scholarly journals Detecting Demolished Buildings after a Natural Hazard Using High Resolution RGB Satellite Imagery and Modified U-Net Convolutional Neural Networks

2021 ◽  
Vol 13 (11) ◽  
pp. 2176
Author(s):  
Vahid Rashidian ◽  
Laurie G. Baise ◽  
Magaly Koch ◽  
Babak Moaveni
Keyword(s):  
Satellite Imagery ◽  
Natural Hazard ◽  
Google Earth ◽  
Intelligence Agency ◽  
Independent Dataset ◽  
Learning Framework ◽  
And Performance ◽  
Collapsed Buildings ◽  
The U.S ◽  
Trained Network

Collapsed buildings are usually linked with the highest number of human casualties reported after a natural disaster; therefore, quickly finding collapsed buildings can expedite rescue operations and save human lives. Recently, many researchers and agencies have tried to integrate satellite imagery into rapid response. The U.S. Defense Innovation Unit Experimental (DIUx) and National Geospatial Intelligence Agency (NGA) have recently released a ready-to-use dataset known as xView that contains thousands of labeled VHR RGB satellite imagery scenes with 30-cm spatial and 8-bit radiometric resolutions, respectively. Two of the labeled classes represent demolished buildings with 1067 instances and intact buildings with more than 300,000 instances, and both classes are associated with building footprints. In this study, we are using the xView imagery, with building labels (demolished and intact) to create a deep learning framework for classifying buildings as demolished or intact after a natural hazard event. We have used a modified U-Net style fully convolutional neural network (CNN). The results show that the proposed framework has 78% and 95% sensitivity in detecting the demolished and intact buildings, respectively, within the xView dataset. We have also tested the transferability and performance of the trained network on an independent dataset from the 19 September 2017 M 7.1 Pueblo earthquake in central Mexico using Google Earth imagery. To this end, we tested the network on 97 buildings including 10 demolished ones by feeding imagery and building footprints into the trained algorithm. The sensitivity for intact and demolished buildings was 89% and 60%, respectively.

scholarly journals Classifying Economic Areas for Urban Planning using Deep Learning and Satellite Imagery in East Africa

2021 ◽  
Author(s):  
Davy Uwizera ◽  
Charles Ruranga ◽  
Patrick McSharry
Keyword(s):  
Satellite Imagery ◽  
Low Income ◽  
Google Earth ◽  
The Arctic ◽  
Target Area ◽  
Commercial Building ◽  
Industrial Zone ◽  
Middle Income ◽  
Intelligence Agency ◽  
Spatial Area

<div>In this research we use data from a number of different sources of satellite imagery. Below we describe and visualize various metrics of the datasets being considered. Satellite imagery is retrieved from Google earth which is supported by Data SIO (Scripps Institution of Oceanography), NOAA (National Oceanic and Atmospheric Administration), US. Navy (United States Navy), NGA (National Geospatial-Intelligence Agency), GEBCO (General Bathymetric Chart of the Oceans), Image Landsat, and Image IBCAO (International Bathymetric Chart of the Arctic Ocean). Using random sampling of spatial area in Kigali per target area, 342,843 thousands images were retrieved under the five categories: residential high income (78941), residential low income(162501), residential middle income(101401), commercial building, (67400) and industrial zone,(24400). For the industrial zone, we also included some images from Nairobi, Kenya industrial spatial area. The average number of samples for a category is 86929. The size of the sample per category is proportional to the size of the spatial target area considered per category. Kigali is located at latitude:-1.985070 and longitude:-1.985070, coordinates. Nairobi is located at latitude:-1.286389 and longitude:36.817223, coordinates.</div>

scholarly journals Classifying Economic Areas for Urban Planning using Deep Learning and Satellite Imagery in East Africa

2021 ◽  
Author(s):  
Davy Uwizera ◽  
Charles Ruranga ◽  
Patrick McSharry
Keyword(s):  
Satellite Imagery ◽  
Low Income ◽  
Google Earth ◽  
The Arctic ◽  
Target Area ◽  
Commercial Building ◽  
Industrial Zone ◽  
Middle Income ◽  
Intelligence Agency ◽  
Spatial Area

