scholarly journals Remote Sensing

2021 ◽  
pp. 49-61
Author(s):  
Miguel Ángel Esbrí
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
Information Access ◽  
Open Data ◽  
Earth Observation ◽  
Observation Data ◽  
Related Data ◽  
Data Formats ◽  
Earth Observation Data ◽  
Access Services

AbstractIn this chapter we present the concepts of remote sensing and Earth Observation and, explain why several of their characteristics (volume, variety and velocity) make us consider Earth Observation as Big Data. Thereafter, we discuss the most commonly open data formats used to store and share the data. The main sources of Earth Observation data are also described, with particular focus on the constellation of Sentinel satellites, Copernicus Hub and its six thematic services, as well as other private initiatives like the five Copernicus-related Data and Information Access Services and  Sentinel Hub. Next, we present an overview of representative software technologies for efficiently describing, storing, querying and accessing Earth Observation datasets. The chapter concludes with a summary of the Earth Observation datasets used in each DataBio pilot.

scholarly journals Data Mining and Knowledge Discovery tools for exploiting big Earth-Observation data

2015 ◽  
Vol XL-7/W3 ◽  
pp. 627-633 ◽  
Author(s):  
D. Espinoza Molina ◽  
M Datcu
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Earth Observation ◽  
Database Management System ◽  
Observation Data ◽  
End User ◽  
Image Content ◽  
Continuous Increase ◽  
Related Data ◽  
Earth Observation Data

The continuous increase in the size of the archives and in the variety and complexity of Earth-Observation (EO) sensors require new methodologies and tools that allow the end-user to access a large image repository, to extract and to infer knowledge about the patterns hidden in the images, to retrieve dynamically a collection of relevant images, and to support the creation of emerging applications (e.g.: change detection, global monitoring, disaster and risk management, image time series, etc.). In this context, we are concerned with providing a platform for data mining and knowledge discovery content from EO archives. The platform’s goal is to implement a communication channel between Payload Ground Segments and the end-user who receives the content of the data coded in an understandable format associated with semantics that is ready for immediate exploitation. It will provide the user with automated tools to explore and understand the content of highly complex images archives. The challenge lies in the extraction of meaningful information and understanding observations of large extended areas, over long periods of time, with a broad variety of EO imaging sensors in synergy with other related measurements and data. The platform is composed of several components such as 1.) ingestion of EO images and related data providing basic features for image analysis, 2.) query engine based on metadata, semantics and image content, 3.) data mining and knowledge discovery tools for supporting the interpretation and understanding of image content, 4.) semantic definition of the image content via machine learning methods. All these components are integrated and supported by a relational database management system, ensuring the integrity and consistency of Terabytes of Earth Observation data.

scholarly journals Editorial of Special Issue “Machine and Deep Learning for Earth Observation Data Analysis”

2021 ◽  
Vol 13 (14) ◽  
pp. 2758
Author(s):  
Vasileios Syrris ◽  
Sveinung Loekken
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Data Analysis ◽  
Earth Observation ◽  
Observation Data ◽  
Earth System ◽  
Special Issue ◽  
The Earth ◽  
Comprehensive Information ◽  
Earth Observation Data

Earth observation and remote sensing technologies provide ample and comprehensive information regarding the dynamics and complexity of the Earth system [...]

scholarly journals Big Earth Observation Data Integration in Remote Sensing Based on a Distributed Spatial Framework

2020 ◽  
Vol 12 (6) ◽  
pp. 972 ◽  
Author(s):  
Yinyi Cheng ◽  
Kefa Zhou ◽  
Jinlin Wang ◽  
Jining Yan
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
Data Integration ◽  
Distributed Storage ◽  
Remote Sensing Data ◽  
Earth Observation ◽  
Observation Data ◽  
Integration Framework ◽  
Sensing Data ◽  
Storage Method

The arrival of the era of big data for Earth observation (EO) indicates that traditional data management models have been unable to meet the needs of remote sensing data in big data environments. With the launch of the first remote sensing satellite, the volume of remote sensing data has also been increasing, and traditional data storage methods have been unable to ensure the efficient management of large amounts of remote sensing data. Therefore, a professional remote sensing big data integration method is sorely needed. In recent years, the emergence of some new technical methods has provided effective solutions for multi-source remote sensing data integration. This paper proposes a multi-source remote sensing data integration framework based on a distributed management model. In this framework, the multi-source remote sensing data are partitioned by the proposed spatial segmentation indexing (SSI) model through spatial grid segmentation. The designed complete information description system, based on International Organization for Standardization (ISO) 19115, can explain multi-source remote sensing data in detail. Then, the distributed storage method of data based on MongoDB is used to store multi-source remote sensing data. The distributed storage method is physically based on the sharding mechanism of the MongoDB database, and it can provide advantages for the security and performance of the preservation of remote sensing data. Finally, several experiments have been designed to test the performance of this framework in integrating multi-source remote sensing data. The results show that the storage and retrieval performance of the distributed remote sensing data integration framework proposed in this paper is superior. At the same time, the grid level of the SSI model proposed in this paper also has an important impact on the storage efficiency of remote sensing data. Therefore, the remote storage data integration framework, based on distributed storage, can provide new technical support and development prospects for big EO data.

scholarly journals Earth Observation Data Supporting Non-Communicable Disease Research: A Review

