scholarly journals First satellite imaging of auroral pulsations by the Fast Auroral Imager on e‐ POP

2015 ◽  
Vol 42 (17) ◽  
pp. 6877-6882 ◽  
Author(s):  
A. T. Y. Lui ◽  
L. L. Cogger ◽  
A. Howarth ◽  
A. W. Yau
Keyword(s):  
Satellite Imaging ◽  
Auroral Imager

Satellite imaging reveals increased proportion of population exposed to floods

Nature ◽  
2021 ◽  
Vol 596 (7870) ◽  
pp. 80-86
Author(s):  
B. Tellman ◽  
J. A. Sullivan ◽  
C. Kuhn ◽  
A. J. Kettner ◽  
C. S. Doyle ◽  
...  
Keyword(s):  
Satellite Imaging

scholarly journals CNN Algorithm for Roof Detection and Material Classification in Satellite Images

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1592
Author(s):  
Jonguk Kim ◽  
Hyansu Bae ◽  
Hyunwoo Kang ◽  
Suk Gyu Lee
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Satellite Imagery ◽  
Satellite Images ◽  
External Shocks ◽  
Satellite Imaging ◽  
Material Classification ◽  
Model Algorithm ◽  
Great Damage

This paper suggests an algorithm for extracting the location of a building from satellite imagery and using that information to modify the roof content. The materials are determined by measuring the conditions where the building is located and detecting the position of a building in broad satellite images. Depending on the incomplete roof or material, there is a greater possibility of great damage caused by disaster situations or external shocks. To address these problems, we propose an algorithm to detect roofs and classify materials in satellite images. Satellite imaging locates areas where buildings are likely to exist based on roads. Using images of the detected buildings, we classify the material of the roof using a proposed convolutional neural network (CNN) model algorithm consisting of 43 layers. In this paper, we propose a CNN structure to detect areas with buildings in large images and classify roof materials in the detected areas.

scholarly journals Modeling and Simulation of Mission Planning Problem for Remote Sensing Satellite Imaging

2018 ◽  
Vol 179 ◽  
pp. 03024 ◽  
Author(s):  
Yao Pan ◽  
Zhong Ming Chi ◽  
Qi Long Rao ◽  
Kai Peng Sun ◽  
Yi Nan Liu
Keyword(s):  
Remote Sensing ◽  
Time Window ◽  
Constraint Satisfaction Problem ◽  
Optimal Path ◽  
Time Constraint ◽  
Mission Planning ◽  
Planning Problem ◽  
Satellite Imaging ◽  
Problem Model ◽  
Remote Sensing Satellite

Mission planning problem for remote sensing satellite imaging is studied. Firstly, the time constraint satisfaction problem model is presented after analyzing the characteristic of time constraint. Then, An optimal path searching algorithm based on the discrete time window is proposed according to the non-uniqueness for satellite to mission in the visible time window. Simulation results verify the efficiency of the model and algorithm.

Emplacement of the 1907 Mauna Loa basalt flow as derived from precision topography and satellite imaging

2008 ◽  
Vol 177 (4) ◽  
pp. 837-847 ◽  
Author(s):  
James R. Zimbelman ◽  
W. Brent Garry ◽  
Andrew K. Johnston ◽  
Steven H. Williams
Keyword(s):  
Basalt Flow ◽  
Satellite Imaging ◽  
Mauna Loa

Remote Sensing in Oil Spill Handling in Offshore North West Java

2021 ◽  
Author(s):  
Audra Ligafinza ◽  
Farasdaq Muchibbus Sajjad ◽  
Mohammad Abdul Jabbar ◽  
Anggia Fatmawati ◽  
Alvin Derry Wirawan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Oil Spill ◽  
Environmental Damage ◽  
Satellite Imaging ◽  
North West ◽  
Novel Technologies ◽  
Oil Boom ◽  
Remediation Strategies ◽  
Restoration Strategies

Abstract During the blowout event, it is critical to track the oil spill to minimize environmental damage and optimize restoration cost. In this paper, we deliver our success story in handling oil spill from recent experiences. We utilize remote sensing technologies to establish our analysis and plan the remediation strategies. We also comprehensively discuss the techniques to analyze big data from the satellites, to utilize the downloaded data for forecasting, and to align the satellite information with restoration strategies. PHE relies on its principle to maintain minimum damage and ensures safety by dividing the steps into several aspects of monitoring, response (offshore and onshore), shoreline management and waste management. PHE utilizes latest development in survey by using satellite imaging, survey boat, chopper and UAV drone. Spill containment is done using several layers of oil boom to recover oil spill, complemented with skimmers and storage tanks. PHE encourages shoreline remediation using nets and manual recovery for capturing oil sludge. Using this combination of technologies, PHE is able to model and anticipate oil spill movement from the source up until the farthest shoreline. This enables real time monitoring and handling, therefore minimum environmental damage is ensured. PHE also employs prudent engineering design based on real time field condition in order to ensure the equipment are highly suited for the condition, as well as ensuring good supply chain of the material availability. This publication addresses the first offshore blowout mitigation and handling in Indonesia that uses novel technologies such as static oil boom, satellite imaging and integrated effort in handling shoreline damage. It is hoped that the experience can be replicated for other offshore operating contractors in Indonesia in designing blowout remediation.

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