The American Society for Photogrammetry and Remote Sensing (ASPRS) is an American learned society devoted to photogrammetry and remote sensing. It is the United States' member organization of the International Society for Photogrammetry and Remote Sensing. Founded in 1934 as American Society of Photogrammetry and renamed in 1985, the ASPRS is a scientific association serving over 7,000 professional members around the world. As a professional body with oversight of specialists in the arts of imagery exploitation and photographic cartography.Its official journal is Photogrammetric Engineering & Remote Sensing (PE&RS), known as Photogrammetric Engineering between 1937 and 1975.
Lake ice, as part of the Essential Climate Variable (ECV) lakes, is an important indicator to monitor climate change and global warming. The spatio-temporal extent of lake ice cover, along with the timings of key phenological events such as freeze-up and break-up, provide important cues about the local and global climate. We present a lake ice monitoring system based on the automatic analysis of Sentinel-1 Synthetic Aperture Radar (SAR) data with a deep neural network. In previous studies that used optical satellite imagery for lake ice monitoring, frequent cloud cover was a main limiting factor, which we overcome thanks to the ability of microwave sensors to penetrate clouds and observe the lakes regardless of the weather and illumination conditions. We cast ice detection as a two class (frozen, non-frozen) semantic segmentation problem and solve it using a state-of-the-art deep convolutional network (CNN). We report results on two winters ( 2016 - 17 and 2017 - 18 ) and three alpine lakes in Switzerland. The proposed model reaches mean Intersection-over-Union (mIoU) scores >90% on average, and >84% even for the most difficult lake. Additionally, we perform cross-validation tests and show that our algorithm generalises well across unseen lakes and winters.
Abstract. The increasement of Urban 3D model precision and data quantity puts forward higher requirements for real-time rendering of digital city model. Improving the organization, management and scheduling of 3D model data in 3D digital city can improve the rendering effect and efficiency. This paper takes the complexity of urban models into account, proposes a Quadtree construction and scheduling rendering method for Urban 3D model based on weight. Divide Urban 3D model into different rendering weights according to certain rules, perform Quadtree construction and schedule rendering according to different rendering weights. Also proposed an algorithm for extracting bounding box extraction based on model drawing primitives to generate LOD model automatically. Using the algorithm proposed in this paper, developed a 3D urban planning&management software, the practice has showed the algorithm is efficient and feasible, the render frame rate of big scene and small scene are both stable at around 25 frames.
