Project One

Category: GIS / Lidar data processing

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Overview

The CITYLID project delivers a richly annotated, large-scale urban LiDAR dataset to support precise urban planning and design. At its core, it transforms raw, uncategorized citywide aerial LiDAR point clouds into a semantically meaningful resource by systematically labeling data points and enriching them with street‐ and shadow‐related information. Through this methodical framework, the raw point clouds are first classified into standard urban categories—buildings, trees, and ground—before being fused with detailed street‐feature data such as driveways, medians, bikepaths, walkways, and on-street parking. Leveraging the inherent height information in LiDAR, the project also generates shadow maps via solar‐radiation modeling and seamlessly integrates them back into the point clouds, yielding a comprehensive three-dimensional urban representation. Beyond the dataset itself, CITYLID offers full transparency of its workflow: detailed documentation outlines each step of the LiDAR categorization process, and starter code is provided to extract relevant subsets of the point clouds. This combination of semantic labels, street‐level features, and shadow information—packaged with methodological guidelines—makes CITYLID a versatile tool for a range of urban analyses, from street cross-section studies to neighborhood‐scale comparisons and urban tree inventories.

Key Features

Gallery

Screenshot or photo of Project One

Technologies/Data Used

Python, Lidar datasets, Urban Street Networks.

Resources