Visual Question Generation from Remote Sensing Images Using Gemini API

Abstract

Abstract: Visual Question Generation Extracting Information from Remote Sensing Images Remote Sensing Images plays a vital role in understanding and extracting information from aerial and satellite images. Utilizing Bidirectional Encoder Representation from Transformers (BERT) for extracting valuable insights from remote sensing images. Gemini Application Programming Interface(API), and Convolution Neural Networks (CNNs) are used. First, The proposed methodology employs CNN to extract high-level features from remote sensing images, capturing spatial data and generatingquestions. Similarly, the Gemini Application Programming Interface(API) integrates contextual understanding into the question-generation process by providing relevant environmental data. Lastly, BERT functions as a language model in which employees enhance and refine the generated questions by taking into account both the syntax and semantics. Hence, by combining all these techniques we are capable of generating required relevant questions from remote sensing images in an enhanced and efficient way.