scholarly journals Earth Observation for Sustainable Infrastructure: A Review

2021 ◽  
Vol 13 (8) ◽  
pp. 1528
Author(s):  
Yongze Song ◽  
Peng Wu
Keyword(s):  
Sustainable Development ◽  
Large Scale ◽  
Earth Observation ◽  
Future Research ◽  
Observation Data ◽  
Infrastructure Development ◽  
Sustainable Infrastructure ◽  
Development Goals ◽  
Future Research Directions ◽  
Earth Observation Data

Infrastructure is a fundamental sector for sustainable development and Earth observation has great potentials for sustainable infrastructure development (SID). However, implementations of the timely, large–scale and multi–source Earth observation are still limited in satisfying the huge global requirements of SID. This study presents a systematical literature review to identify trends of Earth observation for sustainable infrastructure (EOSI), investigate the relationship between EOSI and Sustainable Development Goals (SDGs), and explore challenges and future directions of EOSI. Results reveal the close associations of infrastructure, urban development, ecosystems, climate, Earth observation and GIS in EOSI, and indicate their relationships. In addition, from the perspective of EOSI–SDGs relationship, the huge potentials of EOSI are demonstrated from the 70% of the infrastructure influenced targets that can be directly or indirectly derived from Earth observation data, but have not been included in current SDG indicators. Finally, typical EOSI cases are presented to indicate challenges and future research directions. This review emphasizes the contributions and potentials of Earth observation to SID and EOSI is a powerful pathway to deliver on SDGs.

scholarly journals Seeing Sustainability from Space: Using Earth Observation Data to Populate the UN Sustainable Development Goal Indicators

2019 ◽  
Vol 11 (18) ◽  
pp. 5062 ◽  
Author(s):  
Ana Andries ◽  
Stephen Morse ◽  
Richard Murphy ◽  
Jim Lynch ◽  
Emma Woolliams
Keyword(s):  
New York ◽  
Sustainable Development ◽  
Earth Observation ◽  
Development Economic ◽  
Special Focus ◽  
Observation Data ◽  
Structured Interviews ◽  
The Sustainable Development ◽  
Development Goals ◽  
Earth Observation Data

In 2015, member countries of the United Nations adopted the 17 Sustainable Development Goals (SDGs) at the Sustainable Development Summit in New York. These global goals have 169 targets and 232 indicators based on the three pillars of sustainable development: economic, social, and environmental. However, substantial challenges remain in obtaining data of the required quality and quantity to populate these indicators efficiently. One promising and innovative way of addressing this issue is to use Earth observation (EO). The research reported here updates our original work to develop a Maturity Matrix Framework (MMF) for assessing the suitability of EO-derived data for populating the SDG indicators, with a special focus on those indicators covering the more social and economic dimensions of sustainable development, as these have been under-explored in terms of the contribution that can be made by EO. The advanced MMF 2.0 framework set out in this paper is based on a wide consultation with EO and indicator experts (semi-structured interviews with 38 respondents). This paper provides detail of the evolved structure of MMF 2.0 and illustrates its use for one of the SDG indicators (Indicator 11.1.1). The revised MMF is then applied to published work covering the full suite of SDG indicators and demonstrates that EO can make an important contribution to providing data relevant to a substantial number of the SDG indicators.

scholarly journals Monitoring sustainable development by means of earth observation data and machine learning: a review

2020 ◽  
Vol 32 (1) ◽  
Author(s):  
Bruno Ferreira ◽  
Muriel Iten ◽  
Rui G. Silva
Keyword(s):  
Machine Learning ◽  
Sustainable Development ◽  
Sustainable Development Goals ◽  
Earth Observation ◽  
Machine Learning Techniques ◽  
Observation Data ◽  
The Sustainable Development ◽  
Learning Techniques ◽  
Development Goals ◽  
Earth Observation Data

Abstract This paper presents and explores the different Earth Observation approaches and their contribution to the achievement of United Nations Sustainable Development Goals. A review on the Sustainable Development concept and its goals is presented followed by Earth Observation approaches relevant to this field, giving special attention to the contribution of Machine Learning methods and algorithms as well as their potential and capabilities to support the achievement of Sustainable Development Goals. Overall, it is observed that Earth Observation plays a key role in monitoring the Sustainable Development Goals given its cost-effectiveness pertaining to data acquisition on all scales and information richness. Despite the success of Machine Learning upon Earth Observation data analysis, it is observed that performance is heavily dependent on the ability to extract and synthesise characteristics from data. Hence, a deeper and effective analysis of the available data is required to identify the strongest features and, hence, the key factors pertaining to Sustainable Development. Overall, this research provides a deeper understanding on the relation between Sustainable Development, Earth Observation and Machine Learning, and how these can support the Sustainable Development of countries and the means to find their correlations. In pursuing the Sustainable Development Goals, given the relevance and growing amount of data generated through Earth Observation, it is concluded that there is an increased need for new methods and techniques strongly suggesting the use of new Machine Learning techniques.

