GIS (Geographic Information Systems) data showcase locations of earth observations or features, their associated attributes and spatial relationships that exist between such observations. Analysis of GIS data varies widely and may include some modeling and predictions which are usually computing-intensive and complicated, especially, when large datasets are involved. With advancement in computing technologies, techniques such as Machine learning (ML) are being suggested as a potential game changer in the analysis of GIS data because of their comparative speed, accuracy, automation, and repeatability. Perhaps, the greatest benefit of using both GIS and ML is the ability to transfer results from one database to another. GIS and ML tools have been used extensively in medicine, urban development, and environmental modeling such as landslide susceptibility prediction (LSP). There is also the problem of data loss during conversion between GIS systems in medicine, while in geotechnical areas such as erosion and flood prediction, lack of data and variability in soil has limited the use of GIS and ML techniques. This paper gives an overview of the current ML methods that have been incorporated into the spatial analysis of data obtained from GIS tools for LSP, health, and urban development. The use of Supervised Machine Learning (SML) algorithms such as decision trees, SVM, KNN, and perceptron including Unsupervised Machine Learning algorithms such as k-means, elbow algorithms, and hierarchal algorithm have been discussed. Their benefits, as well as their shortcomings as studied by several researchers have been elucidated in this review. Finally, this review also discusses future optimization techniques.

In this study, the basic engineering and geotechnical qualities of poor subgrade soils were evaluated. Woven geotextile was used to stabilize the soil and to address the lime problem, enhancing its strength and mechanical properties. This is considered of significant importance in civil engineering works. Subgrade soils, its properties like plasticity and strength are essential to the design of pavement structures and any road construction. Experiments were conducted to investigate the application of geotextile on lime stabilized lateritic soils under unsoaked conditions. Geotechnical experiments were conducted to determine Grain size analysis, Atterberg, compaction and California bearing ratio test. CBR tests were done by placing the geotextile at varying depths under unsoaked conditions to determine the soil's bearing capacity. The result shows that the strength of lateritic soil is visibly increased by introducing geotextiles at different layers in the soil. It is found that geotextile placed at one-half the distance from the base showed higher CBR value by comparison with layers one-fourth and 1/4&3/4 distances from the base. The strength of the laterites was improved to thereabout 50% of its original strength without any stabilizer. Geotextile requires minimal maintenance, corrosion resistance, no threat to human health and increases the service life of road pavement. Geotextiles should be considered when dealing with the problems with lime and as a modernized form of improving road construction on poor laterites.