LAS 🗿

LAS — ASPRS Point Cloud (LiDAR format)

Open Beta
.las · application/vnd.las

LAS is the binary ASPRS industry standard for point clouds that has underpinned LiDAR data exchange for two decades: each point carries XYZ, intensity, and a classification code (ground, vegetation, buildings). An honest note about our export: we do NOT capture LiDAR from an aircraft — a real point cloud comes from a sensor, not from OpenStreetMap tags. Our exporter builds a structural cloud from OSM vector geometry: a building centroid plus a roof point at height get class 6 (Building), points along roads get class 2 (Ground), and POIs get class 1. The output is a valid LAS 1.2 (Point Data Record Format 0) that opens in CloudCompare, QGIS, and PDAL, but it represents schematic OSM points rather than a dense surface scan.

立即导出 LAS ✨ Open Beta — 免费无限制

主要特点

LAS 1.2, Point Data Record Format 0 (20 bytes per point): XYZ, intensity, classification, scan angle
ASPRS classification: buildings → class 6, roads → class 2 (ground), POIs → class 1
Buildings get a base point and a roof point at a height derived from height / building:levels tags
Correct 227-byte header with scales and bounding box — the file is read by laspy and PDAL without errors
A 500,000-point cap per export for predictable size and runtime
LAS 格式导出示例(莫斯科中心,1x0.5 公里)
示例:莫斯科中心区,约 1x0.5 公里,中等详细度

关于 LAS 格式

LAS is the ASPRS standard for point clouds that laser scanning data exchange rests on: every point carries coordinates, intensity and a classification code — ground, vegetation, building. The format is binary, compact and read by every point cloud processing tool.

The main thing first: a real point cloud is captured by a laser sensor, not by the OpenStreetMap database. Our export builds a structural cloud out of vector geometry: a building contributes a base point and a roof point at its height, points are spread along roads, and points of interest become separate marks. Geometrically this is valid LAS, but in content it is a schematic of a city rather than a survey of the surface.

Hence the scenarios: test the import in your own pipeline without hunting down a real dataset; show the structure of the file and the ASPRS class system; get predictable data for debugging filtering scripts.

Technically this is LAS version 1.2 with point records of format zero — twenty bytes per point: coordinates, intensity, return number, class, scan angle. Buildings get class 6, roads class 2 (ground), points of interest class 1. The header is correct, so the file is read without errors by laspy, PDAL, CloudCompare and QGIS. The limit is 500,000 points per export.

打开 LAS 文件的程序

以下程序可以打开、编辑和处理从 OSM2CDR 导出的 LAS — ASPRS Point Cloud (LiDAR format)(.las)文件:

💻CloudCompare 🌎QGIS 💻PDAL 💻lastools 💻Potree

格式规格

谁在使用 LAS 地图

🏗
A quick test LAS to validate an import pipeline in CloudCompare or PDAL without hunting for a real dataset
A ready file for testing the import in CloudCompare or PDAL without searching for a real dataset.
💻
Teaching demonstrations of LAS file structure and the ASPRS classification scheme on familiar city data
A clear walk-through of the LAS structure and the ASPRS classes on familiar city data.
📊
A rough structural skeleton of a city (building centroids + road network) as reference points over real LiDAR
A rough frame of a city — building centroids and the road network — as reference marks over real LiDAR.
🎨
Prototyping class-filter scripts (Classification[6:6] for buildings, [2:2] for ground) on predictable data
Predictable data for debugging scripts that filter by point class.

如何导出 LAS

1
选择区域
在 osm2cdr 的交互式地图上绘制矩形或多边形
2
选择 LAS
从 127 种可用导出格式中选择 LAS — ASPRS Point Cloud (LiDAR format)
3
导出
点击导出,1–5 分钟内收到您的 .las 文件
4
下载
下载完成的 LAS 文件并在 CloudCompare 中打开

导出后如何打开

LAS 格式的热门地图

Free

导出 LAS — ASPRS Point Cloud (LiDAR format) 地图

从 OpenStreetMap 下载专业的 LAS 地图。即时生成,任意区域。

免费 / 导出
立即导出 LAS →

常见问题

Is this a real LiDAR scan?
Which LAS version and point format?
Where do I get real LiDAR to combine with this export?
How many points end up in the file?
Do the points have color?
Why are there fewer points than I expected?

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经常一起导出

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