Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting 巴拉巴拉 积极向上的懒人1.创新点 第一篇将图卷积用于提取空间和时间信息的文章,没有使用正则卷积和递归单元,使用了完整的卷积结构,在更少的参数下,可以得到更快的训练速度 2.问题描述 通过前[t-m+1,t] 的交通流量...
Deep-Learning-Based Spatio-Temporal-Spectral Integrated Fusion of Heterogeneous Remote Sensing Image from:TGRS,2022 动机 空间光谱融合(Spatiospectral fusion)和时空融合(Spatiotemporal fusion)仅用于融合来自两个空间、时间和光谱域的信息。→ 时空谱融合 由于多源数据集之间的复杂和非线性关系,目前对集成融合方法的...
其中fspatial(⋅)fspatial(·)表示X到~XX~的空间退化关系,通常假设为模糊降采样操作[6],可以用模糊降采样矩阵A和噪声N表示. ftemporal(⋅)ftemporal(·)表示从 X 到 Z 的时间关系,通常假定为线性模型 [29]、[31]。 fheterogeneous(⋅)fheterogeneous(·)表示 X 和 Y 之间的异质关系,目前很难明确表达。
块可以根据情况的规模和复杂性进行堆叠或扩展。 如上图,中间的spatial层是连接两个temporal层,这两个时间层可以实现从图卷积到时间卷积的快速空间状态传播。 “三明治”结构还有助于网络充分应用瓶颈策略,以通过图卷积层对通道C进行按比例缩小/放大来实现比例压缩和特征压缩。 此外,在每个ST-Conv块内利用层归一化来...
The deep learning based pasture prediction model (DeepPaSTL) is trained on this dataset while learning the spatiotemporal characteristics of pasture growth. The architecture purely learns from the trends in pasture growth through available spatial measurements and is agnostic to any site...
时空深度学习Deep Learning for Spatio-Temporal Data张钧波京东智能城市研究院市 京东城市 时空 AI 产品部2020 年7 7 月 22 日2020城市计算夏令营 阅读了该文档的用户还阅读了这些文档 20 p. 无源互调感知波束形成 12 p. 为片上系统布置供电 1 p. 电芯模组及电池组 22 p. 用于管理电信系统中的终端...
MULTITASK DEEP LEARNING MDL框架,由三个组件组成,分别用于数据转换、节点流建模和边缘流建模 我们首先将地图上沿时间方向的轨迹(或行程)数据转换为两种类型的流 :i)节点流为张量时间有序序列(Step (1a)); ii)边流为图的时间有序序列(转移矩阵) (步骤(2a)),将其转化为张量序列(步骤(2b))。然后将这两种类型...
To achieve an accurate and holistic prediction of the short and mid-term SST field, a spatiotemporal deep learning model is proposed which can capture the correlations of SST across both space and time. The model uses the convolutional long short-term memory (ConvLSTM) as the building block ...
the problem of the interpolation of continuous spatio-temporal fields measured on a set of irregular points in space is still under-investigated. To fill this gap, we introduce here a framework for spatio-temporal prediction of climate and environmental data using deep learning. Specifically, we sh...
Deep learning has recently gained attention in atmospheric and oceanic sciences for its potential to improve the accuracy of numerical simulations and reduce computational costs. Super-resolution is one such technique, which obtains high-resolution inference from low-resolution data. This paper proposes ...