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Transversal Mercator projections
Published in Martin Vermeer, Antti Rasila, Map of the World, 2019
Also in the new solution, different choices have been made for large-scale and small-scale maps. For small-scale maps, the UTM projection is chosen, which is also globally extensively used — e.g., in the United States. It differs from Gauss–Krüger firstly in the scale on the central meridian not being 1.0000 but 0.9996. Compared to Equation 6.1 on page 67 we thus have μ=−0.0004+12.29⋅10−15m−2⋅(E−E0)2. The other relevant difference is that the UTM system is global and the whole surface of the Earth has been divided into 60 projection zones, each 6° wide. The numbering of the zones starts at the date line and runs Eastward: zone 1 is 180°W – 172°W, zone 31 is thus 0° – 6°E, and zone 35 is 24°E – 30°E, with a central meridian of 27°.
Methods - Mechanics of Well-Log Mapping
Published in Garry W. Rowe, Sylvia J. Dulaney, Building and Using a Groundwater Database, 2018
Garry W. Rowe, Sylvia J. Dulaney
The Universal Transverse Mercator (UTM) system is a regular grid system, unlike longitude and latitude, because it is based on ground distances rather than angular measurements. While this system has the advantage of being a grid system its base unit is the meter, which is not commonly used in most land surveying and land descriptions in the U.S. In addition, the different zones that comprise the system are delimited by meridians of longitude which do not account for political boundaries such as county lines. This makes the system somewhat inconvenient for local database developers and users who must remain cognizant of their political neighbors.
Position
Published in W. Schofield, M. Breach, Engineering Surveying, 2007
The Universal Transverse Mercator Projection (UTM) is a worldwide system of transverse Mercator projections. It comprises 60 zones, each 6° wide in longitude, with central meridians at 3°, 9°, etc. The zones are numbered from 1 to 60, starting with 180° to 174° W as zone 1 and proceeding eastwards to zone 60. Therefore the central meridian (CM) of zone n is given by CM = 6n° – 183°. In latitude, the UTM system extends from 84° N to 80° S, with the polar caps covered by a polar stereographic projection.
Evaluation of temporal compositing algorithms for annual land cover classification using Landsat time series data
Published in International Journal of Digital Earth, 2023
Xichen Meng, Shuai Xie, Lin Sun, Liangyun Liu, Yilong Han
Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) Surface Reflectance (SR) products provided by USGS were used in this study. The data used in the study are level-2 products of Landsat satellite data. The method of generating the level-2 products is different between Landsat-7 and Landsat-8. Landsat-7 ETM + SR is generated using the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) (Masek et al. 2006), while Landsat-8 OLI SR is generated using Landsat Surface Reflectance Code (LaSRC) (Melaas et al. 2016). Both the satellite data adopted the Universal Transverse Mercator (UTM) projection coordinate system. Six bands for Landsat-7 and −8 were selected including Blue, Green, Red, NIR, SWIR-1, and SWIR-2. In addition, the pixel assessment band indicating the mask of cloud, cloud shadow, snow, and clear sky was also used. All selected bands have the spatial resolution of 30 m, however, Landsat-8 have the narrower wavelength range than Landsat-7 for the same spectral band. Both Landsat-7 and Landsat-8 have a revisit period of 16 days, and single sensor has only two images a month. This means up to two observations can be available for the same pixel in the same month (if there is cloud cover, only one or no value could be available), making monthly synthesis unstable. Therefore, images from two satellites were selected for compositing in order to increase the number of images within each month (i.e. shortening the revisit period to 8 days), which is beneficial for characterizing the temporally dynamic land cover types, such as vegetation and crops. It should also be noted that the Landsat-7 ETM + data experienced a malfunction in its scan line corrector (SLC) since 2003, resulting in missing data strips in the images acquired thereafter.
A data-driven lane-changing behavior detection system based on sequence learning
Published in Transportmetrica B: Transport Dynamics, 2022
Jun Gao, Yi Lu Murphey, Jiangang Yi, Honghui Zhu
Vehicle trajectory curvature contains unique dynamic characteristics for driving maneuvers recognition and more robust to GPS noise (Wu, Chiu, and Liu 2018). It is reasonable, because when a driver makes a lane change, the curvature will be much smoother than turning but have more variation than driving straight. Firstly, the GPS coordinates are converted to the universal transverse Mercator (UTM) coordinates, and the UTM is a type of plane rectangular coordinate system. The conversion algorithm can be found in (Iliffe and Lott 2008). The UTM coordinates of the vehicle at the driving distance is parameterized as and the UTM coordinates within observation window are denoted as . Secondly, in order to reduce the impact of noise points, a polynomial fitting is utilized to fit the UTM coordinates in each observation window. Polynomial fitting from an appropriate historical data group has been proven useful in trajectory prediction (Zhou et al. 2017), and lane/lane-changing detection (Neven et al. 2018; Yoo, Yang, and Sohn 2013; Papadimitriou and Tomizuka 2003). Specifically, a polynomial function is used to fit and another polynomial function is adopted to fit , where each coordinate pair is corresponding to the distance measure . Thirdly, based on the two polynomial fitting functions, the curvature of the driving trajectory at time can be calculated using the equation below, The feature vectors of vehicle heading, is generated by mapping it from its original range to the range within the window size , then the smoothing operation mentioned in (Gao, Murphey, and Zhu 2019) is applied to smooth the data. In addition, a median filter is applied to smooth the speed data to generate the feature vectors . By appending trajectory curvature with vehicle heading and speed , these 3-dimensional features are then arranged as the vehicle dynamics features.