Big Earth data: disruptive changes in Earth observation data management and analysis?
Ontology highlight
ABSTRACT:
SUBMITTER: Sudmanns M
PROVIDER: S-EPMC7455055 | biostudies-literature | 2019 Mar
REPOSITORIES: biostudies-literature
Publications
Big Earth data: disruptive changes in Earth observation data management and analysis?
Sudmanns Martin M Tiede Dirk D Lang Stefan S Bergstedt Helena H Trost Georg G Augustin Hannah H Baraldi Andrea A Blaschke Thomas T
International journal of digital earth 20190314 7
Turning Earth observation (EO) data consistently and systematically into valuable global information layers is an ongoing challenge for the EO community. Recently, the term 'big Earth data' emerged to describe massive EO datasets that confronts analysts and their traditional workflows with a range of challenges. We argue that the altered circumstances must be actively intercepted by an evolution of EO to revolutionise their application in various domains. The disruptive element is that analysts ...[more]
Similar Datasets
Project description:Environmental changes such as extreme weather events become increasingly noticeable worldwide. Earth observation (EO) data provide information about such changes, but little is known about citizens' perceptions of and responses to such changes. Across three studies, we assess whether people's place attachment on different regional levels predicts interest in EO data, and whether perceived environmental change affects emotional responses and place attachment. Two survey studies (N = 118 students and N = 197 citizens from the Palatinate in Southern Germany) revealed that place attachment predicts interest in EO data, especially when people felt strongly attached to the region or place in question. A third experimental study (N = 600) with repeated measures (N = 203) revealed that visualized environmental change - using satellite images of local vs. non-local environmental change - did not affect place attachment but elicited stronger emotional responses than visualizing no change. Policy support measures across Studies 2 and 3 suggest that place attachment and emotional responses are important predictors for action to mitigate consequences of environmental change.
| S-EPMC7366120 | biostudies-literature
Project description:Earth observation (EO) is the use of remote sensing and in situ observations to gather data on the environment. It finds increasing application in the study of environmentally modulated neglected tropical diseases (NTDs). Obtaining and assuring the quality of the relevant spatially and temporally indexed EO data remain challenges. Our objective was to review the Earth observation products currently used in studies of NTD epidemiology and to discuss fundamental issues relating to spatial data quality (SDQ), which limit the utilization of EO and pose challenges for its more effective use. We searched Web of Science and PubMed for studies related to EO and echinococossis, leptospirosis, schistosomiasis, and soil-transmitted helminth infections. Relevant literature was also identified from the bibliographies of those papers. We found that extensive use is made of EO products in the study of NTD epidemiology; however, the quality of these products is usually given little explicit attention. We review key issues in SDQ concerning spatial and temporal scale, uncertainty, and the documentation and use of quality information. We give examples of how these issues may interact with uncertainty in NTD data to affect the output of an epidemiological analysis. We conclude that researchers should give careful attention to SDQ when designing NTD spatial-epidemiological studies. This should be used to inform uncertainty analysis in the epidemiological study. SDQ should be documented and made available to other researchers.
| S-EPMC4683053 | biostudies-literature
Project description:Spatiotemporal analytics of multi-source Earth observation (EO) big data is a pre-condition for semantic content-based image retrieval (SCBIR). As a proof of concept, an innovative EO semantic querying (EO-SQ) subsystem was designed and prototypically implemented in series with an EO image understanding (EO-IU) subsystem. The EO-IU subsystem is automatically generating ESA Level 2 products (scene classification map, up to basic land cover units) from optical satellite data. The EO-SQ subsystem comprises a graphical user interface (GUI) and an array database embedded in a client server model. In the array database, all EO images are stored as a space-time data cube together with their Level 2 products generated by the EO-IU subsystem. The GUI allows users to (a) develop a conceptual world model based on a graphically supported query pipeline as a combination of spatial and temporal operators and/or standard algorithms and (b) create, save and share within the client-server architecture complex semantic queries/decision rules, suitable for SCBIR and/or spatiotemporal EO image analytics, consistent with the conceptual world model.
| S-EPMC5632919 | biostudies-other
Project description:Pandemic outbreaks can cause diverse impacts on society by altering human-nature relations. This study analyzed these relational changes during the severe acute respiratory syndrome (SARS), Swine flu, Middle East respiratory syndrome (MERS), and Ebola outbreaks by applying machine learning and big data analyses of global news articles. The results showed that social-ecological systems play vital roles in analyzing indirect pandemic impacts. Herein, major pandemic impacts, including reduced use of cultural ecosystem services, can be analyzed by big data analyses at the global scale. All the identified pandemic impacts herein were linked to provisioning and cultural ecosystem services, implying that these ecosystem services might be more recognized or valued more by the public than regulating and supporting ecosystem services. Further, the pandemic impacts were presented with human-centric views, indicating a challenge to adapting nature-based solutions to mitigate the risk of future pandemic emergences. These findings will advance the current knowledge of diverse pandemic impacts and human-nature relations.
