scholarly journals Digital Earth in Australia

2019 ◽  
pp. 683-711
Author(s):  
Zaffar Sadiq Mohamed-Ghouse ◽  
Cheryl Desha ◽  
Luis Perez-Mora
Keyword(s):  
Data Use ◽  
Open Data ◽  
Data Access ◽  
Lessons Learned ◽  
Data Cube ◽  
Digital Earth ◽  
National Initiatives ◽  
Processing And Storage ◽  
Access To Data ◽  
And Storage

Abstract Australia must overcome a number of challenges to meet the needs of our growing population in a time of increased climate variability. Fortunately, we have unprecedented access to data about our land and the built environment that is internationally regarded for its quality. Over the last two decades Australia has risen to the forefront in developing and implementing Digital Earth concepts, with several key national initiatives formalising our digital geospatial journey in digital globes, open data access and ensuring data quality. In particular and in part driven by a lack of substantial resources in space, we have directed efforts towards world-leading innovation in big data processing and storage. This chapter highlights these geospatial initiatives, including case-uses, lessons learned, and next steps for Australia. Initiatives addressed include the National Data Grid (NDG), the Queensland Globe, G20 Globe, NSW Live (formerly NSW Globe), Geoscape, the National Map, the Australian Geoscience Data Cube and Digital Earth Australia. We explore several use cases and conclude by considering lessons learned that are transferrable for our colleagues internationally. This includes challenges in: 1) Creating an active context for data use, 2) Capacity building beyond ‘show-and-tell’, and 3) Defining the job market and demand for the market.

scholarly journals RESOURCES PROVIDING DATA FOR MACHINE LEARNING AND TESTING ARTIFICIAL INTELLIGENCE TECHNOLOGIES

2021 ◽  
pp. 39-52
Author(s):  
Денис Валерьевич Сикулер
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
World Bank ◽  
Open Data ◽  
Data Access ◽  
Free Access ◽  
Internet Resources ◽  
Data Registry ◽  
Data Portal ◽  
Access To Data

В статье выполнен обзор 10 ресурсов сети Интернет, позволяющих подобрать данные для разнообразных задач, связанных с машинным обучением и искусственным интеллектом. Рассмотрены как широко известные сайты (например, Kaggle, Registry of Open Data on AWS), так и менее популярные или узкоспециализированные ресурсы (к примеру, The Big Bad NLP Database, Common Crawl). Все ресурсы предоставляют бесплатный доступ к данным, в большинстве случаев для этого даже не требуется регистрация. Для каждого ресурса указаны характеристики и особенности, касающиеся поиска и получения наборов данных. В работе представлены следующие сайты: Kaggle, Google Research, Microsoft Research Open Data, Registry of Open Data on AWS, Harvard Dataverse Repository, Zenodo, Портал открытых данных Российской Федерации, World Bank, The Big Bad NLP Database, Common Crawl. The work presents review of 10 Internet resources that can be used to find data for different tasks related to machine learning and artificial intelligence. There were examined some popular sites (like Kaggle, Registry of Open Data on AWS) and some less known and specific ones (like The Big Bad NLP Database, Common Crawl). All included resources provide free access to data. Moreover in most cases registration is not needed for data access. Main features are specified for every examined resource, including regarding data search and access. The following sites are included in the review: Kaggle, Google Research, Microsoft Research Open Data, Registry of Open Data on AWS, Harvard Dataverse Repository, Zenodo, Open Data portal of the Russian Federation, World Bank, The Big Bad NLP Database, Common Crawl.

scholarly journals Land Cover Mapping using Digital Earth Australia

Data ◽  
2019 ◽  
Vol 4 (4) ◽  
pp. 143 ◽  
Author(s):  
Richard Lucas ◽  
Norman Mueller ◽  
Anders Siggins ◽  
Christopher Owers ◽  
Daniel Clewley ◽  
...  
Keyword(s):  
Land Cover ◽  
Open Data ◽  
Google Earth ◽  
Water Levels ◽  
Data Cube ◽  
Observation Data ◽  
Time Step ◽  
Ecosystem Monitoring ◽  
Digital Earth ◽  
Fractional Cover

