scholarly journals RESEARCH ON THE POLICIES AND LAWS OF INTERNATIONAL CIVILIAN REMOTE SENSING SATELLITES AND THEIR PROBLEMS

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 821-824
Author(s):  
X. P. Zhang ◽  
W. Y. Wan ◽  
G. Q. Zhou ◽  
T. Yue ◽  
B. Chen
Keyword(s):  
Remote Sensing ◽  
Outer Space ◽  
Rapid Development ◽  
Remote Sensing Data ◽  
Developed Countries ◽  
The United States ◽  
Policy Formulation ◽  
Sensing Data ◽  
The World ◽  
1960S And 1970S

Abstract. In the exploration of outer space and international space activities, civilian remote sensing satellites have made rapid development since the 1970s, and countries around the world have accelerated their civilian satellite development and its policy formulation as well. Regarding the regulations on the peaceful use of outer space and related space launches, the United Nations respectively formulated the "Five Treaties on Space" in the 1960s and 1970s to regulate the peaceful uses of outer space. However, in the development of civilian remote sensing satellites, orbital resources and application rules of remote sensing data, the implementation of "first-come, first-occupy" and "non-discriminatory access to remote sensing data" is mainly led by western developed countries, especially the space powers such as the United States and Russia. Based on the outer space policies and regulations, this article will make a comparative study of civilian remote sensing satellite development policies and related laws and regulations in major countries and regions in the world, so as to analyze the policies and legal principles of civilian remote sensing satellites, as well as its corresponding issues and problems.

The use of remote sensing data to extract information from agricultural land with emphasis on soil salinity

Soil Research ◽  
2003 ◽  
Vol 41 (7) ◽  
pp. 1243 ◽  
Author(s):  
F. M. Howari
Keyword(s):  
Remote Sensing ◽  
Soil Salinity ◽  
Large Scale ◽  
Agricultural Land ◽  
Rio Grande Valley ◽  
Remote Sensing Data ◽  
Rio Grande ◽  
The United States ◽  
Sensing Data ◽  
Matching Techniques

The rapid growth of information technologies has provided exciting new sources of data, interpretation tools, and modelling techniques to soil research and education communities at all levels. This paper presents some examples of the capability of remote sensing data such as Landsat ETM+, airborne visible/infrared imaging spectrometer (AVIRIS), colour infrared aerial photos (CIR), and high-resolution field spectroradiometer (GER 3700) to extract surface information about soil salinity. The study used image processing techniques such as supervised classification, spectral extraction, and matching techniques to investigate types and occurrences of salts in the Rio Grande Valley on the United States–Mexico border. Soil salinity groups were established using soil physico-chemical properties and image elements (absorption-reflectivity profiles, band combinations, grey tones of the investigated images, and textures of soil and vegetation covers as they appear in images). The lack of vegetation or scattered vegetation on salt-affected soil (SAS) surfaces makes it possible to detect salt in several locations of the investigated area. The presented remote sensing datasets reveal the presence of gypsum and halite as the dominant salt crusts in the Rio Grande Valley. This information can help agricultural scientists and engineers to produce large-scale maps of salt-affected lands, which will help improve salinity management in watersheds and ecosystems.

scholarly journals Combining Geostatistics and Remote Sensing Data to Improve Spatiotemporal Analysis of Precipitation

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3132
Author(s):  
Emmanouil A. Varouchakis ◽  
Anna Kamińska-Chuchmała ◽  
Grzegorz Kowalik ◽  
Katerina Spanoudaki ◽  
Manuel Graña
Keyword(s):  
Remote Sensing ◽  
Wide Spectrum ◽  
Rapid Development ◽  
Remote Sensing Data ◽  
Spatiotemporal Analysis ◽  
Space Time ◽  
Attractive Alternative ◽  
Kriging Method ◽  
Sensing Data ◽  
Crete Island

The wide availability of satellite data from many distributors in different domains of science has provided the opportunity for the development of new and improved methodologies to aid the analysis of environmental problems and to support more reliable estimations and forecasts. Moreover, the rapid development of specialized technologies in satellite instruments provides the opportunity to obtain a wide spectrum of various measurements. The purpose of this research is to use publicly available remote sensing product data computed from geostationary, polar and near-polar satellites and radar to improve space–time modeling and prediction of precipitation on Crete island in Greece. The proposed space–time kriging method carries out the fusion of remote sensing data with data from ground stations that monitor precipitation during the hydrological period 2009/10–2017/18. Precipitation observations are useful for water resources, flood and drought management studies. However, monitoring stations are usually sparse in regions with complex terrain, are clustered in valleys, and often have missing data. Satellite precipitation data are an attractive alternative to observations. The fusion of the datasets in terms of the space–time residual kriging method exploits the auxiliary satellite information and aids in the accurate and reliable estimation of precipitation rates at ungauged locations. In addition, it represents an alternative option for the improved modeling of precipitation variations in space and time. The obtained results were compared with the outcomes of similar works in the study area.

scholarly journals Application of Deep Learning Algorithm to Build an Automated Cloud Segmentation Model Based on Open Data Cube Framework

2020 ◽  
Vol 36 (4) ◽  
Author(s):  
Pham Vu Dong ◽  
Bui Quang Thanh ◽  
Nguyen Quoc Huy ◽  
Vo Hong Anh ◽  
Pham Van Manh
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Rapid Development ◽  
Open Data ◽  
Remote Sensing Data ◽  
Data Cube ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Sensing Data ◽  
Multi Temporal

Cloud detection is a significant task in optical remote sensing to reconstruct the contaminated cloud area from multi-temporal satellite images. Besides, the rapid development of machine learning techniques, especially deep learning algorithms, can detect clouds over a large area in optical remote sensing data. In this study, the method based on the proposed deep-learning method called ODC-Cloud, which was built on convolutional blocks and integrating with the Open Data Cube (ODC) platform. The results showed that our proposed model achieved an overall 90% accuracy in detecting cloud in Landsat 8 OLI imagery and successfully integrated with the ODC to perform multi-scale and multi-temporal analysis. This is a pioneer study in techniques of storing and analyzing big optical remote sensing data.

