scholarly journals Earthquake observation data grading and storage research

2021 ◽  
Vol 2024 (1) ◽  
pp. 012038
Author(s):  
Kun Zhu ◽  
Hui Liu ◽  
Yu Ji ◽  
Zehao Li
Keyword(s):  
Observation Data ◽  
Earthquake Observation ◽  
And Storage

scholarly journals Achieving the Full Vision of Earth Observation Data Cubes

Data ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 94 ◽  
Author(s):  
Steve Kopp ◽  
Peter Becker ◽  
Abhijit Doshi ◽  
Dawn J. Wright ◽  
Kaixi Zhang ◽  
...  
Keyword(s):  
Image Data ◽  
Earth Observation ◽  
Data Cube ◽  
Observation Data ◽  
Data Cubes ◽  
Multiple Data ◽  
Storage Location ◽  
Science Community ◽  
Earth Observation Data ◽  
And Storage

Earth observation imagery have traditionally been expensive, difficult to find and access, and required specialized skills and software to transform imagery into actionable information. This has limited adoption by the broader science community. Changes in cost of imagery and changes in computing technology over the last decade have enabled a new approach for how to organize, analyze, and share Earth observation imagery, broadly referred to as a data cube. The vision and promise of image data cubes is to lower these hurdles and expand the user community by making analysis ready data readily accessible and providing modern approaches to more easily analyze and visualize the data, empowering a larger community of users to improve their knowledge of place and make better informed decisions. Image data cubes are large collections of temporal, multivariate datasets typically consisting of analysis ready multispectral Earth observation data. Several flavors and variations of data cubes have emerged. To simplify access for end users we developed a flexible approach supporting multiple data cube styles, referencing images in their existing structure and storage location, enabling fast access, visualization, and analysis from a wide variety of web and desktop applications. We provide here an overview of that approach and three case studies.

A machine learning damage prediction model for the 2017 Puebla-Morelos, Mexico, earthquake

2020 ◽  
Vol 36 (2_suppl) ◽  
pp. 314-339
Author(s):  
Samuel Roeslin ◽  
Quincy Ma ◽  
Hugon Juárez-Garcia ◽  
Alonso Gómez-Bernal ◽  
Joerg Wicker ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Prediction Model ◽  
Mexico City ◽  
Engineering Students ◽  
Structural Characteristics ◽  
Observation Data ◽  
Damage Prediction ◽  
Mexico Earthquake ◽  
Earthquake Observation

The 2017 Puebla, Mexico, earthquake event led to significant damage in many buildings in Mexico City. In the months following the earthquake, civil engineering students conducted detailed building assessments throughout the city. They collected building damage information and structural characteristics for 340 buildings in the Mexico City urban area, with an emphasis on the Roma and Condesa neighborhoods where they assessed 237 buildings. These neighborhoods are of particular interest due to the availability of seismic records captured by nearby recording stations, and preexisting information from when the neighborhoods were affected by the 1985 Michoacán earthquake. This article presents a case study on developing a damage prediction model using machine learning. It details a framework suitable for working with future post-earthquake observation data. Four algorithms able to perform classification tasks were trialed. Random forest, the best performing algorithm, achieves more than 65% prediction accuracy. The study of the feature importance for the random forest shows that the building location, seismic demand, and building height are the parameters that influence the model output the most.

Distributed Information System for Processing and Storage of Meteorological Data

2016 ◽  
Vol 685 ◽  
pp. 867-871 ◽  
Author(s):  
Vladislav S. Sherstnyov ◽  
Anna I. Sherstnyova ◽  
Igor A. Botygin ◽  
Denis A. Kustov
Keyword(s):  
Data Processing ◽  
Data Storage ◽  
Meteorological Data ◽  
Data Gathering ◽  
Pilot Project ◽  
Network Storage ◽  
Observation Data ◽  
Distributed Network ◽  
Processing And Storage ◽  
And Storage

The following article features the results of developing distributed network storage of ground meteorological observation data. The data is represented with the national variant of international rapid transmission code of environment data from meteorological stations across the Russian Federation. They are available for researchers in both visual and common export formats. The design of the distributed network storage of meteorological data includes the following modules: dispatcher module (monitors calculation nodes, distributes data to nodes, processes client requests), client module (allows external researchers to access the meteorological data), terminal module (used to import new meteorological data), data processing and storage module (a node for distributed meteorological data storage, consists of two sub-modules for data processing and data storage respectively). The article displays the results of practical testing of the developed software. To simulate the cluster of informational and calculation servers in the pilot project, multithreading was used. Multithreading is supported by nearly every operational system for parallel data processing. The development tools chosen for the network storage allowed to design storage module interaction with the optimal efficiency, to ensure proper performance, stability and reliability of processing and managing large amounts of data. The obtained results allow using the designs for efficient management of meteorological surface observation data, for rapid data gathering, for systematization and storage of hydro-meteorological data in different alphanumeric codes and other related categories.