<div>In this research we use data from a number of different sources of satellite imagery. Below we describe and visualize various metrics of the datasets being considered. Satellite imagery is retrieved from Google earth which is supported by Data SIO (Scripps Institution of Oceanography), NOAA (National Oceanic and Atmospheric Administration), US. Navy (United States Navy), NGA (National Geospatial-Intelligence Agency), GEBCO (General Bathymetric Chart of the Oceans), Image Landsat, and Image IBCAO (International Bathymetric Chart of the Arctic Ocean). Using random sampling of spatial area in Kigali per target area, 342,843 thousands images were retrieved under the five categories: residential high income (78941), residential low income(162501), residential middle income(101401), commercial building, (67400) and industrial zone,(24400). For the industrial zone, we also included some images from Nairobi, Kenya industrial spatial area. The average number of samples for a category is 86929. The size of the sample per category is proportional to the size of the spatial target area considered per category. Kigali is located at latitude:-1.985070 and longitude:-1.985070, coordinates. Nairobi is located at latitude:-1.286389 and longitude:36.817223, coordinates.</div>

scholarly journals LARGE SCALE CITY MAPPING USING SATELLITE IMAGERY / KOSMINIŲ NUOTRAUKŲ IŠ GOOGLE EARTH, TAIKOMŲ MIESTAMS KARTOGRAFUOTI STAMBIUOJU MASTELIU, REKTIFIKAVIMAS / РЕКТИФИКАЦИЯ КОСМИЧЕСКИХ СНИМКОВ ИЗ GOOGLE EARTH ДЛЯ КРУПНОМАСШТАБНОГО КАРТОГРАФИРОВАНИЯ ГОРОДОВ

2012 ◽  
Vol 37 (4) ◽  
pp. 168-171 ◽  
Author(s):  
Birutė Ruzgienė ◽  
Qian Yi Xiang ◽  
Silvija Gečytė
Keyword(s):  
Satellite Imagery ◽  
Large Scale ◽  
Modern Technology ◽  
Google Earth ◽  
Aerial Images ◽  
Control Points ◽  
Image Rectification ◽  
Image Deformation ◽  
Map Construction ◽  
Short Period

The rectification of high resolution digital aerial images or satellite imagery employed for large scale city mapping is modern technology that needs well distributed and accurately defined control points. Digital satellite imagery, obtained using widely known software Google Earth, can be applied for accurate city map construction. The method of five control points is suggested for imagery rectification introducing the algorithm offered by Prof. Ruan Wei (tong ji University, Shanghai). Image rectification software created on the basis of the above suggested algorithm can correct image deformation with required accuracy, is reliable and keeps advantages in flexibility. Experimental research on testing the applied technology has been executed using GeoEye imagery with Google Earth builder over the city of Vilnius. Orthophoto maps at the scales of 1:1000 and 1:500 are generated referring to the methodology of five control points. Reference data and rectification results are checked comparing with those received from processing digital aerial images using a digital photogrammetry approach. The image rectification process applying the investigated method takes a short period of time (about 4-5 minutes) and uses only five control points. The accuracy of the created models satisfies requirements for large scale mapping. Santrauka Didelės skiriamosios gebos skaitmeninių nuotraukų ir kosminių nuotraukų rektifikavimas miestams kartografuoti stambiuoju masteliu yra nauja technologija. Tai atliekant būtini tikslūs ir aiškiai matomi kontroliniai taškai. Skaitmeninės kosminės nuotraukos, gautos taikant plačiai žinomą programinį paketą Google Earth, gali būti naudojamos miestams kartografuoti dideliu tikslumu. Siūloma nuotraukas rektifikuoti Penkių kontrolinių taskų metodu pagal prof. Ruan Wei (Tong Ji universitetas, Šanchajus) algoritmą. Moksliniam eksperimentui pasirinkta Vilniaus GeoEye nuotrauka iš Google Earth. 1:1000 ir 1:500 mastelio ortofotografiniai žemėlapiai sudaromi Penkių kontrolinių taškų metodu. Rektifikavimo duomenys lyginami su skaitmeninių nuotraukų apdorojimo rezultatais, gautais skaitmeninės fotogrametrijos metodu. Nuotraukų rektifikavimas Penkių kontrolinių taskų metodu atitinka kartografavimo stambiuoju masteliu reikalavimus, sumažėja laiko sąnaudos. Резюме Ректификация цифровых и космических снимков высокой резолюции для крупномасштабного картографирования является новой технологией, требующей точных и четких контрольных точек. Цифровые космические снимки, полученные с использованием широкоизвестного программного пакета Google Earth, могут применяться для точного картографирования городов. Для ректификации снимков предложен метод пяти контрольных точек с применением алгоритма проф. Ruan Wei (Университет Tong Ji, Шанхай). Для научного эксперимента использован снимок города Вильнюса GeoEye из Google Earth. Ортофотографические карты в масштабе 1:1000 и 1:500 генерируются с применением метода пяти контрольных точек. Полученные результаты и данные ректификации сравниваются с результатами цифровых снимков, полученных с применением метода цифровой фотограмметрии. Ректификация снимков с применением метода пяти контрольных точек уменьшает временные расходы и удовлетворяет требования, предъявляемые к крупномасштабному картографированию.

scholarly journals Development, selection criteria, and performance of Composite IV sheep at the U.S. Meat Animal Research Center1,2