| S-EPMC8609451 | biostudies-literature
Project description:PurposeLittle is known about the current practice of airway management in Germany and its development over the last decades. The present study was, therefore, designed to answer the following questions. Which airway management procedures have been performed over the last 16 years and how has the frequency of these procedures changed over time? Is there a relationship between patient characteristics or surgical specialisation and the type of airway management performed?MethodsIn the present study, we used our in-house data acquisition and accounting system to retrospectively analyse airway management data for all patients who underwent a surgical or medical procedure with anaesthesiological care at our tertiary care facility over the past 16 years. 340,748 airway management procedures were analysed by type of procedure, medical/surgical specialty, and type of device used. Logistic regression was used to identify trends over time.ResultsOral intubation was the most common technique over 16 years (65.7%), followed by supraglottic airway devices (18.1%), nasal intubation (7.5%), mask ventilation (1.6%), tracheal cannula (1.3%), double lumen tube (0.7%), and jet ventilation (0.6%). On average, the odds ratio of using supraglottic airway devices increased by 17.0% per year (OR per year = 1.072, 95% CI = 1.071-1.088) while oral intubation rates decreased. In 2005, supraglottic airway devices were used in about 10% of all airway management procedures. Until 2020, this proportion steadily increased by 27%. Frequency of oral intubation on the other hand decreased and was about 75% in 2005 and 53% in 2020. Over time, second-generation supraglottic airway devices were used more frequently than first-generation supraglottic airway devices. While second-generation devices made up about 9% of all supraglottic airway devices in 2010, in 2020 they represented a proportion of 82%. The use of fibreoptic intubation increased over time in otorhinolaryngology and dental, oral, and maxillofacial surgery, but showed no significant trends over the entire 16-year period.ConclusionOur data represent the first large-scale evaluation of airway management procedures over a long time. There was a significant upward trend in the use of supraglottic airway devices, with an increase in the use of second-generation masks while a decrease in oral intubations was observed.
| S-EPMC9488754 | biostudies-literature
Project description:The advent of big data technologies makes a profound impact on various facets of our lives, which also presents an opportunity for Chinese audits. However, the heterogeneity of multi-source audit data, the intricacy of converting Chinese into SQL, and the inefficiency of data processing methods present significant obstacles to the growth of Chinese audits. In this article, we proposed BDMCA, a big data management system designed for Chinese audits. We developed a hybrid management architecture for handling Chinese audit big data, that can alleviate the heterogeneity of multi-mode data. Moreover, we defined an R-HBase spatio-temporal meta-structure for auditing purposes, which exhibits almost linear response time and excellent scalability. Compared to MD-HBase, R-HBase performs 4.5× and 3× better in range query and kNN query, respectively. In addition, we leveraged the slot value filling method to generate templates and build a multi-topic presentation learning model MRo-SQL. MRo-SQL outperforms the state-of-the-art X-SQL parsing model with improvements in logical-form accuracy of up to 5.2%, and execution accuracy of up to 5.9%.
| S-EPMC10280443 | biostudies-literature
Project description:The current exponential increase of spatiotemporally explicit data streams from satellitebased Earth observation missions offers promising opportunities for global vegetation monitoring. Intelligent sampling through active learning (AL) heuristics provides a pathway for fast inference of essential vegetation variables by means of hybrid retrieval approaches, i.e., machine learning regression algorithms trained by radiative transfer model (RTM) simulations. In this study we summarize AL theory and perform a brief systematic literature survey about AL heuristics used in the context of Earth observation regression problems over terrestrial targets. Across all relevant studies it appeared that: (i) retrieval accuracy of AL-optimized training data sets outperformed models trained over large randomly sampled data sets, and (ii) Euclidean distance-based (EBD) diversity method tends to be the most efficient AL technique in terms of accuracy and computational demand. Additionally, a case study is presented based on experimental data employing both uncertainty and diversity AL criteria. Hereby, a a simulated training data base by the PROSAIL-PRO canopy RTM is used to demonstrate the benefit of AL techniques for the estimation of total leaf carotenoid content (Cxc) and leaf water content (Cw). Gaussian process regression (GPR) was incorporated to minimize and optimize the training data set with AL. Training the GPR algorithm on optimally AL-based sampled data sets led to improved variable retrievals compared to training on full data pools, which is further demonstrated on a mapping example. From these findings we can recommend the use of AL-based sub-sampling procedures to select the most informative samples out of large training data pools. This will not only optimize regression accuracy due to exclusion of redundant information, but also speed up processing time and reduce final model size of kernel-based machine learning regression algorithms, such as GPR. With this study we want to encourage further testing and implementation of AL sampling methods for hybrid retrieval workflows. AL can contribute to the solution of regression problems within the framework of operational vegetation monitoring using satellite imaging spectroscopy data, and may strongly facilitate data processing for cloud-computing platforms.
