> For the complete documentation index, see [llms.txt](https://envida-protocol.gitbook.io/envida/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://envida-protocol.gitbook.io/envida/about-envida-blockchain/why-envida.md).

# Why EnviDa?

Environmental tracking and lidar scanning using generative AI involves the use of AI algorithms to analyze data collected from lidar (Light Detection and Ranging) sensors, which use lasers to measure distances and create detailed 3D maps of the environment. The AI algorithms can be trained on large datasets to generate predictive models of environmental conditions, such as vegetation cover, topography, and soil moisture, allowing for more accurate and efficient tracking of environmental changes over time.

Generative AI can also be used to improve the accuracy and resolution of lidar scans by generating synthetic data that can be used to train and validate the algorithms. This can help to fill in gaps in the data and reduce the amount of manual labor required for data collection and analysis.

Overall, the use of generative AI for environmental tracking and lidar scanning can provide more accurate and comprehensive insights into the state of the environment, enabling better decision making and management of natural resources.


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