This study establishes the use of the Earth Observation Data for Ecosystem Monitoring (EODESM) to generate land cover and change classifications based on the United Nations Food and Agriculture Organisation (FAO) Land Cover Classification System (LCCS) and environmental variables (EVs) available within, or accessible from, Geoscience Australia’s (GA) Digital Earth Australia (DEA). Classifications representing the LCCS Level 3 taxonomy (8 categories representing semi-(natural) and/or cultivated/managed vegetation or natural or artificial bare or water bodies) were generated for two time periods and across four test sites located in the Australian states of Queensland and New South Wales. This was achieved by progressively and hierarchically combining existing time-static layers relating to (a) the extent of artificial surfaces (urban, water) and agriculture and (b) annual summaries of EVs relating to the extent of vegetation (fractional cover) and water (hydroperiod, intertidal area, mangroves) generated through DEA. More detailed classifications that integrated information on, for example, forest structure (based on vegetation cover (%) and height (m); time-static for 2009) and hydroperiod (months), were subsequently produced for each time-step. The overall accuracies of the land cover classifications were dependent upon those reported for the individual input layers, with these ranging from 80% (for cultivated, urban and artificial water) to over 95% (for hydroperiod and fractional cover). The changes identified include mangrove dieback in the southeastern Gulf of Carpentaria and reduced dam water levels and an associated expansion of vegetation in Lake Ross, Burdekin. The extent of detected changes corresponded with those observed using time-series of RapidEye data (2014 to 2016; for the Gulf of Carpentaria) and Google Earth imagery (2009–2016 for Lake Ross). This use case demonstrates the capacity and a conceptual framework to implement EODESM within DEA and provides countries using the Open Data Cube (ODC) environment with the opportunity to routinely generate land cover maps from Landsat or Sentinel-1/2 data, at least annually, using a consistent and internationally recognised taxonomy.

scholarly journals An Operational Analysis Ready Radar Backscatter Dataset for the African Continent

2022 ◽  
Vol 14 (2) ◽  
pp. 351
Author(s):  
Fang Yuan ◽  
Marko Repse ◽  
Alex Leith ◽  
Ake Rosenqvist ◽  
Grega Milcinski ◽  
...  
Keyword(s):  
Open Data ◽  
Data Cube ◽  
Observation Data ◽  
Cloud Infrastructure ◽  
Resource Monitoring ◽  
Radar Backscatter ◽  
Digital Earth ◽  
Continental Scale ◽  
Wide Range ◽  
Natural Resource Monitoring

Digital Earth Africa is now providing an operational Sentinel-1 normalized radar backscatter dataset for Africa. This is the first free and open continental scale analysis ready data of this kind that has been developed to be compliant with the CEOS Analysis Ready Data for Land (CARD4L) specification for normalized radar backscatter (NRB) products. Partnership with Sinergise, a European geospatial company and Earth observation data provider, has ensured this dataset is produced efficiently in the cloud infrastructure and can be sustained in the long term. The workflow applies radiometric terrain correction (RTC) to the Sentinel-1 ground range detected (GRD) product, using the Copernicus 30 m digital elevation model (DEM). The method is used to generate data for a range of sites around the world and has been validated as producing good results. This dataset over Africa is made available publicly as a AWS public dataset and can be accessed through the Digital Earth Africa platform and its Open Data Cube API. We expect this dataset to support a wide range of applications, including natural resource monitoring, agriculture, and land cover mapping across Africa.

scholarly journals Data Access for the Social Sciences in Austria. Open Data, Closed Data, Research Infrastructures and Re-Use

2021 ◽  
Vol 50 (1) ◽  
pp. 15
Author(s):  
Matthias Reiter-Pázmándy
Keyword(s):  
Social Sciences ◽  
Research Policy ◽  
Open Data ◽  
Data Access ◽  
Open Science ◽  
Research Data ◽  
The Social ◽  
Research Infrastructures ◽  
Barriers And Challenges ◽  
Access To Data

Open science and open access to research data are important aspects of research policy in Austria. In the last years, the social sciences have seen the building of research infrastructures that generate data and archives that store data. Data standards have been established, several working groups exist and a number of activities aim to further develop various aspects of open science, open data and access to data. However, some barriers and challenges still exist in the practice of sharing research data. One aspect that should be emphasised and incentivised is the re-use of research data.

scholarly journals Collection, Processing and Storage of Bibliographic Records using Semantic Web Technologies

2015 ◽  
pp. 37-42 ◽  
Author(s):  
O. N. Shorin
Keyword(s):  
High Performance ◽  
Open Data ◽  
Semantic Integration ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Bibliographic Records ◽  
Storage Solutions ◽  
Processing And Storage ◽  
And Storage ◽  
Different Sources

Implementation of the project on semantic integration of bibliographic records has allowed to solve urgent problems: there is developed domain ontology and created modules of interaction with a variety of automated library information systems; bibliographic records converted from different formats into RDF, enriched using the information obtained from different sources, and released in accordance with the principles of Linked Open Data. Hand-ling one of the world’s largest arrays of bibliographic records required utilization of highly specialized protocols of access to information, high-performance processing algorithms and scalable storage solutions.

