Supervised Classification Model Using Google Earth Engine Development Environment for Wasit Governorate

Abstract

Abstract As a result of the advancements that have occurred in the technical field of geomatics, particularly after the development of developmental programming environments, they have become the most important machine for conducting image analyses of satellite data, creating and modifying spatial analysis tools, and performing large data analyses at a fast rate without the need for high-end specifications on the personal computer. This study has several objectives, including the definition and popularization of the use of the power of Google Earth Engine (GEE) in the speed of conducting spatial analyzes, which cite by conducting a classification at the level of a governorate and obtaining results with speed and relatively good quality. By using the Google Earth Engine (GEE) platform and through Javascript programming language, a classification of the land cover of Wasit Governorate, Iraq was created under the supervision of a satellite image (Landsat 8) by creating a training sample, Google Maps’ High Resolution basemap imagery was used to create this map to identify classes of landcover (water, bare soil, vegetation, and urban). Each source pixel is assigned to one of the previously mentioned classes. Then to create a land cover map of the region using the Statistical Machine Intelligence and Learning Engine (SMILE) classifier from the JAVA library, which is used by Google Earth Engine (GEE) to implement these algorithms. The result is an array of pixels (raster data). The pixel value represents the class that was previously determined by the samples.