Multitemporal Cloud Masking in the Google Earth Engine

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چکیده

منابع مشابه

Multitemporal settlement and population mapping from Landsat using Google Earth Engine

As countries become increasingly urbanized, understanding how urban areas are changing within the landscape becomes increasingly important. Urbanized areas are often the strongest indicators of human interaction with the environment, and understanding how urban areas develop through remotely sensed data allows for more sustainable practices. The Google Earth Engine (GEE) leverages cloud computi...

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Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing

Quantifying and monitoring the spatial and temporal dynamics of the global land cover is critical for better understanding many of the Earth's land surface processes. However, the lack of regularly updated, continental-scale, and high spatial resolution (30 m) land cover data limit our ability to better understand the spatial extent and the temporal dynamics of land surface changes. Despite the...

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Scientific Computing in the Cloud with Google App Engine

Cloud Computing as a computing paradigm recently emerged to a topic of high research interest. It has become attractive alternative to traditional computing environments, especially for smaller research groups that can not afford expensive infrastructure. Most of the research regarding scientific computing in the cloud however focused on IaaS cloud providers. Google App Engine is a PaaS cloud f...

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Software Verification in the Google App-Engine Cloud

Software verification often requires a large amount of computing resources. In the last years, cloud services emerged as an inexpensive, flexible, and energy-efficient source of computing power. We have investigated if such cloud resources can be used effectively for verification. We chose the platform-as-a-service offer Google App Engine and ported the open-source verification framework CPAche...

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Investigation of land use changes in Gorganrood catchment using Google Earth Engine platform

The purpose of this study is to investigate landuse changes in Gorganrood basin in 2001, 2010 and 2019. Using Landsat and Product-Modes satellite images, used maps were prepared using the classification method of random forest algorithm in Google Earth Engine. Satellite imagery was classified into eight classes including forest, cropland, shrubland, grassland, wetland, urban, barren, and water....

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ژورنال

عنوان ژورنال: Remote Sensing

سال: 2018

ISSN: 2072-4292

DOI: 10.3390/rs10071079