Study On Data Science And Machine LearningTechniques For Geographic Information Systems(GIS)

Machine learning refers to a set of "data-performance" techniques and algorithms that predict, classify, and cluster data automatically. Machine learning has a key role in solving geospatial problems that performs a wide range of tasks including classification of satellite images, identification of spatial patterns, and multivariate predictions. In addition to traditional machine learning (ML) techniques, ArcGIS software has a subset of machine learning techniques that are spatial in nature. These spatial methods directly include a geographical perspective in their calculations, they can create a deeper understanding in us. This spatial component takes the form of vector processing, density, connectivity, spatial spatial distribution, and proximity. Both of these techniques and machine learning (machine learning) techniques, traditional and inherently spatial, can play an important role in solving spatial problems, and ArcGIS supports the use of these techniques in several ways. Remote Sensing (RS) is one of the most important land surface observation methods and plays a fundamental role in many fields including climate change, land and resource survey, disaster monitoring, crop growth monitoring, urbanization, and changes in land use. It plays the role of land cover. In recent decades, rapid progress in satellite and sensor technology has led to a significant growth in the amount of data from a variety of platforms and sensors, ushering us into the era of Remote Sensing Big Data (RSBD). Remote sensing big data helps us to expand the scope of remote sensing applications and presents new challenges for its processing and analysis. On the other hand, advances in RS are accompanied by advances in information technology and artificial intelligence. RS scientists have introduced machine learning algorithms (such as artificial neural networks, support vector machines, and random forests) to improve the performance of RS applications. In particular, in recent years, there has been a huge progress in artificial intelligence technology as Deep Learning, which has wide applications in many fields. With the great ability of hierarchical learning and representative and discriminative features, deep learning has started a new wave of studies in the processing and analysis of large remote sensing data.

کلیدواژه ها:

Data Science ، Machine Learning ، Geographic Information Systems ، GIS.