Merging GIS and Machine Learning Techniques: A Paper Review

Abstract

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.

Using UAVs and Machine Learning for Nothofagus alessandrii Species Identification in Mediterranean Forests

“…Moreover, UAVs have been combined with Geographic Information Systems (GIS) to gather data on the Earth's surface and atmosphere. GIS data provide spatial information on Earth's features, along with their attributes and spatial relationships, and the integration of machine learning techniques in GIS analysis has shown promise in enhancing the speed, accuracy, automation, and repeatability of data processing [13].…”

Landslide Movement of Bendungan District Trenggalek Using an Artificial Neural Network

“…Advancement in computation and geospatial technologies has been proven supportive to the efforts for landslide modeling (Ekeanyanwu et al, 2022). There have been varying machine learning (ML) algorithms used in predicting landslide risks such as artificial neural network (ANN), logistics regression, random forest classification and regression, and support vector machine algorithms (Alqadhi et al, 2021;Nhu et al, 2020).…”