scholarly journals «JUDICIAL CLOUD»: LEGAL ISSUES OF DATA STRUCTURING AND PROTECTION

2019 ◽  
pp. 195-204
Author(s):  
V. A. Laptev ◽  
N. I. Solovyanenko
Keyword(s):  
Cloud Storage ◽  
Digital Data ◽  
Digital Information ◽  
Digital Form ◽  
Cloud Data ◽  
Multiple Servers ◽  
Processing And Storage ◽  
Access To Data ◽  
Cloud Technologies ◽  
And Storage

Formation technologies and cloud database architecture affect legal approaches to the processing and storage of information in digital form. Providing access to data stored in the «cloud» through the Internet made access to user’s information extraterritorial. Modern digital society uses cloud technologies due to the lack of competitive alternatives for remote and secure data storage.In cloud storage, the owner of digital information is not aware of the specific location of the hard disk with digital data. They are stored on multiple servers distributed across the network. Data is available online anytime, anywhere. In this paper, the authors explore the problem of the formation of cloud data. The «judicial cloud» used in the activities of the Russian courts was chosen as a specific subject. This issue is essential for the digital state and business.The paper discovers the cloud architecture and considers the characteristic of the algorithms of the cloud system. Special attention is given to the description of the principles and methods of information security, including in order to ensure the interests of the state. Some aspects of the possible use of artificial intelligence in the administration of the «judicial cloud» are also considered.

scholarly journals USING FREE AND OPEN SOURCE SOFTWARE TO TEACH UNIVERSITY GIS COURSES ONLINE: LESSONS LEARNED DURING A PANDEMIC

2021 ◽  
Vol XLVI-4/W2-2021 ◽  
pp. 127-131
Author(s):  
S. Quinn
Keyword(s):  
Open Source Software ◽  
Hydrological Modeling ◽  
Open Data ◽  
Data Access ◽  
Lessons Learned ◽  
Point Pattern Analysis ◽  
Point Pattern ◽  
Analysis Process ◽  
Cross Platform ◽  
Software And Hardware

Abstract. During the remote learning necessitated by the COVID-19 pandemic, university GIS students did not always have home access to the kinds of software and hardware that they would ordinarily get in their on-campus lab facilities. In this situation, the free and cross-platform nature of FOSS opened the door for some students to continue their GIS education uninterrupted. In this article, I describe how one university allowed students to choose FOSS such as QGIS, PostGIS, and GeoDa as alternatives to proprietary software in upper-division GIS coursework. These were used to teach techniques such as point pattern analysis, visibility analysis, hydrological modeling, proximity surfaces, LISA analysis, process modeling, open data access, and data summation. I share specific software tools, commands, and plugins used to apply these techniques in lab assignments. I discuss how these approaches can form a lasting part of the GIS curriculum beyond the pandemic, and how students can position these FOSS skills as they prepare for the GIS job market.

scholarly journals CBERS DATA CUBE: A POWERFUL TECHNOLOGY FOR MAPPING AND MONITORING BRAZILIAN BIOMES

2020 ◽  
Vol V-3-2020 ◽  
pp. 533-539
Author(s):  
M. C. A. Picoli ◽  
R. Simoes ◽  
M. Chaves ◽  
L. A. Santos ◽  
A. Sanchez ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Data Cube ◽  
Multidimensional Data ◽  
Observation Data ◽  
Earth Observation Data ◽  
Processing And Storage ◽  
Multidimensional Data Cubes ◽  
And Storage

Abstract. Currently, the overwhelming amount of Earth Observation data demands new solutions regarding processing and storage. To reduce the amount of time spent in searching, downloading and pre-processing data, the remote Sensing community is coming to an agreement on the minimum amount of corrections satellite images must convey in order to reach the broadest range of applications. Satellite imagery meeting such criteria (which usually include atmospheric, radiometric and topographic corrections) are generically called Analysis Ready Data (ARD). Furthermore, ARD is being assembled into multidimensional data cubes, minimising preprocessing tasks and allowing scientists and users in general to focus on analysis. A particular instance of this is the Brazil Data Cube (BDC) project, which is processing remote sensing images of medium spatial resolution into ARD datasets and assembling them as multidimensional cubes of the Brazilian territory. For example, BDC users are released from performing tasks such as image co-registration , aerosol interference correction. This work presents a BDC proof of concept, by analysing a BDC data cube made with images from the fourth China-Brazil Earth Resources Satellite (CBERS-4) of one of the largest biodiversity hotspot in the world, the Cerrado biome. It also shows how to map and monitor land use and land cover using the CBERS data cube. We demonstrate that the CBERS data cube is effective in resolving land use and and land cover issues to meet local and national needs related to the landscape dynamics, including deforestation, carbon emissions, and public policies.

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