Basics of Map Projection

2014 ◽  
pp. 61-85
Keyword(s):  
Map Projection

A Photographic Map of the Moon

1962 ◽  
Vol 14 ◽  
pp. 113-115
Author(s):  
D. W. G. Arthur ◽  
E. A. Whitaker
Keyword(s):  
Lunar Surface ◽  
The Moon ◽  
Map Projection

The cartography of the lunar surface can be split into two operations which can be carried on quite independently. The first, which is also the most laborious, is the interpretation of the lunar photographs into the symbolism of the map, with the addition of fine details from telescopic sketches. An example of this kind of work is contained in Johann Krieger'sMond Atlaswhich consists of photographic enlargements in which Krieger has sharpened up the detail to accord with his telescopic impressions. Krieger did not go on either to convert the photographic picture into the line symbolism of a map, or to place this picture on any definite map projection.

GIS Technologies in Course of Mathematical Cartography

2017 ◽  
Vol 921 (3) ◽  
pp. 30-35
Author(s):  
N.G. Ivlieva ◽  
V.F. Manukhov
Keyword(s):  
Software Tools ◽  
Digital Models ◽  
Map Projection ◽  
Gis Technologies ◽  
Surface Distortion ◽  
Nodal Points ◽  
Projection Properties ◽  
Special Software

GIS are closely related to mathematical cartography, as they work with spatially coordinated data. Practical course in mathematical cartography should meet the requirements of time and include tasks involving the use of modern GIS technologies. The functionality of GIS packages allow you to easily create maps in a given map projection, draw graticules and measured grids, perform dimensions on maps. This article is devoted to the research of map projection properties on the basis of GIS technologies in a practical course of mathematical cartography. The focus is on visual way to display local and regional distortions on maps. To create lines of equal distortion should use special software tools that allow to build digital models of surface distortion distribution directly on formulas or be interpolated both discretely located nodal points and isolines.

A Manual of Map Projection

1922 ◽  
Vol 12 (3) ◽  
pp. 518
Author(s):  
C. H. Swick ◽  
C. H. Deetz ◽  
O. S. Adams
Keyword(s):  
Map Projection

Elements of Map Projection, with Applications to Map and Chart Construction

1923 ◽  
Vol 61 (3) ◽  
pp. 219
Author(s):  
A. E. Y. ◽  
Charles H. Deetz
Keyword(s):  
Map Projection

An Improved Method for Transforming GPS/INS Attitude to National Map Projection Frame

2015 ◽  
Vol 12 (6) ◽  
pp. 1302-1306 ◽  
Author(s):  
Xiang Shen ◽  
Yongjun Zhang ◽  
Xiao Lu ◽  
Qian Xie ◽  
Qingquan Li
Keyword(s):  
Improved Method ◽  
Map Projection

scholarly journals Automated Bale Mapping Using Machine Learning and Photogrammetry

2021 ◽  
Vol 13 (22) ◽  
pp. 4675
Author(s):  
William Yamada ◽  
Wei Zhao ◽  
Matthew Digman
Keyword(s):  
Lower Cost ◽  
Mean Squared Error ◽  
Spatial Clustering ◽  
Mean Average Precision ◽  
Average Precision ◽  
Data Set ◽  
Map Projection ◽  
Squared Error ◽  
Aerial Vehicle ◽  
The Impact

An automatic method of obtaining geographic coordinates of bales using monovision un-crewed aerial vehicle imagery was developed utilizing a data set of 300 images with a 20-megapixel resolution containing a total of 783 labeled bales of corn stover and soybean stubble. The relative performance of image processing with Otsu’s segmentation, you only look once version three (YOLOv3), and region-based convolutional neural networks was assessed. As a result, the best option in terms of accuracy and speed was determined to be YOLOv3, with 80% precision, 99% recall, 89% F1 score, 97% mean average precision, and a 0.38 s inference time. Next, the impact of using lower-cost cameras was evaluated by reducing image quality to one megapixel. The lower-resolution images resulted in decreased performance, with 79% precision, 97% recall, 88% F1 score, 96% mean average precision, and 0.40 s inference time. Finally, the output of the YOLOv3 trained model, density-based spatial clustering, photogrammetry, and map projection were utilized to predict the geocoordinates of the bales with a root mean squared error of 2.41 m.

scholarly journals Analysis on the Effect of Map Projection System for Area Calculation

2019 ◽  
Vol 0 (2) ◽  
pp. 69
Author(s):  
Yustisi Lumban-Gaol ◽  
Ayu N. Safi’i ◽  
Prayudha Hartanto ◽  
Tia R. N. Rachma
Keyword(s):  
Projection System ◽  
Map Projection
Sign in / Sign up

Export Citation Format

Share Document