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Some of the free software mentioned here does not have detailed maps (or maps at all) or the ability to follow streets or type in street names (no geocoding). However, in many cases, it is also that which makes the program free (and sometimes open source [ 1 ] ), avoid the need of an Internet connection, [ 2 ] [ 3 ] [ 4 ] and make it very ...
Magicplan is a mobile application developed by Sensopia Inc. [1] The app enables users to create 2D and 3D floor plans in real time by using the camera of a smartphone or tablet alongside augmented reality (AR) technology. [2]
Online land planning is a collaborative process in which sustainable development practices and design professionals from across the world are networked to provide advice and solutions on urban design and land planning issues.
Image comparing the hectare (the small blue area at lower left) to other units. The entire yellow square is one square mile.. The hectare (/ ˈ h ɛ k t ɛər,-t ɑːr /; SI symbol: ha) is a non-SI metric unit of area equal to a square with 100-metre sides (1 hm 2), that is, 10,000 square metres (10,000 m 2), and is primarily used in the measurement of land.
Plan of the health area of the Universidad de Antioquia, Medellín, Colombia [a] Today its size varies between countries: In Argentina it is a hectare, 10,000 m 2. In most Central American countries it is about 7,000 m 2 (1.7 acres), varying between countries. In Belize it is 8,353 m 2 (2.064 acres). In Nicaragua it is 7,042.25 m 2 (1.74018 acres).
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On average, a global hectare can be produced in the area of a standard hectare. A hectare (/ ˈ h ɛ k t ɛər /; symbol ha) is a unit of area equal to 10,000 square metres (107,639 sq ft) (a square 100 metres on each side or 328 feet on each side), 2.471 acres, 0.01 square kilometers, 0.00386102 square miles, or one square hectometre (100 metres squared).
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.