Challenges remain in data retrieval and mapping development when compared to other land objects, despite the dependability of remote sensing technology in assessing land use and land cover distribution. The Fuzzy ARTMAP model is an ART-based neural network (FAM). The goal of this study is to use satellite imagery and advanced geomatics methods to create accurate digital maps. Field coordinates were matched with satellite imagery to increase spatial accuracy as part of a series of geomatic correction processes that also included geographic correction using GPS coordinates. The Fuzzy ARTMAP method was used to assess the quality of the data classification. This algorithm has already shown its efficacy in distinguishing between Farmlands, Urban Structures, and Arid Lands. The algorithm's kappa value of 0.83 and overall accuracy of 89% indicate a very reliable data classification process. Further, extensive evaluations of the accuracy of geographical measures were carried out, specifically focussing on areas and distances. The findings indicated an overall error of 0.73% for distances and a mere 0.03% for areas. These results indicate that the methods used to get very high degrees of spatial accuracy while simultaneously decreasing spatial deviations work. The findings show that state-of-the-art georectification methods coupled with current classification algorithms may significantly enhance digital map quality, making them more reliable for applications such as environmental change monitoring, urban planning, and natural resource management. The research reinforces the importance of integrating low- and medium-resolution satellite imagery with modern geomatics techniques to achieve high-resolution digital maps.