Mining engineers are frequently faced with problems of deciding on the best option to access the ore bodylocation and value of the ore body. The main problem occurred in those farther areas where no facilities are available for life. Many technical and financial risks are also involved. In that case, special experts and equipment are also needed to accessand locate the mineral. Moreover, a large amount of data is required to assist with this process. In developing countries, due to lack of modern technologies, it is difficult and costly to mine these precious natural resources.To provide a simple and low cost solution, we propose an IT based framework for this problem. In our proposed research, we use special feature of satellite images of hilly areas to classify the areas that possibly contain minerals and metals.The proposed system uses Mean shift algorithm to identify various features of colored images. Markov logic is used to classify each color according to their weights.The initial experiments show that the results are positive.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.