Environmental observation technology utilizing ground-based imaging devices [Saitama University, Faculty of Engineering]
Utilizing images from smartphones, live cameras, and so on.
- New Proposals for Agriculture, Disaster Prevention, and Land Management - By utilizing images from smartphones, live cameras, and other devices without special filming equipment, it is possible to obtain and infer detailed environmental information from a ground perspective that cannot be captured by satellite observations or drones. By applying deep learning models to image data with location and time information, we can classify and detect objects and understand spatial environmental changes. 【Features】 ■ Continuous image data captured on the ground (proximity sensing data) can be utilized. ■ Images from smartphones, action cameras, in-vehicle cameras, security cameras, live cameras, and images posted on social media are also available. ■ By applying deep learning models, we can grasp detailed spatial environmental changes. 【Expected Areas of Application】 - Understanding growth conditions in agriculture / Estimating flowering and harvest times - Classification of land cover and land use (LCLU: Land Cover Land Use) - Detection of disaster occurrences / Understanding damage conditions, among others.
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[Purpose] - Collection of biological seasonal information related to biological seasonal observations - Detection of disasters caused by rainfall, flooding, and fires - Understanding disaster damage due to the collapse of houses and structures - Changes in land cover due to abandonment of cultivation, deforestation, land development, etc. - Landscape changes - Detection of changes in thermal environments such as heat islands, etc.
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