Alongside being a great way to search and navigate to places, Google maps also provides a amount of area and length estimation functions. These allow for the amelioration of web applications that allow the user to quantum distances and land features online. Such applications replace the need for specialized and expensive map production services as well as allowing the user categorically and speedily originate maps and measurements as and when they are needed.
As we know, Google maps relies on satellite imagery and so its accuracy is dependent on its resolution. Resolution is essentially the size of the smallest object that can be detected and meaningfully identified in the image. Thus, an image where we can read a amount plate of a car is of a higher resolution than one in which we can only see the shape of the car itself. And so it follows that the better the resolution the more inherent better and more definite measurements can be made on it. Commonly speaking urban areas have higher resolution or accuracy than rural or remote areas as do European countries over the Us. Resolution can vary in the middle of a lousy 50 meters at the low end up to an impressive 0.15 meters at the high end. Increasingly this accuracy is improving.
Of policy the test of a pudding is in the eating and so lets quantum some objects of a known size and length. Specifically lets use the farm-file.com application to quantum the Pentagon Usa, the Colosseum Italy and Manchester United's football pitch in the Uk. These represent, respectively, an irregular, elliptical and quarterly shape. Additionally they rehearse typical feature sizes for large, medium and small farms. A summary is given here:
- Pentagon (Central Plaza) Actual construction Area = 116,000 quadrate meters Measured = 116,100 quadrate meters Accuracy = 99.91%.
- Colosseum, Actual construction Area = 21,000 quadrate meters Measured = 21,167 quadrate meters Accuracy = 99.21%.
- Manchester United Football Pitch Area = 7,140 quadrate meters Measured = 7,100 quadrate meters Accuracy = 99.44%.
We can therefore see that this particular application is extremely definite in measuring land areas. In fact, accuracies up to 99% can be predicted on land areas from a football pitch upwards. This coupled with the positive convenience of production virtual, definite measurements of large objects against physically measuring them commends these applications to land managers everywhere.
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