Rugged: an operational, open-source solution for Sentinel-2 mapping

2015 ◽  
Author(s):  
Luc Maisonobe ◽  
Jean Seyral ◽  
Guylaine Prat ◽  
Jonathan Guinet ◽  
Aude Espesset
Keyword(s):  
Open Source ◽  
Sentinel 2

scholarly journals Mise en œuvre d'outils open source pour le suivi opérationnel de l'occupation des sols et de la déforestation à partir des données Sentinel radar et optique : études de cas en Guyane et au Togo

2020 ◽  
pp. 59-70
Author(s):  
Cédric Lardeux ◽  
Anoumou Kemavo ◽  
Maxence Rageade ◽  
Mathieu Rahm ◽  
Pierre-Louis Frison ◽  
...  
Keyword(s):  
Open Source ◽  
Grand Nombre ◽  
Mise En Œuvre ◽  
Sentinel 2

La télédétection est un outil particulièrement adapté pour le suivi de l'occupation des sols en général ; il est aussi particulièrement utilisé pour le suivi de la déforestation. De par l'envoi des satellites Sentinel-1 et Sentinel-2 via le programme Copernicus, la communauté dispose maintenant de données gratuites, avec des périodes de revisites temporelles réduites, permettant au plus grand nombre de suivre efficacement la dynamique d'occupation d'une zone d'étude. Ce papier présente une méthode de suivi de l'occupation du sol se basant sur la combinaison de deux approches s'appuyant sur des outils logiciels Open Source (QGIS, Orfeo ToolBox, python). Nous combinons tout d'abord le suivi de l'occupation du sol à une échelle de temps annuelle utilisant des données Sentinel-1 et Sentinel-2 puis une approche de suivi des changements liés à la déforestation à une échelle de temps bi-mensuelle à partir des données Sentinel-1. Les résultats obtenus démontrent la bonne synergie de ces approches, qui permettent d'utiliser de façon complémentaire les données optiques et radar. En vue de rendre accessible la méthode proposée, tous les outils Open source utilisés sont disponibles sur ce lien : http://remotesensing4all.net/index.php/2018/09/11/kit-dutilisation-des-donnees-radar-sentinel-1-lors-de-latelier-radar-du-foss4g-fr-2018-2/.

scholarly journals Detecting Change at Archaeological Sites in North Africa Using Open-Source Satellite Imagery

2020 ◽  
Vol 12 (22) ◽  
pp. 3694
Author(s):  
Louise Rayne ◽  
Maria Carmela Gatto ◽  
Lamin Abdulaati ◽  
Muftah Al-Haddad ◽  
Martin Sterry ◽  
...  
Keyword(s):  
Open Source ◽  
Satellite Imagery ◽  
North Africa ◽  
Urban Areas ◽  
Google Earth ◽  
Archaeological Sites ◽  
Natural Processes ◽  
Registration Errors ◽  
Training Courses ◽  
Sentinel 2

Our paper presents a remote sensing workflow for identifying modern activities that threaten archaeological sites, developed as part of the work of the Endangered Archaeology of the Middle East and North Africa (EAMENA) project. We use open-source Sentinel-2 satellite imagery and the free tool Google Earth Engine to run a per-pixel change detection to make the methods and data as accessible as possible for heritage professionals. We apply this and perform validation at two case studies, the Aswan and Kom-Ombo area in Egypt, and the Jufra oases in Libya, with an overall accuracy of the results ranging from 85–91%. Human activities, such as construction, agriculture, rubbish dumping and natural processes were successfully detected at archaeological sites by the algorithm, allowing these sites to be prioritised for recording. A few instances of change too small to be detected by Sentinel-2 were missed, and false positives were caused by registration errors, shadow and movements of sand. This paper shows that the expansion of agricultural and urban areas particularly threatens the survival of archaeological sites, but our extensive online database of archaeological sites and programme of training courses places us in a unique position to make our methods widely available.

scholarly journals The generation of digital elevation models using open source images

2021 ◽  
Author(s):  
Nithin Santosh Kumar
Keyword(s):  
Open Source ◽  
Spatial Resolution ◽  
Digital Elevation Models ◽  
Landsat 8 ◽  
Web Mapping ◽  
Digital Elevation ◽  
Satellite Sensors ◽  
Global Coverage ◽  
Comparable Accuracy ◽  
Sentinel 2

Digital Elevation Models are a representation of Earth’s surface and are used in many areas of research. There are a number of freely available DEMs with near-global coverage, which have elevation accuracies ranging between 10 to 25 m. This project attempts to generate DEMs of comparable accuracy using open source images from satellite sensors and web mapping services. Images from Landsat 8, ASTER, and Sentinel-2 satellites, and from Microsoft’s Bing Maps were used to generate DEMs for a 6.633 km2 area in Oshawa, Canada. It was found that it is key that when combining images from different spaceborne sensors, the spatial resolution should be within 10 m of one another. Additionally, the radiometry of the images, in terms of intensity and contrast, must be similar. The highest accuracies of DEMs had RMSE values of 20.047 m and 20.579 m, when combining images from Sentinel-2 with ASTER and Landsat 8, respectively.

Object-Based Classification of Sentinel-2 Data Using Free and Open-Source Machine Learning and GIS Tools

2021 ◽  
pp. 771-780
Author(s):  
Harpinder Singh ◽  
Ajay Roy ◽  
Shashikant Patel ◽  
Brijendra Pateriya
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Gis Tools ◽  
Object Based ◽  
Sentinel 2

scholarly journals The generation of digital elevation models using open source images

2021 ◽  
Author(s):  
Nithin Santosh Kumar
Keyword(s):  
Open Source ◽  
Spatial Resolution ◽  
Digital Elevation Models ◽  
Landsat 8 ◽  
Web Mapping ◽  
Digital Elevation ◽  
Satellite Sensors ◽  
Global Coverage ◽  
Comparable Accuracy ◽  
Sentinel 2

Digital Elevation Models are a representation of Earth’s surface and are used in many areas of research. There are a number of freely available DEMs with near-global coverage, which have elevation accuracies ranging between 10 to 25 m. This project attempts to generate DEMs of comparable accuracy using open source images from satellite sensors and web mapping services. Images from Landsat 8, ASTER, and Sentinel-2 satellites, and from Microsoft’s Bing Maps were used to generate DEMs for a 6.633 km2 area in Oshawa, Canada. It was found that it is key that when combining images from different spaceborne sensors, the spatial resolution should be within 10 m of one another. Additionally, the radiometry of the images, in terms of intensity and contrast, must be similar. The highest accuracies of DEMs had RMSE values of 20.047 m and 20.579 m, when combining images from Sentinel-2 with ASTER and Landsat 8, respectively.

Open Source

2007 ◽  
Author(s):  
Fadi P. Deek ◽  
James A. M. McHugh
Keyword(s):  
Open Source
Sign in / Sign up

Export Citation Format

Share Document