Higher Resolution Satellite Imagery of Israel and Palestine: Reassessing the Kyl-Bingaman Amendment

Zerbini, Andrea; Fradley, Michael

doi:10.1016/j.spacepol.2018.03.002

Cited by 16 publications

(10 citation statements)

References 13 publications

(15 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It should be noted that these areas were extracted, in a first stage, from Google Earth aerial imagery. It is remarkable, also, that the Google Earth uses aerial imagery, in natural colors, provided by Airbus Defense and Space under contract with French Centre National d'Études Spatiales (CNES) at a spatial resolution of 0.5 m [52]. The surfaces were then validated by field measurements using a Trimble GeoXH 2008 Series GPS device.…”

Section: Flash-flood Inventorymentioning

confidence: 99%

Novel Ensembles of Deep Learning Neural Network and Statistical Learning for Flash-Flood Susceptibility Mapping

Costache

Ngo

Bui

2020

Water

View full text Add to dashboard Cite

This study aimed to assess flash-flood susceptibility using a new hybridization approach of Deep Neural Network (DNN), Analytical Hierarchy Process (AHP), and Frequency Ratio (FR). A catchment area in south-eastern Romania was selected for this proposed approach. In this regard, a geospatial database of the flood with 178 flood locations and with 10 flash-flood predictors was prepared and used for this proposed approach. AHP and FR were used for processing and coding the predictors into a numeric format, whereas DNN, which is a powerful and state-of-the-art probabilistic machine leaning, was employed to build an inference flash-flood model. The reliability of the models was verified with the help of Receiver Operating Characteristic (ROC) Curve, Area Under Curve (AUC), and several statistical measures. The result shows that the two proposed ensemble models, DNN-AHP and DNN-FR, are capable of predicting future flash-flood areas with accuracy higher than 92%; therefore, they are a new tool for flash-flood studies.

show abstract

Section: Flash-flood Inventorymentioning

confidence: 99%

Novel Ensembles of Deep Learning Neural Network and Statistical Learning for Flash-Flood Susceptibility Mapping

Costache

Ngo

Bui

2020

Water

View full text Add to dashboard Cite

show abstract

“…Where the quality or coverage of freely-available imagery is not satisfactory (as is the case for Palestine [Zerbini and Fradley 2018]), EAMENA has been purchasing VHR, multispectral imagery from a wide range of international retailers.…”

Section: Methodology and Datasetsmentioning

confidence: 99%

Developing a Heritage Database for the Middle East and North Africa

Zerbini

2018

Journal of Field Archaeology

Self Cite

View full text Add to dashboard Cite

The Endangered Archaeology in the Middle East and North Africa (EAMENA) project based at the Universities of Oxford, Leicester, and Durham uses remote sensing to record archaeological and cultural heritage landscapes across twenty countries from Morocco to Iran and from Syria to Yemen. The project has developed an online heritage database built by adapting the open-source Arches heritage inventory platform developed by the Getty Conservation Institute and the World Monuments Fund. This paper discusses the process of customization of Arches v.3 adopted by EAMENA, particularly with regard to the development of new CIDOC CRM resource data models, reference data, and modifications to the Arches codebase.

show abstract

“…Flights over Israel are known to have targeted the Dimona nuclear facility, and possibly other cities and sites, but have been systematically excluded from the declassification process. This is likely due to the 1996 Kyl-Bingaman Amendment to the 1997 Defense Authorization Act, which restricts the resolution of imagery of Israel and Palestine that may be distributed by US-based companies (Zerbini and Fradley 2018).…”

Section: Cameras Film and Middle Eastern Flightsmentioning

confidence: 99%

Near Eastern Landscapes and Declassified U2 Aerial Imagery

Hammer

2019

Adv. archaeol. pract.

View full text Add to dashboard Cite

Recently declassified photographs taken by U2 spy planes in the 1950s and 1960s provide an important new source of historical aerial imagery useful for Eurasian archaeology. Like other sources of historical imagery, U2 photos provide a window into the past, before modern agriculture and development destroyed many archaeological sites. U2 imagery is older and in many cases higher resolution than CORONA spy satellite imagery, the other major source of historical imagery for Eurasia, and thus can expand the range of archaeological sites and features that can be studied from an aerial perspective. However, there are significant barriers to finding and retrieving U2 imagery of particular locales, and archaeologists have thus not yet widely used it. In this article, we aim to reduce these barriers by describing the U2 photo dataset and how to access it. We also provide the first spatial index of U2 photos for the Middle East. A brief discussion of archaeological case studies drawn from U2 imagery illustrates its merits and limitations. These case studies include investigations of prehistoric mass-kill hunting traps in eastern Jordan, irrigation systems of the first millennium BC Neo-Assyrian Empire in northern Iraq, and twentieth-century marsh communities in southern Iraq.

show abstract

Higher Resolution Satellite Imagery of Israel and Palestine: Reassessing the Kyl-Bingaman Amendment

Cited by 16 publications

References 13 publications

Novel Ensembles of Deep Learning Neural Network and Statistical Learning for Flash-Flood Susceptibility Mapping

Novel Ensembles of Deep Learning Neural Network and Statistical Learning for Flash-Flood Susceptibility Mapping

Developing a Heritage Database for the Middle East and North Africa

Near Eastern Landscapes and Declassified U2 Aerial Imagery

Contact Info

Product

Resources

About