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The rod, perch, or pole (sometimes also lug) is a surveyor's tool [1] and unit of length of various historical definitions. In British imperial and US customary units, it is defined as 16 + 1 ⁄ 2 feet, equal to exactly 1 ⁄ 320 of a mile, or 5 + 1 ⁄ 2 yards (a quarter of a surveyor's chain), and is exactly 5.0292 meters.
The projection found on these maps, dating to 1511, was stated by John Snyder in 1987 to be the same projection as Mercator's. [6] However, given the geometry of a sundial, these maps may well have been based on the similar central cylindrical projection, a limiting case of the gnomonic projection, which is the basis for a sundial. Snyder ...
The Dymaxion map projection, also called the Fuller projection, is a kind of polyhedral map projection of the Earth's surface onto the unfolded net of an icosahedron. The resulting map is heavily interrupted in order to reduce shape and size distortion compared to other world maps , but the interruptions are chosen to lie in the ocean.
A landmass, or land mass, is a large region or area of land that is in one piece and not noticeably broken up by oceans. [1] [2] The term is often used to refer to lands surrounded by an ocean or sea, such as a continent or a large island. [3] [4] In the field of geology, a landmass is a defined section of continental crust extending above sea ...
6.1 Mass- or volume flow measurement. 6.2 Speed or velocity ... An instrument for detecting net charges, ... Spectroscope is an important tool used by physicists.
Pangaea, the supercontinent that existed about 200 million years ago and combined most of dry land on Earth into one giant landmass (according to the BBC), is well known.
Roll – a U.K. unit of mass for butter and cheese [7]: 46 equal to 24 oz (680 g) [7]: 52 Room – a U.K. unit of mass of coal equivalent to 15,680 lb (7,110 kg) [7]: 52 Sarpler; Ship load; Slug; Talent – a unit of mass in the tens of kg; Tank; Tod; Truss – a unit of mass used to describe tight bundle of hay or straw.
A supervised classification is a system of classification in which the user builds a series of randomly generated training datasets or spectral signatures representing different land-use and land-cover (LULC) classes and applies these datasets in machine learning models to predict and spatially classify LULC patterns and evaluate classification accuracies.