Analyzing large-scale Earth Observation data repositories made simple with OpenEO Platform

2021 ◽  
Author(s):  
Edzer Pebesma ◽  
Patrick Griffiths ◽  
Christian Briese ◽  
Alexander Jacob ◽  
Anze Skerlevaj ◽  
...  
Keyword(s):  
Large Scale ◽  
Virtual Machines ◽  
Google Earth ◽  
Earth Observation ◽  
Data Cube ◽  
Observation Data ◽  
Data Repositories ◽  
Software Ecosystem ◽  
Wide Range ◽  
Earth Observation Data

<p>The OpenEO API allows the analysis of large amounts of Earth Observation data using a high-level abstraction of data and processes. Rather than focusing on the management of virtual machines and millions of imagery files, it allows to create jobs that take a spatio-temporal section of an image collection (such as Sentinel L2A), and treat it as a data cube. Processes iterate or aggregate over pixels, spatial areas, spectral bands, or time series, while working at arbitrary spatial resolution. This pattern, pioneered by Google Earth Engine™ (GEE), lets the user focus on the science rather than on data management.</p><p>The openEO H2020 project (2017-2020) has developed the API as well as an ecosystem of software around it, including clients (JavaScript, Python, R, QGIS, browser-based), back-ends that translate API calls into existing image analysis or GIS software or services (for Sentinel Hub, WCPS, Open Data Cube, GRASS GIS, GeoTrellis/GeoPySpark, and GEE) as well as a hub that allows querying and searching openEO providers for their capabilities and datasets. The project demonstrated this software in a number of use cases, where identical processing instructions were sent to different implementations, allowing comparison of returned results.</p><p>A follow-up, ESA-funded project “openEO Platform” realizes the API and progresses the software ecosystem into operational services and applications that are accessible to everyone, that involve federated deployment (using the clouds managed by EODC, Terrascope, CreoDIAS and EuroDataCube), that will provide payment models (“pay per compute job”) conceived and implemented following the user community needs and that will use the EOSC (European Open Science Cloud) marketplace for dissemination and authentication. A wide range of large-scale cases studies will demonstrate the ability of the openEO Platform to scale to large data volumes.  The case studies to be addressed include on-demand ARD generation for SAR and multi-spectral data, agricultural demonstrators like crop type and condition monitoring, forestry services like near real time forest damage assessment as well as canopy cover mapping, environmental hazard monitoring of floods and air pollution as well as security applications in terms of vessel detection in the mediterranean sea.</p><p>While the landscape of cloud-based EO platforms and services has matured and diversified over the past decade, we believe there are strong advantages for scientists and government agencies to adopt the openEO approach. Beyond the absence of vendor/platform lock-in or EULA’s we mention the abilities to (i) run arbitrary user code (e.g. written in R or Python) close to the data, (ii) carry out scientific computations on an entirely open source software stack, (iii) integrate different platforms (e.g., different cloud providers offering different datasets), and (iv) help create and extend this software ecosystem. openEO uses the OpenAPI standard, aligns with modern OGC API standards, and uses the STAC (SpatioTemporal Asset Catalog) to describe image collections and image tiles.</p>

Achieving the Sustainable Development Goals: Evidence from the Longitudinal Parenting Across Cultures Project

2021 ◽  
pp. 89-112
Author(s):  
Jennifer E. Lansford ◽  
W. Andrew Rothenberg ◽  
Sombat Tapanya ◽  
Liliana Maria Uribe Tirado ◽  
Saengduean Yotanyamaneewong ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Longitudinal Data ◽  
Sustainable Development Goals ◽  
Well Being ◽  
Future Research ◽  
Cross Sectional ◽  
The Sustainable Development ◽  
Development Goals ◽  
Future Research Directions ◽  
Health And Well Being

This chapter uses evidence from the Parenting Across Cultures (PAC) project to illustrate ways in which longitudinal data can help achieve the Sustainable Development Goals (SDGs.) The chapter begins by providing an overview of the research questions that have guided the international PAC as well as a description of the participants, procedures, and measures. Next, empirical findings from PAC are summarized to illustrate implications for six specific SDGs related to child and adolescent development in relation to education, poverty, gender, mental health, and well-being. Then the chapter describes how longitudinal data offer advantages over cross-sectional data in operationalizing SDG targets and implementing the SDGs. Finally, limitations, future research directions, and conclusions are provided.

scholarly journals A Review of the Sustainability Concept and the State of SDG Monitoring Using Remote Sensing

2020 ◽  
Vol 12 (11) ◽  
pp. 1770 ◽  
Author(s):  
Ronald Estoque
Keyword(s):  
Remote Sensing ◽  
Sustainable Development ◽  
Sustainable Development Goals ◽  
Global Scale ◽  
Full Potential ◽  
Observation Data ◽  
The Sustainable Development ◽  
Sustainability Concept ◽  
Development Goals ◽  
Earth Observation Data

