The detection of changes in land use and land cover is a crucial task in the monitoring and management of natural resources. In this instance, we differentiate two common approaches to detect changes over time: Convolutional Neural Network (CNN) and Time-Weighted Dynamic Time Warping (TWDTW) algorithms. We assessed the attainment of both methods on two real data sets of MODIS satellite imagery and compared their accuracy over days. According to findings, while both methods can accurately detect changes, TWDTW outperforms CNN in detecting gradual changes that take place over long periods of time. We also examined the computational efficiency of both methods and found that CNN is faster than TWDTW, making it more suitable for real-time applications. Our results provide insight into the strengths and limitations of these two methods for the ability to comprehend trends. Combining these two methods can provide a more comprehensive approach to accurately detect alterations in land usage over time. The results of this instance may influence the selection of appropriate methods for specific applications and provide a basis for future research in this area.