Detection and Prediction of Land Use and Land Cover Changes Using Deep Learning

Abstract

Our planet Earth is endowed with plenty of natural resources that sustain life for millennia. Nature provides us with five elements such as water, land, air, sky, and fire. Of these, land is an important element in nature to analyze the change in Earth. The land is covered with trees, crops, wetlands, water, and buildings. However, land use/land cover changes contribute to the major environmental challenges in various parts of the world. Globally, there has been an increase in the exploitation of land by humans due to industrialization and globalization, and it poses a major threat to the environment. Therefore, analyzing the land use/land cover (LU/LC) change has become an important and crucial issue to be solved all around the world. Identifying the physical aspect of the Earth’s surface (Land cover) as well as how we exploit the land (Land use) is a challenging problem in environmental monitoring and many other subdomains. This can be done through field surveys or analyzing satellite images (Remote Sensing). While carrying out field surveys is more comprehensive and authoritative, it is an expensive project and mostly takes a long time to update. So to overcome these hurdles, we have proposed a deep learning-based approach to analyze the historical land use and land cover changes in a particular region from satellite images and use it to predict land use and land cover in the future. This prediction will be very useful for urban planning and environmental management of rapidly growing cities. The use of Geo-informatics is immensely helpful in accomplishing this task saving a huge amount of time and energy.