Remote Sensing 专刊“谷歌地球引擎：基于云的地球观测数据和分析平台"
Remote Sensing- Special Issue
Special Issue "Google Earth Engine: Cloud-Based Platform for Earth Observation Data and Analysis"
Deadline
A special issue of Remote Sensing (ISSN 2072-4292).
Deadline for manuscript submissions: 1 August 2021.
Special Issue Editors
Dr. Koreen Millard Website SciProfiles
Guest Editor
Department of Geography and Environmental Studies, Carleton University, 1125 Colonel By Drive, Ottawa, Ontario K1S 5B6, Canada Interests: SAR; LiDAR; wetlands; machine learning; random forest
Mr. Alexandre R. Bevington Website SciProfiles
Guest Editor
Research Hydrologist, British Columbia Ministry of Forests, Lands, Natural Resource Operations & Rural Development, 499 George St., Prince George, British Columbia V2M 2H3, Canada Interests: remote sensing; cryosphere; hydrology; environment; mountains; cold regions; change detection; data science
Special Issue Information
Dear Colleagues,
The ever-increasing global archive of earth observation data enables environmental and societal problems to be assessed and issues to be monitored globally and over long time-scales. Remote sensing software and applications must be able to perform large-area and time-series analysis in a timely manner and at meaningful scales. However, at these spatial and temporal extents, traditional remote sensing workflows that include downloading, processing, and analyzing data locally become challenging for individual scientists and institutions, requiring terabytes to petabytes of storage space and expertise in server or cloud-based storage and processing systems.
Google Earth Engine (GEE) enables free programmatic access to the MODIS, Landsat 1-5,7, and 8, and Sentinel-1, 2, 3, and 5 archives, with continual updates, as well as many other imagery and ancillary datasets (e.g., land-use data, climate and soil data), through either a Javascript or Python API. As only a browser and internet access is required, these platforms enable access to earth observation data by a new generation of analysts, without the requirement of expensive infrastructure and software. Google provides free training and example codes online to easily enable access to the basic data and algorithms exposed through GEE, and the GEE user community has posted thousands of code and workflow examples online, allowing users to adopt a wide variety of different processing and analysis techniques. Recently, the addition of access to TensorFlow through Google CoLabs has exposed advanced data science and machine learning techniques to users of the GEE earth observation archive. Not only does this enable new tools for the remote sensing scientific community, but it also introduces data scientists to earth observation data analysis using familiar tools and platforms.
For this Special Issue, we are soliciting contributions that demonstrate new algorithms, methods or applications implemented in either of the GEE APIs. We particularly encourage studies that introduce new analysis techniques, address challenges in implementing large-scale and/or long-time series analysis, and those that share code or application examples. While the main focus of this Special Issue will be on methodological advances using GEE, site-specific case studies that employ GEE functions or tools to advance scientific understanding of environmental and societal issues are also welcome.
Dr. Koreen Millard Mr. Alexandre R. Bevington Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPIs English editing service prior to publication or during author revisions.
Key Words
1 Google Earth Engine
2 Environmental change
3 Cloud computing
4 Big data
5 Data democratization
6 Machine learning
Published Papers (6 papers)
Zhang, Zhaoming; Wei, Mingyue; Pu, Dongchuan; He, Guojin; Wang, Guizhou; Long, Tengfei. 2021. "Assessment of Annual Composite Images Obtained by Google Earth Engine for Urban Areas Mapping Using Random Forest" Remote Sens. 13, no. 4: 748. https://doi.org/10.3390/rs13040748
Praticò, Salvatore; Solano, Francesco; Di Fazio, Salvatore; Modica, Giuseppe. 2021. "Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation" Remote Sens. 13, no. 4: 586. https://doi.org/10.3390/rs13040586
Bai, Bingxin; Tan, Yumin; Donchyts, Gennadii; Haag, Arjen; Weerts, Albrecht. 2020. "A Simple Spatio–Temporal Data Fusion Method Based on Linear Regression Coefficient Compensation" Remote Sens. 12, no. 23: 3900. https://doi.org/10.3390/rs12233900
Amani, Meisam; Kakooei, Mohammad; Moghimi, Armin; Ghorbanian, Arsalan; Ranjgar, Babak; Mahdavi, Sahel; Davidson, Andrew; Fisette, Thierry; Rollin, Patrick; Brisco, Brian; Mohammadzadeh, Ali. 2020. "Application of Google Earth Engine Cloud Computing Platform, Sentinel Imagery, and Neural Networks for Crop Mapping in Canada" Remote Sens. 12, no. 21: 3561. https://doi.org/10.3390/rs12213561
Ji, Hanyu; Li, Xing; Wei, Xinchun; Liu, Wei; Zhang, Lianpeng; Wang, Lijuan. 2020. "Mapping 10-m Resolution Rural Settlements Using Multi-Source Remote Sensing Datasets with the Google Earth Engine Platform" Remote Sens. 12, no. 17: 2832. https://doi.org/10.3390/rs12172832
Seydi, Seyd T.; Akhoondzadeh, Mehdi; Amani, Meisam; Mahdavi, Sahel. 2021. "Wildfire Damage Assessment over Australia Using Sentinel-2 Imagery and MODIS Land Cover Product within the Google Earth Engine Cloud Platform" Remote Sens. 13, no. 2: 220. https://doi.org/10.3390/rs13020220
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