The ever-increasing sensor service, though opening a precious path and providing a deluge of earth system data for deep-learning-oriented earth science, sadly introduce a daunting obstacle to their industrial level deployment. Concretely, earth science systems rely heavily on the extensive deployment of sensors, however, the data collection from sensors is constrained by complex geographical and social factors, making it challenging to achieve comprehensive coverage and uniform deployment. To alleviate the obstacle, traditional approaches to sensor deployment utilize specific algorithms to design and deploy sensors. These methods dynamically adjust the activation times of sensors to optimize the detection process across each sub-region. Regrettably, formulating an activation strategy generally based on historical observations and geographic characteristics, which make the methods and resultant models were neither simple nor practical. Worse still, the complex technical design may ultimately lead to a model with weak generalizability. In this paper, we introduce for the first time the concept of spatio-temporal data dynamic sparse training and are committed to adaptively, dynamically filtering important sensor distributions. To our knowledge, this is the first proposal (termed DynST) of an industry-level deployment optimization concept at the data level. However, due to the existence of the temporal dimension, pruning of spatio-temporal data may lead to conflicts at different timestamps. To achieve this goal, we employ dynamic merge technology, along with ingenious dimensional mapping to mitigate potential impacts caused by the temporal aspect. During the training process, DynST utilize iterative pruning and sparse training, repeatedly identifying and dynamically removing sensor perception areas that contribute the least to future predictions.

翻译：传感器服务的持续增长，虽然为基于深度学习的地球科学提供了宝贵途径和海量地球系统数据，但遗憾的是，也为其工业级部署带来了巨大障碍。具体而言，地球科学系统高度依赖传感器的广泛部署，然而，受复杂地理和社会因素的制约，数据采集难以实现全面覆盖和均匀部署。为缓解这一障碍，传统的传感器部署方法采用特定算法来设计和部署传感器。这些方法动态调整传感器的激活时间，以优化每个子区域的检测过程。遗憾的是，制定激活策略通常基于历史观测和地理特征，这使得方法及最终模型既不简便也不实用。更糟糕的是，复杂的技术设计最终可能导致模型泛化能力薄弱。本文首次提出时空数据动态稀疏训练的概念，致力于自适应、动态地过滤重要的传感器分布。据我们所知，这是首次在数据层面提出工业级部署优化概念（称为DynST）。然而，由于时间维度的存在，对时空数据进行剪枝可能导致不同时间戳上的冲突。为实现这一目标，我们采用动态合并技术，并结合巧妙的维度映射来减轻时间维度带来的潜在影响。在训练过程中，DynST利用迭代剪枝和稀疏训练，反复识别并动态移除对未来预测贡献最小的传感器感知区域。