面向人类聚居区范围回归与局地气候区分类的多任务学习
Multi-task Learning for Human Settlement Extent Regression and Local Climate Zone Classification
AI总结：
针对现有HSE回归与LCZ分类单任务研究存在特征冗余的问题，提出融合共享骨干、任务注意力模块与平衡策略的多任务CNN框架，还引入HSE预测作为LCZ分类先验，经全球13城Sentinel-2数据验证可在两项任务上取得有竞争力的结果。
摘要：
人类聚居区范围（Human Settlement Extent, HSE）图和局地气候区（Local Climate Zone, LCZ）图都是可持续城市发展、城市热岛（Urban Heat Island, UHI）研究等领域的关键数据源。基于遥感（Remote Sensing, RS）与深度学习（Deep Learning, DL）的分类方法因具备全球制图潜力而发挥着重要作用。但现有研究大多仅聚焦两类任务中的一种，且通常针对特定尺度开展，由于学习到的特征可同时支撑这两项相关任务，这种单任务模式会造成不必要的冗余。\n本研究首次将多任务学习（Multi-task Learning, MTL）概念引入HSE回归与LCZ分类任务中，提出了一种MTL框架并构建了端到端卷积神经网络（Convolutional Neural Network, CNN）：该网络包含用于共享特征学习的骨干网络、用于任务专属特征学习的注意力模块，以及用于平衡两项任务的加权策略。此外，研究提出将HSE预测结果作为LCZ分类的先验信息以提升分类精度。\n研究利用全球13座城市的Sentinel-2数据对该MTL方法进行了广泛测试，结果表明，该框架能够为两项任务提供具备竞争力的解决方案。
英文摘要：
Human Settlement Extent (HSE) and Local Climate Zone (LCZ) maps are both essential sources, e.g., for sustainable urban development and Urban Heat Island (UHI) studies. Remote sensing (RS)- and deep learning (DL)-based classification approaches play a significant role by providing the potential for global mapping. However, most of the efforts only focus on one of the two schemes, usually on a specific scale. This leads to unnecessary redundancies, since the learned features could be leveraged for both of these related tasks. In this letter, the concept of multi-task learning (MTL) is introduced to HSE regression and LCZ classification for the first time. We propose a MTL framework and develop an end-to-end Convolutional Neural Network (CNN), which consists of a backbone network for shared feature learning, attention modules for task-specific feature learning, and a weighting strategy for balancing the two tasks. We additionally propose to exploit HSE predictions as a prior for LCZ classification to enhance the accuracy. The MTL approach was extensively tested with Sentinel-2 data of 13 cities across the world. The results demonstrate that the framework is able to provide a competitive solution for both tasks.
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