体来说，LoveNAS引入了分层密集搜索空间，包括密集连接的层级和多尺度操作级搜索空间。搜索空间包含独立的连接和操作融合策略，促进多尺度特征的充分交互。经过基于大规模数据集的搜索，得到了一系列预训练的编码器和自适应解码器。这些可以使用权重转移网络训练顺利地应用于多场景任务。正常和灾难场景的实验结果表明，LoveNAS 优于 16 种手工设计的架构和 NAS 方法。一些搜索到的结构与现有的先进人工设计相吻合，揭示了LoveNAS在网络设计和指导方面的潜在价值。集团网站： . GitHub 页面： .