Land Cover Classification (Sentinel-2)

The land cover is the collection of physical materials and features that cover the surface of the Earth. The physical materials include forests, agricultural areas, water, grassland, urban areas, roads, soil, etc. Land cover classification is a crucial step in the process of geospatial analysis due to its ability to give information regarding the distribution and changes of land covers.

Applications of accurate land cover maps include urban planning, agriculture, forestry, management of natural resources, environmental monitoring, disaster assessment, climate change studies, and land cover change detection. Through analysis of remotely sensed data like satellite images and aerial photographs, organizations are able to distinguish the surface classes.

But land cover classification is not an easy task because different land cover types share almost the same spectral properties, while at the same time other aspects like seasonality, shadows, clouds, resolution of the remotely sensed data, and complex land surfaces make the process difficult. Classification approaches used traditionally are cumbersome and less efficient in capturing complex patterns.

The emergence of deep learning has made land cover classification possible through automation by processing large amounts of high-resolution imagery using remote sensing. Deep learning algorithms, such as Convolutional Neural Networks (CNN), U-Net, ResNet, and others involved in semantic segmentation, have the ability to extract features from imagery and identify various land cover classes more accurately.

Deep learning land cover classification has the potential to ensure consistent land cover classification and eliminate manual interpretation. The application of deep learning in land classification can be done through satellite imagery, drone imagery, multispectral imagery, and many other kinds of earth observation data. With the application of GIS and remote sensing technology, deep learning ensures an efficient approach to surface feature characterization and identification of changes in the landscape.