  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.
