New computing technologies inspired by the brain promise fundamentally
different ways to process information with extreme energy efficiency and the
ability to handle the avalanche of unstructured and noisy data that we are
generating at an ever-increasing rate. To realise this promise requires a brave
and coordinated plan to bring together disparate research communities and to
provide them with the funding, focus and support needed. We have done this in
the past with digital technologies; we are in the process of doing it with
quantum technologies; can we now do it for brain-inspired computing?

新的计算技术如何应用于处理信息，提高计算的效率和处理海量数据能力，实现这个目标需要勇气和协作计划，将分散的研究社区聚集在一起，为其提供资金、支持和关注。

脑启发式计算：我们需要一个总体规划

Brain-inspired computing: We need a master plan

The sharp and recent increase in the availability of data captured by
different sensors combined with their considerably heterogeneous natures poses
a serious challenge for the effective and efficient processing of remotely
sensed data. Such an increase in remote sensing and ancillary datasets,
however, opens up the possibility of utilizing multimodal datasets in a joint
manner to further improve the performance of the processing approaches with
respect to the application at hand. Multisource data fusion has, therefore,
received enormous attention from researchers worldwide for a wide variety of
applications. Moreover, thanks to the revisit capability of several spaceborne
sensors, the integration of the temporal information with the spatial and/or
spectral/backscattering information of the remotely sensed data is possible and
helps to move from a representation of 2D/3D data to 4D data structures, where
the time variable adds new information as well as challenges for the
information extraction algorithms. There are a huge number of research works
dedicated to multisource and multitemporal data fusion, but the methods for the
fusion of different modalities have expanded in different paths according to
each research community. This paper brings together the advances of multisource
and multitemporal data fusion approaches with respect to different research
communities and provides a thorough and discipline-specific starting point for
researchers at different levels (i.e., students, researchers, and senior
researchers) willing to conduct novel investigations on this challenging topic
by supplying sufficient detail and references.

本文总结了多个研究社区关于多源、多时间数据融合方法的进展，为不同层次（例如学生、研究人员和高级研究人员）的研究者提供了充足的细节和参考，以便于他们在这个具有挑战性的课题上进行新的调查。