Swimming Pool Detection via Deep Learning and Remote Sensing
The Motivation: The island of Cyprus is known for its numerous swimming pools, but keeping track of their locations and conditions can be challenging. Our team has developed a solution that uses deep learning techniques and high-resolution remote sensing imagery to detect swimming pools on the island.
The Method: Using high spatial resolution satellite imagery with a resolution of 0.3-0.5 meters/pixel, we trained a deep learning model to detect swimming pools in remote sensing images. Our model achieved impressive precision and recall values of 80% and 85%, respectively. We fine-tuned the model using transfer learning techniques, allowing it to identify swimming pools in high-resolution remote sensing images.
The Results: After extensive testing and evaluation, we produced a map of all the swimming pools on the island of Cyprus as a GIS layer. Our model detected 58,577 swimming pools, providing a comprehensive database of swimming pools on the island. The implications of this project are significant. Local authorities can use this data to regulate the construction and maintenance of swimming pools, while tourism activities can benefit from up-to-date information on swimming pool locations.
The Importance: Our model’s high precision and recall values demonstrate the effectiveness of deep learning techniques in detecting complex patterns in high-resolution remote sensing images. This project opens new possibilities for remote sensing and computer vision research, with potential applications in a range of industries.
The Perspective: We believe that this project can inspire others to leverage the power of deep learning and remote sensing to solve environmental and societal challenges. As we continue to develop and refine these techniques, the potential for remote sensing to inform sustainable land use, conservation efforts, and natural resource management is vast. Our team remains committed to using advanced technologies to address critical issues and create positive change. We are grateful for our partners’ and supporters’ contributions, and we look forward to future collaborations to drive innovation and progress.