Remote sensing big data computing: Challenges and opportunities

Ma, Yan; Wu, Haiping; Wang, Lizhe; Huang, Bormin; Ranjan, Rajiv; Zomaya, Albert Y.; Jie, Wei

doi:10.1016/j.future.2014.10.029

Cited by 525 publications

(251 citation statements)

References 69 publications

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“…For example, in urban planning, there is an interest in current land cover and land use data objects at various spatial and temporal hierarchies. Easy and efficient programing of these systems can be challenging (Ma et al, 2014). GIS experts are focused on accurate mappings and handling relevant big data related to human and natural risks.…”

Section: Challengesmentioning

confidence: 99%

Topologically Consistent Models for Efficient Big Geo-Spatio-Temporal Data Distribution

Jahn

Bradley

Doori

et al. 2017

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

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ABSTRACT:Geo-spatio-temporal topology models are likely to become a key concept to check the consistency of 3D (spatial space) and 4D (spatial + temporal space) models for emerging GIS applications such as subsurface reservoir modelling or the simulation of energy and water supply of mega or smart cities. Furthermore, the data management for complex models consisting of big geo-spatial data is a challenge for GIS and geo-database research. General challenges, concepts, and techniques of big geo-spatial data management are presented. In this paper we introduce a sound mathematical approach for a topologically consistent geo-spatio-temporal model based on the concept of the incidence graph. We redesign DB4GeO, our service-based geo-spatio-temporal database architecture, on the way to the parallel management of massive geo-spatial data. Approaches for a new geo-spatio-temporal and object model of DB4GeO meeting the requirements of big geo-spatial data are discussed in detail. Finally, a conclusion and outlook on our future research are given on the way to support the processing of geo-analytics and -simulations in a parallel and distributed system environment.

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Section: Challengesmentioning

confidence: 99%

Topologically Consistent Models for Efficient Big Geo-Spatio-Temporal Data Distribution

Jahn

Bradley

Doori

et al. 2017

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

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“…Remote-sensing is a powerful and cost-effective data source for assessing the spatial and temporal dynamics of LULC [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Digital archives of remotely sensed data provide an excellent opportunity to study historical land use/cover changes and to relate their spatio-temporal patterns to environmental and human factors.…”

Section: Remote-sensing For Analyzing Lulc Changementioning

confidence: 99%

“…Digital archives of remotely sensed data provide an excellent opportunity to study historical land use/cover changes and to relate their spatio-temporal patterns to environmental and human factors. With the rapid development of Earth observation techniques, it has become convenient to obtain a large number of remotely-sensed imagery over a certain area at different times, from hundreds of Earth observation platforms [12]. However, this brings challenges to researchers to timely process this large amount of remote-sensing data as well as to rapidly transfer data into information and knowledge [13].…”

Section: Remote-sensing For Analyzing Lulc Changementioning

confidence: 99%

Mapping Typical Urban LULC from Landsat Imagery without Training Samples or Self-Defined Parameters

Wang

Zhong

et al. 2017

Remote Sensing

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Abstract:Land use/land cover (LULC) change is one of the most important indicators in understanding the interactions between humans and the environment. Traditionally, when LULC maps are produced yearly, most existing remote-sensing methods have to collect ground reference data annually, as the classifiers have to be trained individually in each corresponding year. This study presented a novel strategy to map LULC classes without training samples or assigning parameters. First of all, several novel indices were carefully selected from the index pool, which were able to highlight certain LULC very well. Following this, a common unsupervised classifier was employed to extract the LULC from the associated index image without assigning thresholds. Finally, a supervised classification was implemented with samples automatically collected from the unsupervised classification outputs. Results illustrated that the proposed method could achieve satisfactory performance, reaching similar accuracies to traditional approaches. Findings of this study demonstrate that the proposed strategy is a simple and effective alternative to mapping urban LULC. With the proposed strategy, the budget and time required for remote-sensing data processing could be reduced dramatically.

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“…We have entered an era of remote sensing big data [1][2][3]. It is well known that a large amount of actionable information hides in remote sensing big data.…”

Section: Introductionmentioning

confidence: 99%

Multiple Feature Hashing Learning for Large-Scale Remote Sensing Image Retrieval

Tao

et al. 2017

IJGI

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Driven by the urgent demand of remote sensing big data management and knowledge discovery, large-scale remote sensing image retrieval (LSRSIR) has attracted more and more attention. As is well known, hashing learning has played an important role in coping with big data mining problems. In the literature, several hashing learning methods have been proposed to address LSRSIR. Until now, existing LSRSIR methods take only one type of feature descriptor as the input of hashing learning methods and ignore the complementary effects of multiple features, which may represent remote sensing images from different aspects. Different from the existing LSRSIR methods, this paper proposes a flexible multiple-feature hashing learning framework for LSRSIR, which takes multiple complementary features as the input and learns the hybrid feature mapping function, which projects multiple features of the remote sensing image to the low-dimensional binary (i.e., compact) feature representation. Furthermore, the compact feature representations can be directly utilized in LSRSIR with the aid of the hamming distance metric. In order to show the superiority of the proposed multiple feature hashing learning method, we compare the proposed approach with the existing methods on two publicly available large-scale remote sensing image datasets. Extensive experiments demonstrate that the proposed approach can significantly outperform the state-of-the-art approaches.

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Remote sensing big data computing: Challenges and opportunities

Abstract: h i g h l i g h t s• This paper identifies the properties and features of remote sensing big data.• This paper reviews the stat-of-the-arts of remote sensing big data computing.• This paper discusses the ''data-intensive computing'' issues in remote sensing big data processing.

Cited by 525 publications

References 69 publications

Topologically Consistent Models for Efficient Big Geo-Spatio-Temporal Data Distribution

Topologically Consistent Models for Efficient Big Geo-Spatio-Temporal Data Distribution

Mapping Typical Urban LULC from Landsat Imagery without Training Samples or Self-Defined Parameters

Multiple Feature Hashing Learning for Large-Scale Remote Sensing Image Retrieval

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