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GEE,ISPRS,2020

The ISPRS Journal of Photogrammetry and Remote Sensing (P&RS) is the official journal of the International Society for Photogrammetry and Remote Sensing (ISPRS). The Journal provides a channel of communication for scientists and professionals in all countries working in the many disciplines that employ photogrammetry, remote sensing, spatial information systems, computer vision, and related fields. The Journal is designed to serve as a source reference and archive of advancements in these disciplines. The P&RS objective is to publish high quality, peer-reviewed, preferably previously unpublished papers of a scientific/research, technological development or application/practical nature. P&RS will publish papers, including those based on ISPRS meeting presentations*, which are regarded as significant contributions in the above-mentioned fields. We especially encourage papers: of broad scientific interest; on innovative applications, particularly in new fields; of an interdisciplinary nature; on topics that have not been dealt with (or to a small degree) by P&RS or related journals; and on topics related to new possible scientific/professional directions. Preferably, theoretical papers should include applications, and papers dealing with systems and applications should include theoretical background. The scope of the journal is extensive and covers sensors, theory and algorithms, systems, experiments, developments and applications. Topics of interest include but are not limited to: Sensors: • Airborne and spaceborne multispectral and hyperspectral imaging systems • Airborne and terrestrial cameras • Airborne, terrestrial and mobile laser scanning • Range imaging • Active and passive imaging sensor characterisation • Sensor calibration and standardisation • Geosensor networks • Internet of Things Methods and procedures: • Spatial data handling technologies • Integrated sensor calibration and orientation • Surface and object reconstruction, modelling and interpretation • GIS data modelling, representation and structur

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python google app engine

云计算的三个层次:issa:paas:saas 云计算有三个层次。图12-1 显示了每个层次,以及对应层次的代表产品。最低层的是IaaS(Infrastructure-as-a-Service),即提供计算机本身基本的计算能力(物理形式或虚拟形式)、存储(通常是磁盘)、计算。亚马逊Web 服务(Amazon Web Services,AWS)提供了弹性计算云(Elastic Compute Cloud,EC2),以及简单存储系统(Simple Storage System,S3)服务,这两者就在IaaS 层面。Google 也提供了IaaS 存储服务,称为Google Cloud Storage。Google App Engine 作为云计算的中间一层,称为Paas(Platform-as-a-Service)。这一层为用户的应用提供执行平台。最高一层是Software-as-a-Service(SaaS)。在这一层,用户只须简单地访问应用,这些应用位于本地,但只能通过因特网访问。SaaS 的例子包括基于Web的电子邮件服务,如Gmail、Yahoo! Mail 和Hotmail。

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《Scikit-Learn、Keras与TensorFlow机器学习实用指南(第二版)》第19章 规模化训练和部署TensorFlow模型

有了能做出惊人预测的模型之后,要做什么呢?当然是部署生产了。这只要用模型运行一批数据就成,可能需要写一个脚本让模型每夜都跑着。但是,现实通常会更复杂。系统基础组件都可能需要这个模型用于实时数据,这种情况需要将模型包装成网络服务:这样的话,任何组件都可以通过REST API询问模型。随着时间的推移,你需要用新数据重新训练模型,更新生产版本。必须处理好模型版本,平稳地过渡到新版本,碰到问题的话需要回滚,也许要并行运行多个版本做AB测试。如果产品很成功,你的服务可能每秒会有大量查询,系统必须提升负载能力。提升负载能力的方法之一,是使用TF Serving,通过自己的硬件或通过云服务,比如Google Cloud API平台。TF Serving能高效服务化模型,优雅处理模型过渡,等等。如果使用云平台,还能获得其它功能,比如强大的监督工具。

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