Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

Central to the looming paradigm shift toward data-intensive science, machine-learning techniques are becoming increasingly important. In particular, deep learning has proven to be both a major breakthrough and an extremely powerful tool in many fields. Shall we embrace deep learning as the key to everything? Or should we resist a black-box solution? These are controversial issues within the remote-sensing community. In this article, we analyze the challenges of using deep learning for remote-sensing data analysis, review recent advances, and provide resources we hope will make deep learning in remote sensing seem ridiculously simple. More importantly, we encourage remote-sensing scientists to bring their expertise into deep learning and use it as an implicit general model to tackle unprecedented, large-scale, influential challenges, such as climate change and urbanization

Impact of Deepfake Technology on Digital World Authenticity: A Review

Deep fake technology is an emerging technology that creates fake videos by using artificial intelligence (AI) with the facial expression and lips sing effect. Deep fake technology is widely used in different scenarios with different objectives. Deep fake technology is used to make a highly realistic fake video that can be widely used to spread the wrong information or fake news by regarding any celebrity or political leader which is not created by them. Due to the high impact of social media, these fake videos can reach millions of views within an hour and create a negative impact on our society. This technology can be used by criminals to threaten society by making such deep fake (AI) videos. The results suggest that deepfakes are a threat to our celebrities, political system, religious beliefs, and business, they can be controlled by rules and regulations, strict corporate policy and awareness, education, and training to the common internet users. We need to develop a technology that can examine such types of video and be able to differentiate between real and fake video. Government agency also needs to create some policy to regulate such technology so that monitoring and controlling the use of this AI technology can be managed

Analysis of Deep-Fake Technology Impacting Digital World Credibility: A Comprehensive Literature Review

Deep-Fake Technique is a new scientific method that uses Artificial-Intelligince to make fake videos with an affect of facial expressions and coordinated movement of lips. This technology is frequently employed in a variety of contexts with various goals. Deep-Fake technology is being used to generate an extremely realistic fake video that can be widely distributed to promote false information or fake news about any celebrity or leader that was not created by them. Because of the widespread use of social media, these fraudulent videos can garner billions of views in under an hour and have a significant impact on our culture. Deep-Fakes are a threat to our celebrities, democracy, religious views, and commerce, according to the findings, but they can be managed through rules and regulations, strong company policy, and general internet user awareness and education. We need to devise a process for examining such video and distinguishing between actual and fraudulent footage