Bird's-Eye View to Street-View: A Survey

• URL: http://arxiv.org/abs/2405.08961v1
• Date: Tue, 14 May 2024 21:01:12 GMT
• Title: Bird's-Eye View to Street-View: A Survey
• Authors: Khawlah Bajbaa, Muhammad Usman, Saeed Anwar, Ibrahim Radwan, Abdul Bais,
• Abstract summary: We screened 20 recent research papers to review the state-of-the-art of how street-view images are synthesized from their corresponding satellite counterparts. Main findings are: (i) novel deep learning techniques are required for synthesizing more realistic and accurate street-view images; (ii) more datasets need to be collected for public usage; and (iii) more specific evaluation metrics need to be investigated for evaluating the generated images appropriately.
• Score: 16.90516098120805
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, street view imagery has grown to become one of the most important sources of geospatial data collection and urban analytics, which facilitates generating meaningful insights and assisting in decision-making. Synthesizing a street-view image from its corresponding satellite image is a challenging task due to the significant differences in appearance and viewpoint between the two domains. In this study, we screened 20 recent research papers to provide a thorough review of the state-of-the-art of how street-view images are synthesized from their corresponding satellite counterparts. The main findings are: (i) novel deep learning techniques are required for synthesizing more realistic and accurate street-view images; (ii) more datasets need to be collected for public usage; and (iii) more specific evaluation metrics need to be investigated for evaluating the generated images appropriately. We conclude that, due to applying outdated deep learning techniques, the recent literature failed to generate detailed and diverse street-view images.

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