| S-EPMC7613397 | biostudies-literature
Project description:In an era of big data, the availability of satellite-derived global climate, terrain, and land cover imagery presents an opportunity for modeling the suitability of malaria disease vectors at fine spatial resolutions, across temporal scales, and over vast geographic extents. Leveraging cloud-based geospatial analytical tools, we present an environmental suitability model that considers water resources, flow accumulation areas, precipitation, temperature, vegetation, and land cover. In contrast to predictive models generated using spatially and temporally discontinuous mosquito presence information, this model provides continuous fine-spatial resolution information on the biophysical drivers of suitability. For the purposes of this study the model is parameterized for Anopheles gambiae s.s. in Malawi for the rainy (December-March) and dry seasons (April-November) in 2017; however, the model may be repurposed to accommodate different mosquito species, temporal periods, or geographical boundaries. Final products elucidate the drivers and potential habitat of Anopheles gambiae s.s. Rainy season results are presented by quartile of precipitation; Quartile four (Q4) identifies areas most likely to become inundated and shows 7.25% of Malawi exhibits suitable water conditions (water only) for Anopheles gambiae s.s., approximately 16% for water plus another factor, and 8.60% is maximally suitable, meeting suitability thresholds for water presence, terrain characteristics, and climatic conditions. Nearly 21% of Malawi is suitable for breeding based on land characteristics alone and 28.24% is suitable according to climate and land characteristics. Only 6.14% of the total land area is suboptimal. Dry season results show 25.07% of the total land area is suboptimal or unsuitable. Approximately 42% of Malawi is suitable based on land characteristics alone during the dry season, and 13.11% is suitable based on land plus another factor. Less than 2% meets suitability criteria for climate, water, and land criteria. Findings illustrate environmental drivers of suitability for malaria vectors, providing an opportunity for a more comprehensive approach to malaria control that includes not only modeled species distributions, but also the underlying drivers of suitability for a more effective approach to environmental management.
| S-EPMC7402481 | biostudies-literature
Project description:The increasing complexity of data and analysis methods has created an environment where scientists, who may not have formal training, are finding themselves playing the impromptu role of software engineer. While several resources are available for introducing scientists to the basics of programming, researchers have been left with little guidance on approaches needed to advance to the next level for the development of robust, large-scale data analysis tools that are amenable to integration into workflow management systems, tools, and frameworks. The integration into such workflow systems necessitates additional requirements on computational tools, such as adherence to standard conventions for robustness, data input, output, logging, and flow control. Here we provide a set of 10 guidelines to steer the creation of command-line computational tools that are usable, reliable, extensible, and in line with standards of modern coding practices.
| S-EPMC6532757 | biostudies-literature
Project description:In the age of ever increasing data, faster and more efficient data processing algorithms are needed. Graphics Processing Units (GPU) are emerging as a cost-effective alternative architecture for high-end computing. The optimal design of GPU algorithms is a challenging task which requires thorough understanding of the high performance computing architecture as well as the algorithmic design. The steep learning curve needed for effective GPU-centric algorithm design and implementation requires considerable expertise, time, and resources. In this paper, we present GPU-DAEMON, a GPU Data Management, Algorithm Design and Optimization technique suitable for processing array based big omics data. Our proposed GPU algorithm design template outlines and provides generic methods to tackle critical bottlenecks which can be followed to implement high performance, scalable GPU algorithms for given big data problem. We study the capability of GPU-DAEMON by reviewing the implementation of GPU-DAEMON based algorithms for three different big data problems. Speed up of as large as 386x (over the sequential version) and 50x (over naive GPU design methods) are observed using the proposed GPU-DAEMON. GPU-DAEMON template is available at https://github.com/pcdslab/GPU-DAEMON and the source codes for GPU-ArraySort, G-MSR and GPU-PCC are available at https://github.com/pcdslab.
| S-EPMC6400487 | biostudies-literature