Multi-Stage Fusion and Multi-Source Attention Network for Multi-Modal Remote Sensing Image Segmentation

2021 ◽  
Vol 12 (6) ◽  
pp. 1-20
Author(s):  
Jiaqi Zhao ◽  
Yong Zhou ◽  
Boyu Shi ◽  
Jingsong Yang ◽  
Di Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Rapid Development ◽  
Remote Sensing Data ◽  
Semantic Segmentation ◽  
Sensor Technology ◽  
Feature Maps ◽  
Attention Network ◽  
Modal Data ◽  
Sensing Data ◽  
Multi Stage

With the rapid development of sensor technology, lots of remote sensing data have been collected. It effectively obtains good semantic segmentation performance by extracting feature maps based on multi-modal remote sensing images since extra modal data provides more information. How to make full use of multi-model remote sensing data for semantic segmentation is challenging. Toward this end, we propose a new network called Multi-Stage Fusion and Multi-Source Attention Network ((MS) 2 -Net) for multi-modal remote sensing data segmentation. The multi-stage fusion module fuses complementary information after calibrating the deviation information by filtering the noise from the multi-modal data. Besides, similar feature points are aggregated by the proposed multi-source attention for enhancing the discriminability of features with different modalities. The proposed model is evaluated on publicly available multi-modal remote sensing data sets, and results demonstrate the effectiveness of the proposed method.

Global trends of development of services in the post-industrial society

2014 ◽  
Vol 8 (9) ◽  
pp. 3-12
Author(s):  
Меружан Аветисян ◽  
Meruzhan Avetisyan
Keyword(s):  
Comparative Analysis ◽  
Service Sector ◽  
Rapid Development ◽  
Well Being ◽  
Developed Countries ◽  
The United States ◽  
Industrial Society ◽  
Stage Of Development ◽  
The World ◽  
Post Industrial

The concept of post-industrial society represents a society in which the economy as a result of the technological revolution and significant income growth went from pre-emptive priority production of goods to production of services, has recently become even more relevant. For example, the World Bank experts, authors of the report "Industry of the future: a new era of global growth and innovation" argue that if a country has reached the average level of well-being, the share of services in GDP of the country begins to exceed the performance of industry and agriculture. Currently, as post-industrial countries are classified those countries in which the service sector accounts for well over half of GDP. Fall under this criterion, in the first place, the United States (the service sector accounts for 79.4% of US GDP), European Union (the service sector is 69.4% of the GDP of the EU countries), and all developed countries. A comparative analysis of the service sector in Russia shows that without a radical increase in the efficiency of the sector the transition of our country in the post-industrial stage of development is impossible. The post-industrial structure of the economy suggests that overall GDP of more than 50% is formed by the service sector. The rapid development of the service sector and the increase of its share in the gross national product are features of the country´s transition to a post-industrial stage of development. Only relatively recently came the understanding of the important role services can play in the process of integration into the global economy and the international division of labor. Overall condition of the Russian service sector shows that without a radical increase in the efficiency of this sector, to speak of Russia´s transition to a post-industrial stage of development is prematurely. Comparative analysis of the dependence of the well-being of the world from the share of services in countries’ GDP, revealed a number of interesting facts that have enabled the author to supplement, clarify and restate the conclusion of international experts as follows: the service sector in the GDP of the country begins to exceed the performance of industry and agriculture if the country embarked on the path of the main characteristics of the post-industrial society - the development of services. The welfare of the country, in this case does not matter. Moreover, at present the number of countries in which the service sector accounts for well over half of GDP, is growing rapidly.

scholarly journals A Distributed Storage and Access Approach for Massive Remote Sensing Data in MongoDB

2019 ◽  
Vol 8 (12) ◽  
pp. 533 ◽  
Author(s):  
Shuang Wang ◽  
Guoqing Li ◽  
Xiaochuang Yao ◽  
Yi Zeng ◽  
Lushen Pang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Relational Databases ◽  
Large Scale ◽  
Distributed Storage ◽  
Rapid Development ◽  
Remote Sensing Data ◽  
Image Data ◽  
Sensing Data ◽  
Cluster Architecture ◽  
Distributed Cluster

With the rapid development of earth-observation technology, the amount of remote sensing data has increased exponentially, and traditional relational databases cannot satisfy the requirements of managing large-scale remote sensing data. To address this problem, this paper undertakes intensive research of the NoSQL (Not Only SQL) data management model, especially the MongoDB database, and proposes a new approach to managing large-scale remote sensing data. Firstly, based on the sharding technology of MongoDB, a distributed cluster architecture was designed and established for massive remote sensing data. Secondly, for the convenience in the unified management of remote sensing data, an archiving model was constructed, and remote sensing data, including structured metadata and unstructured image data, were stored in the above cluster separately, with the metadata stored in the form of a document, and image data stored with the GridFS mechanism. Finally, by designing different shard strategies and comparing MongoDB cluster with a typical relational database, several groups of experiments were conducted to verify the storage performance and access performance of the cluster. The experimental results show that the proposed method can overcome the deficiencies of traditional methods, as well as scale out the database, which is more suitable for managing massive remote sensing data and can provide technical support for the management of massive remote sensing data.

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