scholarly journals CBERS DATA CUBE: A POWERFUL TECHNOLOGY FOR MAPPING AND MONITORING BRAZILIAN BIOMES

2020 ◽  
Vol V-3-2020 ◽  
pp. 533-539
Author(s):  
M. C. A. Picoli ◽  
R. Simoes ◽  
M. Chaves ◽  
L. A. Santos ◽  
A. Sanchez ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Data Cube ◽  
Multidimensional Data ◽  
Observation Data ◽  
Earth Observation Data ◽  
Processing And Storage ◽  
Multidimensional Data Cubes ◽  
And Storage

Abstract. Currently, the overwhelming amount of Earth Observation data demands new solutions regarding processing and storage. To reduce the amount of time spent in searching, downloading and pre-processing data, the remote Sensing community is coming to an agreement on the minimum amount of corrections satellite images must convey in order to reach the broadest range of applications. Satellite imagery meeting such criteria (which usually include atmospheric, radiometric and topographic corrections) are generically called Analysis Ready Data (ARD). Furthermore, ARD is being assembled into multidimensional data cubes, minimising preprocessing tasks and allowing scientists and users in general to focus on analysis. A particular instance of this is the Brazil Data Cube (BDC) project, which is processing remote sensing images of medium spatial resolution into ARD datasets and assembling them as multidimensional cubes of the Brazilian territory. For example, BDC users are released from performing tasks such as image co-registration , aerosol interference correction. This work presents a BDC proof of concept, by analysing a BDC data cube made with images from the fourth China-Brazil Earth Resources Satellite (CBERS-4) of one of the largest biodiversity hotspot in the world, the Cerrado biome. It also shows how to map and monitor land use and land cover using the CBERS data cube. We demonstrate that the CBERS data cube is effective in resolving land use and and land cover issues to meet local and national needs related to the landscape dynamics, including deforestation, carbon emissions, and public policies.

An Overview of Digital Image Processing and Recognition (Invited Paper)

1982 ◽  
Vol 40 ◽  
pp. 700-702
Author(s):  
R. C. Gonzalez
Keyword(s):  
Image Processing ◽  
New York ◽  
Digital Image Processing ◽  
Digital Image ◽  
Practical Implementation ◽  
Submarine Cable ◽  
Vigorous Growth ◽  
Digitized Pictures ◽  
Processing Techniques ◽  
And Storage

Interest in digital image processing techniques dates back to the early 1920's, when digitized pictures of world news events were first transmitted by submarine cable between New York and London. Applications of digital image processing concepts, however, did not become widespread until the middle 1960's, when third-generation digital computers began to offer the speed and storage capabilities required for practical implementation of image processing algorithms. Since then, this area has experienced vigorous growth, having been a subject of interdisciplinary research in fields ranging from engineering and computer science to biology, chemistry, and medicine.

Control of Secretory Production in the Isopod Hepatopancreas

1972 ◽  
Vol 30 ◽  
pp. 166-167
Author(s):  
John W. Roberts ◽  
E. R. Witkus
Keyword(s):  
B Cells ◽  
Morphological Data ◽  
Secretory Product ◽  
Secretory Function ◽  
Secretory Material ◽  
Blind Ending ◽  
Secretory Production ◽  
Regenerative Cells ◽  
Immature Cells ◽  
And Storage

The isopod hepatopancreas, as exemplified by Oniscus ascellus. is comprised of four blind-ending diverticula. The regenerative cells at the tip of each diverticula differentiate into either club-shaped B-cells, which serve a secretory function, or into conoid S-cells, which serve in the absorption and storage of nutrients.The glandular B-cells begin producing secretory material with the development of rough endoplasmic reticulum during their process of maturation from the undifferentiated regenerative cells. Cytochemical and morphological data indicate that the hepatopancreas sequentially produces two types of secretory material within the large club-shaped cells. The production of the carbohydrate-like secretory product in immature cells seems to be phased out as the production of the osmiophilic secretion was phased in as the cell matured.

Quantitative Analysis Of X-Ray Images From Geological Materials

1990 ◽  
Vol 48 (2) ◽  
pp. 244-245
Author(s):  
J. M. Paque ◽  
R. Browning ◽  
P. L. King ◽  
P. Pianetta
Keyword(s):  
Quantitative Analysis ◽  
Bulk Composition ◽  
Principal Component ◽  
Analytical Description ◽  
Bulk Sample ◽  
Geological Samples ◽  
X Ray ◽  
Software And Hardware ◽  
And Storage ◽  
Insight Into

Geological samples typically contain many minerals (phases) with multiple element compositions. A complete analytical description should give the number of phases present, the volume occupied by each phase in the bulk sample, the average and range of composition of each phase, and the bulk composition of the sample. A practical approach to providing such a complete description is from quantitative analysis of multi-elemental x-ray images.With the advances in recent years in the speed and storage capabilities of laboratory computers, large quantities of data can be efficiently manipulated. Commercial software and hardware presently available allow simultaneous collection of multiple x-ray images from a sample (up to 16 for the Kevex Delta system). Thus, high resolution x-ray images of the majority of the detectable elements in a sample can be collected. The use of statistical techniques, including principal component analysis (PCA), can provide insight into mineral phase composition and the distribution of minerals within a sample.

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