Classification of Vegetation Areas Using LiDAR Images and AI
Journal of Computer Science Applications and Information Technology
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Many states have limited amount of drinking water which becomes scarcer every year due to changing climate and growth. To manage their water resources wisely they encourage their residence to replace the grass on their loans with xeriscaping. Thus, they pay the residence to take of the grass which demands a great deal of water and replace it with gravel or desert plants demanding very little watering, or no more watering than the rainfall. The amount of money paying the residence to take of the grass and other water consuming vegetation is considerable often in the order of tens of thousands of dollars. Often, property owners take of the grass, take the money, and later on they change their mind or sell the property to another person and they reinstall the grass in the property. In such a case the authorities must take the money back. In order to automate the process of who maintains a loan with grass and who does not, an unmanned aerial vehicle with LIDAR is being used to automatically recognize grass loans and vegetation areas, as well as xeriscaping for each address in a city. In this research paper we show the digital image processing and AI algorithm used on LIDAR images in order to classify a building as having a grass loan and vegetation, or having a certain percentage of vegetation, and the rest xeriscaping, or all xeriscaping.
Machine vision; Computer vision; Machine intelligence; Image segmentation
Classification of Vegetation Areas Using LiDAR Images and AI.
Journal of Computer Science Applications and Information Technology, 3(3),