Big Data for Remote Sensing: Challenges and Opportunities

Chi, Mingmin; Plaza, Antonio; Benediktsson, Jón Atli; Sun, Zhongyi; Shen, Jinsheng; Zhu, Yangyong

doi:10.1109/jproc.2016.2598228

Cited by 405 publications

(208 citation statements)

References 49 publications

(12 reference statements)

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“…), which in a remote sensing context refers to the recent increase in the volume and variety of remote sensing data available, as well as the increase in processing velocity (Chi et al. ). These developments in online platforms and virtual machines should help those working in conservation to capitalize on the potential of big data to monitor large areas.…”

Section: Resultsmentioning

confidence: 99%

“…However, this has changed thanks to the availability of services such as the cloud-based platform Google Earth Engine (Gorelick et al 2017) and the free virtual machines provided by the European Space Agency's Research and User Support Service (RUS 2018). There is currently a growing interest in the potential of big data (Liu et al 2018), which in a remote sensing context refers to the recent increase in the volume and variety of remote sensing data available, as well as the increase in processing velocity (Chi et al 2016). These developments in online platforms and virtual machines should help those working in conservation to capitalize on the potential of big data to monitor large areas.…”

Section: Resultsmentioning

confidence: 99%

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A comparison of satellite remote sensing data fusion methods to map peat swamp forest loss in Sumatra, Indonesia

Crowson

Warren‐Thomas

Hill

et al. 2018

Remote Sens Ecol Conserv

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The loss of huge areas of peat swamp forest in Southeast Asia and the resulting negative environmental effects, both local and global, have led to an increasing interest in peat restoration in the region. Satellite remote sensing offers the potential to provide up‐to‐date information on peat swamp forest loss across large areas, and support spatial explicit conservation and restoration planning. Fusion of optical and radar remote sensing data may be particularly valuable in this context, as most peat swamp forests are in areas with high cloud cover, which limits the use of optical data. Radar data can ‘see through’ cloud, but experience so far has shown that it doesn't discriminate well between certain types of land cover. Various approaches to fusion exist, but there is little information on how they compare. To assess this untapped potential, we compare three different classification methods with Sentinel‐1 and Sentinel‐2 images to map the remnant distribution of peat swamp forest in the area surrounding Sungai Buluh Protection Forest, Sumatra, Indonesia. Results show that data fusion increases overall accuracy in one of the three methods, compared to the use of optical data only. When data fusion was used with the pixel‐based classification using the original pixel values, overall accuracy increased by a small, but statistically significant amount. Data fusion was not beneficial in the case of object‐based classification or pixel‐based classification using principal components. This indicates optical data are still the main source of information for land cover mapping in the region. Based on our findings, we provide methodological recommendations to help those involved in peatland restoration capitalize on the potential of big data.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

A comparison of satellite remote sensing data fusion methods to map peat swamp forest loss in Sumatra, Indonesia

Crowson

Warren‐Thomas

Hill

et al. 2018

Remote Sens Ecol Conserv

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“…The true value of big earth observation data can be realized in a real remote sensing application (Chi et al, 2016). Global mapping and monitoring of human settlements are an illustration of the challenges in managing, processing, and efficient exploitation of big earth observation data.…”

Section: Conclusion and Outlooksmentioning

confidence: 99%

Big earth data analytics on Sentinel-1 and Landsat imagery in support to global human settlements mapping

Corbane

Pesaresi

Politis³

et al. 2017

Big Earth Data

108

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Continuous global-scale mapping of human settlements in the service of international agreements calls for massive volume of multi-source, multi-temporal, and multi-scale earth observation data. In this paper, the latest developments in terms of processing big earth observation data for the purpose of improving the Global Human Settlement Layer (GHSL) data are presented. Two experiments with Sentinel-1 and Landsat data collections were run leveraging on the Joint Research Centre Earth Observation Data and Processing Platform. A comparative analysis of the results of built-up areas extraction from different remote sensing data and processing workflows shows how the information production supported by data-intensive computing infrastructure for optimization and multiple testing can improve the output information reliability and consistency within the GHSL scope. The paper presents the processing workflows and the results of the two main experiments, giving insights into the enhanced mapping capabilities gained by analyzing Sentinel-1 and Landsat data-sets, and the lessons learnt in terms of handling and processing big earth observation data.

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“…Although a number of recent surveys and position papers have been published on the prospects and applications of Big Data and ML for environmental and earth sciences, they tend to either focus on (a) specific topical areas within EWM such as remote sensing [14][15][16][17][18], hydrology [13], groundwater [19], ecology [20], smart city [21,22], renewable energy [23], hydroinformatics [24], and disaster response and resource management [25][26][27]; or (b) computing technologies [28][29][30]. Given the rapidly evolving technological landscape, an evidence-based review is deemed necessary to provide an update-to-date synthesis of the technologies and their challenges.…”

Section: Introductionmentioning

confidence: 99%

How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions

Sun

Scanlon

2019

Environ. Res. Lett.

308

129

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Big Data and machine learning (ML) technologies have the potential to impact many facets of environment and water management (EWM). Big Data are information assets characterized by high volume, velocity, variety, and veracity. Fast advances in high-resolution remote sensing techniques, smart information and communication technologies, and social media have contributed to the proliferation of Big Data in many EWM fields, such as weather forecasting, disaster management, smart water and energy management systems, and remote sensing. Big Data brings about new opportunities for data-driven discovery in EWM, but it also requires new forms of information processing, storage, retrieval, as well as analytics. ML, a subdomain of artificial intelligence (AI), refers broadly to computer algorithms that can automatically learn from data. ML may help unlock the power of Big Data if properly integrated with data analytics. Recent breakthroughs in AI and computing infrastructure have led to the fast development of powerful deep learning (DL) algorithms that can extract hierarchical features from data, with better predictive performance and less human intervention. Collectively Big Data and ML techniques have shown great potential for data-driven decision making, scientific discovery, and process optimization. These technological advances may greatly benefit EWM, especially because (1) many EWM applications (e.g. early flood warning) require the capability to extract useful information from a large amount of data in autonomous manner and in real time, (2) EWM researches have become highly multidisciplinary, and handling the ever increasing data volume/types using the traditional workflow is simply not an option, and last but not least, (3) the current theoretical knowledge about many EWM processes is still incomplete, but which may now be complemented through data-driven discovery. A large number of applications on Big Data and ML have already appeared in the EWM literature in recent years. The purposes of this survey are to (1) examine the potential and benefits of data-driven research in EWM, (2) give a synopsis of key concepts and approaches in Big Data and ML, (3) provide a systematic review of current applications, and finally (4) discuss major issues and challenges, and recommend future research directions. EWM includes a broad range of research topics. Instead of attempting to survey each individual area, this review focuses on areas of nexus in EWM, with an emphasis on elucidating the potential benefits of increased data availability and predictive analytics to improving the EWM research.

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Big Data for Remote Sensing: Challenges and Opportunities

Cited by 405 publications

References 49 publications

A comparison of satellite remote sensing data fusion methods to map peat swamp forest loss in Sumatra, Indonesia

A comparison of satellite remote sensing data fusion methods to map peat swamp forest loss in Sumatra, Indonesia

Big earth data analytics on Sentinel-1 and Landsat imagery in support to global human settlements mapping

How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions

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