Detection of Land-Use Land-Cover Changes Using Machine Learning Systems

Abstract

A deeper comprehension of land-use and land-cover change (LULCC) is beneficial for environmental modeling and assessment, natural resource management, and agricultural output management. However, because of the processing of massive amounts of historical and current data, real-time interaction of scenario data, and geographical environmental data, LULCC detection and modeling is a challenging, data-driven process in the realm of remote sensing. This study provides an overview of the topic and highlights its significance with respect to global land transitions. It examines both historical and contemporary techniques for precisely identifying LULC change. The need for more accurate and automated solutions grows as artificial intelligence (AI) becomes more prevalent. In order to identify LULCC, this article suggests a number of machine learning techniques. Machine learning models trained to leverage spectral resolution, digital change-based detection, and picture regression to identify and classify LULC changes across time. By enabling the extraction of useful information from sizable and intricate datasets, these models enhance the accuracy and speed of change detection. The study's conclusions indicate that the application of machine learning methods for LULC change detection is a major development in the precise and effective monitoring of land changes. By assessing existing methods, this study contributes to the greater goal of environmentally responsible land management. More studies and real-world applications in environmental science, urban and regional planning, natural resource management, etc. can use the same analytical approach.