The formulation of the 17 sustainable development goals (SDGs) was a major leap forward in humankind’s quest for a sustainable future, which likely began in the 17th century, when declining forest resources in Europe led to proposals for the re-establishment and conservation of forests, a strategy that embodies the great idea that the current generation bears responsibility for future generations. Global progress toward SDG fulfillment is monitored by 231 unique social-ecological indicators spread across 169 targets, and remote sensing (RS) provides Earth observation data, directly or indirectly, for 30 (18%) of these indicators. Unfortunately, the UN Global Sustainable Development Report 2019—The Future is Now: Science for Achieving Sustainable Development concluded that, despite initial efforts, the world is not yet on track for achieving most of the SDG targets. Meanwhile, through the EO4SDG initiative by the Group on Earth Observations, the full potential of RS for SDG monitoring is now being explored at a global scale. As of April 2020, preliminary statistical data were available for 21 (70%) of the 30 RS-based SDG indicators, according to the Global SDG Indicators Database. Ten (33%) of the RS-based SDG indicators have also been included in the SDG Index and Dashboards found in the Sustainable Development Report 2019—Transformations to Achieve the Sustainable Development Goals. These statistics, however, do not necessarily reflect the actual status and availability of raw and processed geospatial data for the RS-based indicators, which remains an important issue. Nevertheless, various initiatives have been started to address the need for open access data. RS data can also help in the development of other potentially relevant complementary indicators or sub-indicators. By doing so, they can help meet one of the current challenges of SDG monitoring, which is how best to operationalize the SDG indicators.

scholarly journals Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review-Part I: Evolution and Recent Trends

2020 ◽  
Vol 12 (10) ◽  
pp. 1667 ◽  
Author(s):  
Thorsten Hoeser ◽  
Claudia Kuenzer
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Object Detection ◽  
Great Influence ◽  
Adaptive Method ◽  
Earth Observation ◽  
Future Research ◽  
Observation Data ◽  
Theoretical Concepts ◽  
Earth Observation Data

Deep learning (DL) has great influence on large parts of science and increasingly established itself as an adaptive method for new challenges in the field of Earth observation (EO). Nevertheless, the entry barriers for EO researchers are high due to the dense and rapidly developing field mainly driven by advances in computer vision (CV). To lower the barriers for researchers in EO, this review gives an overview of the evolution of DL with a focus on image segmentation and object detection in convolutional neural networks (CNN). The survey starts in 2012, when a CNN set new standards in image recognition, and lasts until late 2019. Thereby, we highlight the connections between the most important CNN architectures and cornerstones coming from CV in order to alleviate the evaluation of modern DL models. Furthermore, we briefly outline the evolution of the most popular DL frameworks and provide a summary of datasets in EO. By discussing well performing DL architectures on these datasets as well as reflecting on advances made in CV and their impact on future research in EO, we narrow the gap between the reviewed, theoretical concepts from CV and practical application in EO.

scholarly journals A review of approaches to estimate wildfire plume injection height within large-scale atmospheric chemical transport models

2016 ◽  
Vol 16 (2) ◽  
pp. 907-925 ◽  
Author(s):  
R. Paugam ◽  
M. Wooster ◽  
S. Freitas ◽  
M. Val Martin
Keyword(s):  
Large Scale ◽  
Atmospheric Transport ◽  
Chemical Constituents ◽  
Source Region ◽  
Ambient Air ◽  
Earth Observation ◽  
Observation Data ◽  
Transport Models ◽  
Fire Emissions ◽  
Earth Observation Data

Abstract. Landscape fires produce smoke containing a very wide variety of chemical species, both gases and aerosols. For larger, more intense fires that produce the greatest amounts of emissions per unit time, the smoke tends initially to be transported vertically or semi-vertically close by the source region, driven by the intense heat and convective energy released by the burning vegetation. The column of hot smoke rapidly entrains cooler ambient air, forming a rising plume within which the fire emissions are transported. The characteristics of this plume, and in particular the height to which it rises before releasing the majority of the smoke burden into the wider atmosphere, are important in terms of how the fire emissions are ultimately transported, since for example winds at different altitudes may be quite different. This difference in atmospheric transport then may also affect the longevity, chemical conversion, and fate of the plumes chemical constituents, with for example very high plume injection heights being associated with extreme long-range atmospheric transport. Here we review how such landscape-scale fire smoke plume injection heights are represented in larger-scale atmospheric transport models aiming to represent the impacts of wildfire emissions on component of the Earth system. In particular we detail (i) satellite Earth observation data sets capable of being used to remotely assess wildfire plume height distributions and (ii) the driving characteristics of the causal fires. We also discuss both the physical mechanisms and dynamics taking place in fire plumes and investigate the efficiency and limitations of currently available injection height parameterizations. Finally, we conclude by suggesting some future parameterization developments and ideas on Earth observation data selection that may be relevant to the instigation of enhanced methodologies aimed at injection height representation.

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