6th International Conference on ICT for Intelligent Systems (ICTIS – 2022)  Application of Machine Learning in Mineral Mapping using Remote Sensing 

[Audio] Welcome to the Presentation on Application of Machine Learning in Mineral Mapping using Remote Sensing.

[Audio] We will cover Study Background Problem Statement Framework Conclusion References.

[Audio] There are two main facets to this Research : Machine Learning & Remote Sensing The Machine Learning is an effective approach towards acquiring patterns. Remote Sensing is the field of study the imageries captured by the satellite may have a resolution of varied types including; spatial, spectral and temporal imagery.

Introduction. Remote Sensing is one popular field that can be employed with the ML concepts With the raw spatial data captured by the sensors like LANDSAT,MODIS meaningful insights can be drawn adhering to the specifics of the arena. The images are captured in the form of electromagnetic waves The paper intends to showcase the relevance of machine learning concepts pertaining to a specific area of application in Geosciences with the identification of potential mineral mapping areas as the key objective. The Sensor Captures Surface Image in the form of spectral Signatures The Surface is analysed for the common data points and categorised based on the application area Larger Area can be covered The spectral imagery can be collected using multispectral or hyperspectral sensors the difference being in the number of bands present in the images captured.

Mineral Analysis. Scope. Mineral Prospectivity Modeling & Mapping.

Introduction. Several potential regions with mineral deposits have been identified on earth’s surface area and research is being carried out to explore the regions with capacitive mineral bank digging below the surface regions. Even though site mapping and physical scrutiny of geological assets are more effective, the aerial and satellite capture employing remote sensing arena facilitates insight into wider geographical locations as well as capture required data in the format required, especially in the era where technology has become the primary source of aiding solution to the environmental crisis. Remote sensing can be used to collect valuable information in the form of satellite imagery that can be supplemented with Machine Learning processing to draw meaningful insights into discovering potential areas of mineral mapping . The proposed Solution accustoms to develop and design of feasible solution for Multispectral analysis and Geological Mapping employing Earth Observation remote sensing data and Machine Learning Algorithms.

Research Considerations. EMR: Electromagnetic Radiation Atmospheric Scattering Atmospheric Corrections Spectral Signatures Geospatial Data Analysis and Machine Learning Accuracy Assessment of Land Cover.