Google Earth Engine for Landuse Mapping

Google Earth Engine (GEE) is a cloud-based platform that provides access to a vast archive of remote sensing data and tools for analyzing and visualizing this data. GEE is particularly useful for land use mapping, as it allows users to process and analyze large amounts of satellite imagery in a fast and efficient manner.

Land use mapping is the process of categorizing the different types of land use within a given geographic area, such as urban areas, agricultural land, forests, water bodies, and barren land. This information is critical for a range of applications, from urban planning and environmental management to disaster response and climate change monitoring.

GEE provides a suite of tools for land use mapping, including algorithms for image classification and machine learning, as well as tools for data visualization and analysis. The platform includes a large collection of remote sensing datasets, including satellite imagery, elevation data, and climate data, which can be accessed and analyzed in a single platform.

One of the key features of GEE is its ability to process large amounts of data quickly and efficiently. The platform uses Google’s cloud infrastructure to parallelize and distribute the processing of data across multiple machines, allowing users to analyze and visualize large datasets in a matter of minutes or hours.

GEE also includes a range of tools for image classification, which is a key component of land use mapping. Image classification is the process of categorizing the different types of land use based on the spectral characteristics of satellite imagery. GEE includes a range of supervised and unsupervised classification algorithms, including maximum likelihood, random forest, and support vector machines.

Supervised classification algorithms require the user to provide training data, which consists of samples of different land use types within the study area. The algorithm then uses this training data to classify the entire image. Unsupervised classification algorithms, on the other hand, do not require any training data and instead group pixels based on their spectral similarity.

GEE also includes a range of tools for machine learning, which can be used to improve the accuracy of land use mapping. Machine learning algorithms, such as deep learning, can be used to automatically extract features from satellite imagery and classify different land use types based on these features. These algorithms can also be used to improve the accuracy of supervised classification algorithms by identifying and correcting misclassifications.

In addition to classification algorithms, GEE includes a range of tools for data visualization and analysis. The platform includes a range of tools for visualizing satellite imagery, including the ability to view different bands and spectral indices, such as the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI).

GEE also includes a range of tools for data analysis, including the ability to calculate vegetation indices, such as NDVI and EVI, and to extract statistics, such as mean and maximum values, from satellite imagery. These tools can be used to identify areas of vegetation, calculate vegetation cover, and monitor changes in vegetation over time.