This study (Rana et al 2025) of land features in Vehari, Pakistan, uses remote sensing satellite imagery and machine learning to classify and analyse changes in land cover and land use between 1990 and 2025. It uses data from multispectral satellite images along with corresponding ground truth data, for training and validation of ML models. The study concludes that ML techniques, when integrated with remote sensing data, provide an effective means for monitoring and analysing land cover and land use.

Various ML algorithms, including Random Forest, Support Vector Machines, and Convolutional Neural Networks, are implemented and compared for their accuracy in classifying land features such as agriculture fields, water bodies, urban areas, and barren land. Preprocessing techniques such as image normalization, feature extraction, and dimensionality reduction are applied to enhance the performance of the models. The results reveal significant changes in land cover and land use patterns over the study period, with urban expansion and agricultural intensification being notable trends. These findings are crucial for urban planning, agricultural management, and environmental conservation efforts in the region.

This work shows how to monitor and analyses changes in land cover and land use by combining machine learning techniques with data from remote sensing. The knowledge acquired is priceless for making well-informed decisions in environmental preservation, agricultural management, and urban planning, which supports District Vehari sustainable growth. Subsequent studies might concentrate on enhancing classification accuracy through the incorporation of other data sources, like high-resolution temporal data, and investigating sophisticated machine learning methods. More research could broaden the focus to include socioeconomic variables impacting changes in land use, offering a more comprehensive knowledge of the dynamics of the area.