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Satellite data and random forest model estimate building heights

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a method for estimating building heights in urban areas using a combination of satellite imagery and a geographically weighted random forest model. This approach aims to provide accurate building height data at a city scale, particularly in regions where LiDAR data is scarce or expensive. The study utilized data from TerraSAR-X StripMap, PlanetScope, and Sentinel-1 sensors, achieving an RMSE of 5.34 m against a LiDAR reference dataset. Analysis of feature importance revealed that different sensors and data types are more effective for estimating heights of varying building types and contexts. AI

RANK_REASON The cluster contains a research paper detailing a new methodology for building height estimation using satellite data. [lever_c_demoted from research: ic=1 ai=1.0]

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AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Satellite data and random forest model estimate building heights

COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Guilherme Iablonovski, Pierre-Louis Frison, Tatiana Silva da Silva ·

    Spatially explicit feature importance for building height estimation using research-access high-resolution SAR and optical sensors

    arXiv:2608.17822v1 Announce Type: cross Abstract: Accurate building height information at the individual footprint scale is essential for material stock accounting and post-disaster damage assessments yet remains difficult to obtain at city scale in the Global South where airborn…

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