A CNN regression model to estimate buildings height maps using
  Sentinel-1 SAR and Sentinel-2 MSI time series
Ritu Yadav Andrea Nascetti Yifang Ban
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-Net)
for estimating building heights at 10m spatial resolution using Sentinel-1 (S1)
and Sentinel-2 (S2) satellite time series. S1 provides Synthetic Aperture Radar
(SAR) data that offers valuable information on building structures, while S2
provides multispectral data that is sensitive to different land cover types,
vegetation phenology, and building shadows. Our MBHR-Net aims to extract
meaningful features from the S1 and S2 images to learn complex spatio-temporal
relationships between image patterns and building heights. The model is trained
and tested in 10 cities in the Netherlands. Root Mean Squared Error (RMSE),
Intersection over Union (IOU), and R-squared (R2) score metrics are used to
evaluate the performance of the model. The preliminary results (3.73m RMSE,
0.95 IoU, 0.61 R2) demonstrate the effectiveness of our deep learning model in
accurately estimating building heights, showcasing its potential for urban
planning, environmental impact analysis, and other related applications.