用卫星图生成全球建筑密度与高度的动态地图，精度高且成本低。
TEMPO: Global Temporal Building Density and Height Estimation from Satellite Imagery
- 基于深度学习，融合卫星影像与已有建筑数据，实现多任务预测。
- 全球覆盖，分辨率37.6米，可追踪2018至2025年每季度变化。
- 精度达F1 85%-88%，趋势一致性高达0.96，适合城市规划与气候研究。
我们提出TEMPO，一个基于深度学习模型从高分辨率卫星影像中生成的全球、时间解析的建筑密度与高度数据集。将现有数据集中的建筑轮廓与高度信息与季度更新的PlanetScope基底卫星图像配对，训练一个多任务深度学习模型，以37.6米/像素的分辨率预测建筑密度与高度。该模型应用于2018年Q1至2025年Q2的全球PlanetScope基底影像，生成全球范围的时空建筑密度与高度图。通过与现有建筑轮廓数据集对比验证，其在不同人工标注子集上的F1分数为85%至88%，五年趋势一致性达0.96。TEMPO能以远低于同类方法的计算成本捕捉建成区的季度变化，支持大规模发展模式与气候变化影响监测，助力全球韧性与适应性建设。
原文摘要 · Abstract (English)
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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