2020 ◽  
Vol 12 (16) ◽  
pp. 2541
Author(s):  
Patrick Sogno ◽  
Claudia Traidl-Hoffmann ◽  
Claudia Kuenzer
Keyword(s):  
Public Health ◽  
Remote Sensing ◽  
Environmental Parameters ◽  
Communicable Diseases ◽  
Health Data ◽  
Earth Observation ◽  
Observation Data ◽  
Non Communicable Diseases ◽  
Public Health Data ◽  
Earth Observation Data

A disease is non-communicable when it is not transferred from one person to another. Typical examples include all types of cancer, diabetes, stroke, or allergies, as well as mental diseases. Non-communicable diseases have at least two things in common—environmental impact and chronicity. These diseases are often associated with reduced quality of life, a higher rate of premature deaths, and negative impacts on a countries’ economy due to healthcare costs and missing work force. Additionally, they affect the individual’s immune system, which increases susceptibility toward communicable diseases, such as the flu or other viral and bacterial infections. Thus, mitigating the effects of non-communicable diseases is one of the most pressing issues of modern medicine, healthcare, and governments in general. Apart from the predisposition toward such diseases (the genome), their occurrence is associated with environmental parameters that people are exposed to (the exposome). Exposure to stressors such as bad air or water quality, noise, extreme heat, or an overall unnatural surrounding all impact the susceptibility to non-communicable diseases. In the identification of such environmental parameters, geoinformation products derived from Earth Observation data acquired by satellites play an increasingly important role. In this paper, we present a review on the joint use of Earth Observation data and public health data for research on non-communicable diseases. We analyzed 146 articles from peer-reviewed journals (Impact Factor ≥ 2) from all over the world that included Earth Observation data and public health data for their assessments. Our results show that this field of synergistic geohealth analyses is still relatively young, with most studies published within the last five years and within national boundaries. While the contribution of Earth Observation, and especially remote sensing-derived geoinformation products on land surface dynamics is on the rise, there is still a huge potential for transdisciplinary integration into studies. We see the necessity for future research and advocate for the increased incorporation of thematically profound remote sensing products with high spatial and temporal resolution into the mapping of exposomes and thus the vulnerability and resilience assessment of a population regarding non-communicable diseases.

scholarly journals Bringing Earth Observation to Schools with Digital Integrated Learning Environments

2020 ◽  
Vol 12 (3) ◽  
pp. 345 ◽  
Author(s):  
Henryk Hodam ◽  
Andreas Rienow ◽  
Carsten Jürgens
Keyword(s):  
Remote Sensing ◽  
Learning Environments ◽  
School Environment ◽  
Earth Observation ◽  
Observation Data ◽  
Integrated Learning ◽  
Online Questionnaire ◽  
Teaching Resources ◽  
E Learning ◽  
Earth Observation Data

The digital integrated learning environments (ILEs) for earth observation described in this article are bringing the complex topic of earth observation into classrooms. They are intended to give pupils with no prior experience in remote sensing the opportunity to solve tasks with earth observation data by using the same means that professionals have at hand. These learning environments integrate remote sensing tools and background knowledge in a comprehensive e-learning environment. They are tailored for use in schools, whereby the curriculum typically does not include earth observation, teachers are generally not familiar with its concepts, and the technical infrastructure is still not quite ready for digital teaching resources. To make the learning environments applicable, the special demands and obstacles presented by a school environment have to be considered. These obstacles are used to derive the requirements for the use of satellite data in school classes and create classroom resources in terms of technology, didactics, and e-learning. The concept itself was developed ten years ago, and since, then multiple applications have been created and used in classes. Data from an online questionnaire focuses on the specific qualities of the learning modules, enabling us to assess whether the concept works, and where there is need for improvement. The results show that the learning environments are being used, and that they continue to open the minds of pupils and teachers alike to a new perspective on the earth.

scholarly journals Misperceptions of Predominant Slum Locations? Spatial Analysis of Slum Locations in Terms of Topography Based on Earth Observation Data

2020 ◽  
Vol 12 (15) ◽  
pp. 2474 ◽  
Author(s):  
Inken Müller ◽  
Hannes Taubenböck ◽  
Monika Kuffer ◽  
Michael Wurm
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Earth Observation ◽  
Observation Data ◽  
Population Densities ◽  
Poverty And Inequality ◽  
Test Of Homogeneity ◽  
Chi Squared ◽  
Earth Observation Data ◽  
Do So

Slums are a physical expression of poverty and inequality in cities. According to the UN definition, this inequality is, e.g., reflected in the fact that slums are much more often located in hazardous zones. However, this has not yet been empirically investigated. In this study, we derive proxies from multi-sensoral high resolution remote sensing data to investigate both the location of slums and the location of slopes. We do so for seven cities on three continents. Using a chi-squared test of homogeneity, we compare the locations of formal areas with that of slums. Contrary to the perception indirectly stated in the literature, we find that slums are in none of the sample cities predominantly located in these exposed areas. In five out of seven cities, the spatial share of slums on hills steeper than 10° is even less than 5% of all slums. However, we also find a higher likelihood of slums occurring in these exposed areas than of formal settlements. In six out of seven sample cities, the probability that a slum is located in steep areas is higher than for a formal settlement. As slums mostly feature higher population densities, these findings reveal a clear tendency that slum residents are more likely to settle in exposed areas.

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