Land Cover Classification

Land cover classification refers to the identification and mapping of the materials and features found on the Earth's surface, such as vegetation, forested areas, agricultural land, water features, urban zones, exposed soil, among others. Knowledge of land cover is necessary for applications like urban planning, agriculture, environmental monitoring, natural resources management, disaster assessment, and climate research, among others.

Even though there are many freely accessible land cover databases that provide important geographical information, their limitations can prevent their use for project-specific applications. In some cases, the limitation might be regarding the spatial resolution, temporal coverage, classification level, geographical scope, or availability of data on certain dates or geographical locations. The creation of land cover datasets for project-specific applications requires extensive manual processing and labeling, which can consume much time and effort.

The process of creating project-specific land cover datasets becomes significantly easier using deep learning. This technology makes it possible to automate the whole process of land cover classification. Deep learning models make use of computer vision and machine learning techniques for classifying land cover using satellite imagery and other remotely sensed datasets.

The algorithm is capable of processing vast amounts of imagery and learning complicated patterns in both space and spectra that are linked with various surfaces. Thus, it allows classifying the data more rapidly and consistently and, at the same time, saves the manual labor that is needed for traditional methods. Based on the availability of imagery and data to train the network, one can customize the output land cover maps depending on the geographical location, classification scheme, and period of time chosen.

An automated land cover classification is especially valuable for monitoring changes that occur in such objects as urban expansion, vegetation, agriculture, forest, water, and other environmental objects. Due to the timeliness and scalability of the geospatial data obtained from the classification, it can enhance decision-making and optimize GIS and remote sensing processes.

This technology provides an easy way to turn vast amounts of imagery collected through Earth observations into useful land cover information and, thus, create customized datasets.