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digital-surface-model

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Preprocessed and split version of the SSBH remote sensing dataset for building height estimation, including RGB composites, height maps, and building masks with train/valid/test manifests and ready-to-train scripts.

  • Updated Sep 24, 2025
  • Jupyter Notebook

Python-based slippy map tile server that downloads USGS 3DEP LIDAR point clouds, processes them into DSMs and generates tiles. Intended to reveal hidden terrain features (building edges, cliffs) under vegetation in OpenStreetMap iD Editor

  • Updated May 11, 2026
  • Python

Hands-on introductory lab activities for learning lidar and deriving gridded products (DEM, DSM, DTM, and CHM) in R. Designed for undergraduate students with no prior lidar or programming experience, the browser-based lessons guide learners through accessing public lidar data, processing point clouds, and interpreting terrain and canopy datasets.

  • Updated Sep 9, 2026
  • Jupyter Notebook

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