Related papers: TEMPO: Global Temporal Building Density and Height Estimation from Satellite Imagery

TEMPO: Global Temporal Building Density and Height Estimation from Satellite Imagery

URL: http://arxiv.org/abs/2511.12104v1
Date: Sat, 15 Nov 2025 08:48:34 GMT
Title: TEMPO: Global Temporal Building Density and Height Estimation from Satellite Imagery
Authors: Tammy Glazer, Gilles Q. Hacheme, Akram Zaytar, Luana Marotti, Amy Michaels, Girmaw Abebe Tadesse, Kevin White, Rahul Dodhia, Andrew Zolli, Inbal Becker-Reshef, Juan M. Lavista Ferres, Caleb Robinson,
Abstract summary: We present TEMPO, a global, temporally resolved dataset of building density and height derived from high-resolution satellite imagery using deep learning models.<n>We pair building footprint and height data with quarterly PlanetScope basemap satellite images to train a multi-task deep learning model that predicts building density and building height at a 37.6-meter per pixel resolution.<n>We apply this model to global PlanetScope basemaps from Q1 2018 through Q2 2025 to create global, temporal maps of building density and height.
Score: 6.725489723441197
License: http://creativecommons.org/licenses/by/4.0/
Abstract: We present TEMPO, a global, temporally resolved dataset of building density and height derived from high-resolution satellite imagery using deep learning models. We pair building footprint and height data from existing datasets with quarterly PlanetScope basemap satellite images to train a multi-task deep learning model that predicts building density and building height at a 37.6-meter per pixel resolution. We apply this model to global PlanetScope basemaps from Q1 2018 through Q2 2025 to create global, temporal maps of building density and height. We validate these maps by comparing against existing building footprint datasets. Our estimates achieve an F1 score between 85% and 88% on different hand-labeled subsets, and are temporally stable, with a 0.96 five-year trend-consistency score. TEMPO captures quarterly changes in built settlements at a fraction of the computational cost of comparable approaches, unlocking large-scale monitoring of development patterns and climate impacts essential for global resilience and adaptation efforts.

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