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Serverless Geospatial Data Processing Workflow System Design

Geospatial data and related technologies have become an increasingly important aspect of data analysis processes, with their prominent role in most of them. Serverless paradigm have become the most popular and frequently used technology within cloud computing. This paper reviews the serverless paradigm and examines how it could be leveraged for geospatial data processes by using open standards in the geospatial community. We propose a system design and architecture to handle complex geospatial data processing jobs with minimum human intervention and resource consumption using serverless technologies. In order to define and execute workflows in the system, we also propose new models for both workflow and task definitions models. Moreover, the proposed system has new Open Geospatial Consortium (OGC) Application Programming Interface (API) Processes specification-based web services to provide interoperability with other geospatial applications with the anticipation that it will be more commonly used in the future. We implemented the proposed system on one of the public cloud providers as a proof of concept and evaluated it with sample geospatial workflows and cloud architecture best practices.

Open Ocean Cloud : Open Data and Computing Infrastructure for the UN Ocean Decade

To leverage our past investments in ocean observations and modeling, and to fully exploit new observations, we must transform our infrastructure and tools for working with ocean data. Currently, data intensive ocean research is only accessible to privileged institutions with the resources for high performance computing and data storage. OpenOceanCloud will break down this barrier, providing a research platform to the thousands of potential oceanographers who lack such resources. Access to vast data sets and powerful computing environments can help remove the barriers related to low-bandwidth internet, intermittent power, and limited cyber infrastructure. With this infrastructure, anyone can do science, anywhere, and this empowers communities that have been historically excluded from full participation in oceanography.

Towards Environmental Digital Twins in Azure with Dask and Pangeo

Digital Twins of the environment can help reaching sustainability goals and tackling climate change related issues. They will strongly rely on geospatial data, and the processing and analytics thereof. Cloud environments provides the flexibility and scalability needed to cope with the potential enormous geospatial datasets. This article explores the Azure cloud capabilities, and places them in a broader multi cloud perspective.

Available Now: Machine Learning for Earth Observation Online Course

Radiant Earth’s first online course aims to strengthen practitioners’ capacity and skills to create impactful machine learning applications.

Can space-based technologies help manage and prevent pandemics?

Nature article: Earth Observation Epidemiology or tele-epidemiology is defined as ‘using space technology with remote sensing in epidemiology. It is a useful tool that is increasingly being used by clinicians and stakeholders for zoonotic infections1,2,4. Tele-epidemiology helped map out the spread of the Ebola virus among animals and can be used for risk mapping, risk communication and identifying vulnerable populations. Similarly, geographic information science technology can improve the understanding and control of COVID-19 through surveillance, data sharing, digital contact tracing and investigation of risk factors and infectious disease forecasting

Countries are using forests to pad their climate commitments. New satellite images might call their bluff

Countries are using forests to pad their climate commitments. New satellite images might call their bluff

CJRS’ Special Issue on Deep Learning for Environmental Applications of Remote Sensing Data

This Special Issue covers a broad range of topics, such as transfer learning, design of new Deep Neural Network (DNN), CNN, and GAN models, as well as a wide range of applications (Table 1), including agriculture (four papers), natural resources (three papers), marine environments (two papers), change detection (one paper), and disaster damage detection (one paper).

Radiant Earth’s Online Course on Machine Learning for Earth Observation

ML4EO Training given by Radiant Earth. Designed to strengthen practitioners’ local capacity and skills in support of creating impactful machine learning applications

STAC Specification 1.0.0 Released

The SpatioTemporal Asset Catalog (STAC) community is pleased to announce the release of version 1.0.0

21st Century Digital Skills: Competencies, Innovations and Curriculum in Canada

The rapid advance of digital technologies has significantly impacted education in recent years. It is evident that the increasing digitization of the economy and society will require students to become comfortable with technology to prepare for the future. In turn, this also requires teachers to be supported to develop the skills and knowledge required to fully utilize the capabilities of technology, whether in the classroom or in a hybridized model that utilizes distributed online learning

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