LLMs can generate a 3D map in seconds. That speed is impressive, but it is not the same as understanding the landscape the map represents.
If an AI can generate a map in a few seconds, does that mean it actually understands the landscape it is mapping?
The recent progress of LLMs can make it seem that way. A simple prompt can generate Python code, process terrain data, and create a visually appealing 3D map. The output often looks impressive enough to create the impression that mapping has become an almost fully automated task.
But mapping is not just about generating code.
A map is a representation of reality. The way a mountain range is displayed, the colours used to represent elevation, the boundaries shown, and even the amount of detail included can influence how people interpret a landscape. Two maps created from the same dataset can look very different and communicate different things.
This is where an interesting limitation of LLMs appears. They are very good at generating code, but they have limited understanding of the geospatial concepts behind the code. A generated script may run successfully and still produce a map that is visually misleading or scientifically inaccurate. The challenge is not whether the code works, but whether the map makes sense.
Consider a simple prompt: create a 3D terrain map using SRTM data and reproject it to UTM. An LLM may generate code that appears convincing, references a dataset, applies a projection, and produces a map. However, it may also introduce dataset names, functions, or parameters that do not actually exist. More importantly, it may not verify whether coordinate systems, elevation units, or spatial alignment are correct. The result can be a map that looks plausible but contains errors that make it unreliable for analysis.
This observation is also reflected in the GeoCode-GPT paper, which explores geospatial code generation using LLMs. The paper highlights that general-purpose language models can struggle with specialised geospatial tasks and may generate outputs that appear correct while containing important mistakes.
Another issue is hallucination. LLMs can sometimes invent dataset names, functions, parameters, or workflows that do not actually exist. In specialised domains such as geospatial analysis, these errors can be difficult to identify unless the output is carefully checked.
There are also practical constraints. Many cloud-mapping platforms and geospatial tools provide powerful capabilities, but some advanced features, such as higher processing limits and access to large-scale datasets, are often restricted behind paid tiers or platform limits. As a result, users may need to work with reduced detail, smaller study areas, or simplified workflows. While these compromises make mapping more accessible, they can also affect the quality and richness of the final 3D output.
The broader lesson is that generating a map and understanding a map are two different things. LLMs are excellent assistants for coding, debugging, and exploring ideas, but they are not a substitute for geospatial knowledge and human judgement. Though this is the situation as of now, efforts are being carried out by geospatial companies to leverage Geo-AI, which may help overcome these limitations in the future.
Perhaps the more interesting question is not whether AI can create maps, but whether it can understand the reality those maps are trying to represent.