> For the complete documentation index, see [llms.txt](https://docs.aeroai.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.aeroai.io/siega-web-features/analysis.md).

# Analysis

Analysis is where you take data that already holds more information than it first shows and bring that detail to the
 surface. A LiDAR scan, for example, often arrives as one solid wall of points, even though every point already knows
 whether it is ground, vegetation, a building, or a power line. The tools in this section let you read that hidden
 detail and put it to use, so the scene goes from a single mass of data to something you can actually pick apart and
 understand. Each tool has its own page with the full walkthrough.

Point Cloud Classification works with point clouds that were captured with classification codes built in. It reads
 those codes straight from the data, then hands you control over how each type appears. You can keep the natural color
 of the cloud or switch to coloring by class, recolor any type, hide the ones you do not need, and adjust the size of
 the points until the picture reads clearly. The result is the same scan, but now the ground, the trees, the buildings,
 and the wires each stand on their own.

This is where your data stops being one undivided whole and starts being something you can read piece by piece.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.aeroai.io/siega-web-features/analysis.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.