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. S150-S154
Author(s):  
Thomas W Murphy ◽  
Brad A Freking ◽  
Gary L Bennett ◽  
John W Keele
Keyword(s):  
Selection Criteria ◽  
Animal Research ◽  
Meat Animal ◽  
And Performance ◽  
The U.S

The Clinton Administration and Africa: White House Involvement and the Foreign Affairs Bureaucracies

1998 ◽  
Vol 26 (2) ◽  
pp. 8-13
Author(s):  
John F. Clark
Keyword(s):  
Foreign Policy ◽  
Clinton Administration ◽  
The United States ◽  
Policy Formulation ◽  
Central Intelligence Agency ◽  
Intelligence Agency ◽  
White House ◽  
Continuity And Change ◽  
The Cold War ◽  
The U.S

Both continuity and change capture the evolving role of the Clinton White House in the formulation and implementation of U.S. foreign policy toward Africa. Elements of continuity are reflected in a familiar pattern of relationships between the White House and the principal foreign policy bureaucracies, most notably the U.S. State Department, the U.S. Department of Defense (Pentagon), the Central Intelligence Agency (CIA), and more recently the U.S. Department of Commerce. As cogently argued in Peter J. Schraeder’s analysis of U.S. foreign policy toward Africa during the Cold War era, the White House has tended to take charge of U.S. African policies only in those relatively rare situations perceived as crises by the president and his closest advisors. In other, more routine situations—the hallmark of the myriad of U.S. African relations—the main foreign policy bureaucracies have been at the forefront of policy formulation, and “bureaucratic dominance” of the policymaking process has prevailed. Much the same pattern is visible in the Clinton administration, with the exception of President Clinton’s trip to Africa in 1998. Until that time, events in Somalia in 1993 served as the only true African crisis of the administration that was capable of focusing the ongoing attention of President Clinton and his closest advisors. Given that the United States is now disengaged from most African crises, Africa has remained a “backwater” for the White House and the wider foreign policymaking establishment.

On improving the training of aircraft engineers

2021 ◽  
Vol 20 (5) ◽  
pp. 865-885
Author(s):  
Leonid B. SOBOLEV
Keyword(s):  
Aviation Industry ◽  
Information Protection ◽  
Global Trends ◽  
Space Industry ◽  
The Poor ◽  
Military Aviation ◽  
And Performance ◽  
Performance Results ◽  
The Military ◽  
The U.S

Subject. The article continues the discussion about the method of training aircraft engineers to work in the military and civil segments of aviation and rocket-and-space industry. Objectives. The purpose is to improve the training of Russian engineers to work in the competitive market environment, on the basis of the analysis of experience in training the aviation engineers in leading foreign technical universities. Methods. The study rests on the comparative analysis of implementation of major projects in the military and civil segments of aviation in the U.S. and Russia, as well as programs for training aircraft engineers in both countries. Results. The analysis shows that the duration of modern large military aviation projects in both countries is the same (the comparison of cost is impossible, due to information protection in Russia), while in the civil segment of the aviation industry, Russia's lagging behind is significant both in terms of the duration of projects and performance results. One of the reasons is in the poor training of aircraft engineers to work in the competitive environment. Conclusions. It is crucial to reform Russian aviation universities in terms of conformity to global trends in multidisciplinarity and differentiation of financing and research base.

scholarly journals Characterizing the Spatial and Temporal Availability of Very High Resolution Satellite Imagery in Google Earth and Microsoft Bing Maps as a Source of Reference Data

Land ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 118 ◽  
Author(s):  
Myroslava Lesiv ◽  
Linda See ◽  
Juan Laso Bayas ◽  
Tobias Sturn ◽  
Dmitry Schepaschenko ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Reference Data ◽  
Temporal Distribution ◽  
Google Earth ◽  
Training Data ◽  
Visual Interpretation ◽  
The Usa ◽  
Very High

Very high resolution (VHR) satellite imagery from Google Earth and Microsoft Bing Maps is increasingly being used in a variety of applications from computer sciences to arts and humanities. In the field of remote sensing, one use of this imagery is to create reference data sets through visual interpretation, e.g., to complement existing training data or to aid in the validation of land-cover products. Through new applications such as Collect Earth, this imagery is also being used for monitoring purposes in the form of statistical surveys obtained through visual interpretation. However, little is known about where VHR satellite imagery exists globally or the dates of the imagery. Here we present a global overview of the spatial and temporal distribution of VHR satellite imagery in Google Earth and Microsoft Bing Maps. The results show an uneven availability globally, with biases in certain areas such as the USA, Europe and India, and with clear discontinuities at political borders. We also show that the availability of VHR imagery is currently not adequate for monitoring protected areas and deforestation, but is better suited for monitoring changes in cropland or urban areas using visual interpretation.

Sign in / Sign up

Export Citation Format

Share Document