Geospatial Big Data: Challenges and Opportunities

Lee, Jae-Gil; Kang, Minseo

doi:10.1016/j.bdr.2015.01.003

Cited by 357 publications

(196 citation statements)

References 11 publications

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“…Examples include Amazon's cloud storage and web services, Microsoft Azure cloud, ESRI's ArcGIS Online, GeoNode, GIS Cloud, and CartoDB. The cloud provides a platform to manage and manipulate Big Geospatial Data which is a characteristic of the growing volumes of geospatial data being collected through sensors and mobile devices (Lee and Kang, 2015).…”

Section: Framework For Geoweb Terminologymentioning

confidence: 99%

What Iswebmapping Anyway?

Veenendaal

Brovelli

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Although maps have been around for a very long time, web maps are yet very young in their origin. Despite their relatively short history, web maps have been developing very rapidly over the past few decades. The use, users and usability of web maps have rapidly expanded along with developments in web technologies and new ways of mapping. In the process of these developments, the terms and terminology surrounding web mapping have also changed and evolved, often relating to the new technologies or new uses. Examples include web mapping, web GIS, cloud mapping, internet mapping, internet GIS, geoweb, map mashup, online mapping etc., not to mention those with prefixes such as "web-based" and "internet-based". So, how do we keep track of these terms, relate them to each other and have common understandings of their meanings so that references to them are not ambiguous, misunderstood or even different? This paper explores the terms surrounding web mapping and web GIS, and the development of their meaning over time. The paper then suggests the current context in which these terms are used and provides meanings that may assist in better understanding and communicating using these terms in the future.

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Section: Framework For Geoweb Terminologymentioning

confidence: 99%

What Iswebmapping Anyway?

Veenendaal

Brovelli

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…The progress and innovation is no longer hindered by the ability to collect data. The most important issue is how we exploit these geospatial big data (Lee and Kang, 2015). We consider that, we are facing the paradigm shift from datadriven research to knowledge-driven scientific method in Big Data which was considered as a challenge by R. Kitchin in 2014.…”

Section: Geospatial Big Datamentioning

confidence: 99%

“…An additional particular kind of location-aware data is also examined by analysts; social media-like data which requires a particular approach to collect and process as well. Along with Big Data theory geospatial big data is defined as volume, variety and update frequency rate that exceed the capability of spatial computing technology (Lee and Kang, 2015, Li et al, 2015, Kambatla et al, 2014. In Table 1 According to the previously mentioned definitions and characteristics of Big Data and Geospatial Big Data represented in the table are reasonable.…”

Section: Defining Geospatial Big Datamentioning

confidence: 99%

A New Initiative for Tiling, Stitching and Processing Geospatial Big Data in Distributed Computing Environments

Olasz¹,

Thai

Kristóf³

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Within recent years, several new approaches and solutions for Big Data processing have been developed. The Geospatial world is still facing the lack of well-established distributed processing solutions tailored to the amount and heterogeneity of geodata, especially when fast data processing is a must. The goal of such systems is to improve processing time by distributing data transparently across processing (and/or storage) nodes. These types of methodology are based on the concept of divide and conquer. Nevertheless, in the context of geospatial processing, most of the distributed computing frameworks have important limitations regarding both data distribution and data partitioning methods. Moreover, flexibility and expendability for handling various data types (often in binary formats) are also strongly required. This paper presents a concept for tiling, stitching and processing of big geospatial data. The system is based on the IQLib concept (https://github.com/posseidon/IQLib/) developed in the frame of the IQmulus EU FP7 research and development project (http://www.iqmulus.eu). The data distribution framework has no limitations on programming language environment and can execute scripts (and workflows) written in different development frameworks (e.g. Python, R or C#). It is capable of processing raster, vector and point cloud data. The above-mentioned prototype is presented through a case study dealing with country-wide processing of raster imagery. Further investigations on algorithmic and implementation details are in focus for the near future.

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“…the work of the US National Institute of Standards and Technology -NIST (bigdatawg.nist.gov)) on the general level and (Lee and Kang, 2015) for a representative example that is particularly related to Big Geospatial Data), the underlying technologies are still evolving, and their landscape remains dynamic. We might expect stabilization only in the medium term.…”

Section: Overall Landscapementioning

confidence: 99%

Big Data breaking barriers - first steps on a long trail

Schade

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Most data sets and streams have a geospatial component. Some people even claim that about 80% of all data is related to location. In the era of Big Data this number might even be underestimated, as data sets interrelate and initially non-spatial data becomes indirectly geo-referenced. The optimal treatment of Big Data thus requires advanced methods and technologies for handling the geospatial aspects in data storage, processing, pattern recognition, prediction, visualisation and exploration. On the one hand, our work exploits earth and environmental sciences for existing interoperability standards, and the foundational data structures, algorithms and software that are required to meet these geospatial information handling tasks. On the other hand, we are concerned with the arising needs to combine human analysis capacities (intelligence augmentation) with machine power (artificial intelligence). This paper provides an overview of the emerging landscape and outlines our (Digital Earth) vision for addressing the upcoming issues. We particularly request the projection and re-use of the existing environmental, earth observation and remote sensing expertise in other sectors, i.e. to break the barriers of all of these silos by investigating integrated applications.

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Geospatial Big Data: Challenges and Opportunities

Cited by 357 publications

References 11 publications

What Iswebmapping Anyway?

What Iswebmapping Anyway?

A New Initiative for Tiling, Stitching and Processing Geospatial Big Data in Distributed Computing Environments

Big Data breaking barriers - first steps on a long trail

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