TEMPO: Global Temporal Building Density and Height Estimation from Satellite Imagery
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
ECHOSAT: Estimating Canopy Height Over Space And Time
Forest monitoring is critical for climate change mitigation. However, existing global tree height maps provide only static snapshots and do not capture temporal forest dynamics, which are essential for accurate carbon ac…
Capturing Temporal Dynamics in Large-Scale Canopy Tree Height Estimation
With the rise in global greenhouse gas emissions, accurate large-scale tree canopy height maps are essential for understanding forest structure, estimating above-ground biomass, and monitoring ecological disruptions. To …
A CNN regression model to estimate buildings height maps using Sentinel-1 SAR and Sentinel-2 MSI time series
Accurate estimation of building heights is essential for urban planning, infrastructure management, and environmental analysis. In this study, we propose a supervised Multimodal Building Height Regression Network (MBHR-N…
ManagementTime SeriesEstimate the building height at a 10-meter resolution based on Sentinel data
Building height is an important indicator for scientific research and practical application. However, building height products with a high spatial resolution (10m) are still very scarce. To meet the needs of high-resolut…
Feature ImportanceAdaptive Height Optimisation for Cellular-Connected UAVs using Reinforcement Learning
Providing reliable connectivity to cellular-connected UAV can be very challenging; their performance highly depends on the nature of the surrounding environment, such as density and heights of the ground BSs. On the othe…
reinforcement-learningReinforcement Learning (RL)