Land Cover Classification (Landsat 8)

The land cover is described as the physical components found on the Earth's surface. This covers forests, grasslands, cropland, water surfaces, urban areas, and barren or bare land, among others. The land cover information is fundamental in the analysis of landscape distribution, changes, and human impacts on the environment.

Land cover mapping is done through the use of satellite images, air photos, drone images, and other remote sensing technology used in the identification and classification of various surface types. They help in numerous applications such as urban planning, agriculture, forestry, environmental monitoring, natural resources management, disaster analysis, climate change research, and land use change detection.

The traditional land cover classifications use manual interpretations, rule-based techniques, spectral indices, and other conventional machine learning techniques. Although these methodologies yield useful results, they can be tedious in handling large datasets.

Deep learning technology has offered advanced techniques for land cover classification through automation. Methods that include CNNs, U-Net, ResNet, among others that use semantic segmentation, can detect and classify land cover categories by using the inherent patterns of spatial, spectral, and contextual information contained in remote sensing images. As a result, deep learning models have become capable of discerning visually similar land cover classes.

By utilizing deep learning algorithms with high-resolution satellite images, multispectral images, hyperspectral images, and geospatial workflows, organizations can carry out the automation of large-scale land cover mapping and change detection.