An Object-Based Deep Learning Approach for Building Height Estimation from Single SAR Images

• URL: http://arxiv.org/abs/2507.08096v1
• Date: Thu, 10 Jul 2025 18:16:16 GMT
• Title: An Object-Based Deep Learning Approach for Building Height Estimation from Single SAR Images
• Authors: Babak Memar, Luigi Russo, Silvia Liberata Ullo, Paolo Gamba,
• Abstract summary: This paper introduces a Deep Learning (DL)-based methodology for automated building height estimation from single VHR COSMO-SkyMed images.<n>This model was trained and evaluated on a unique multi-continental dataset comprising eight geographically diverse cities across Europe, North and South America, and Asia.<n>The results demonstrate highly promising performance, particularly on European cities where the model achieves a Mean Absolute Error (MAE) of approximately one building story (2.20 m in Munich)
• Score: 1.6070833439280312
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Accurate estimation of building heights using very high resolution (VHR) synthetic aperture radar (SAR) imagery is crucial for various urban applications. This paper introduces a Deep Learning (DL)-based methodology for automated building height estimation from single VHR COSMO-SkyMed images: an object-based regression approach based on bounding box detection followed by height estimation. This model was trained and evaluated on a unique multi-continental dataset comprising eight geographically diverse cities across Europe, North and South America, and Asia, employing a cross-validation strategy to explicitly assess out-of-distribution (OOD) generalization. The results demonstrate highly promising performance, particularly on European cities where the model achieves a Mean Absolute Error (MAE) of approximately one building story (2.20 m in Munich), significantly outperforming recent state-of-the-art methods in similar OOD scenarios. Despite the increased variability observed when generalizing to cities in other continents, particularly in Asia with its distinct urban typologies and prevalence of high-rise structures, this study underscores the significant potential of DL for robust cross-city and cross-continental transfer learning in building height estimation from single VHR SAR data.

Related papers

• Enhancing Monocular Height Estimation via Sparse LiDAR-Guided Correction [18.671925059007478]
  We investigate a state-of-the-art MHE model trained purely on synthetic data.<n>We find that the model relies heavily on shadow cues, a factor that can lead to overestimation or underestimation of heights.<n>We propose a novel correction pipeline that integrates sparse, imperfect global LiDAR measurements.
  arXiv  Detail & Related papers  (2025-05-11T08:54:09Z)
• Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmark [101.23684938489413]
  Anomaly detection (AD) is often focused on detecting anomalies for industrial quality inspection and medical lesion examination. This work first constructs a large-scale and general-purpose COCO-AD dataset by extending COCO to the AD field. Inspired by the metrics in the segmentation field, we propose several more practical threshold-dependent AD-specific metrics.
  arXiv  Detail & Related papers  (2024-04-16T17:38:26Z)
• Recognize Any Regions [55.76437190434433]
  RegionSpot integrates position-aware localization knowledge from a localization foundation model with semantic information from a ViL model.<n>Experiments in open-world object recognition show that our RegionSpot achieves significant performance gain over prior alternatives.
  arXiv  Detail & Related papers  (2023-11-02T16:31:49Z)
• Cross-City Matters: A Multimodal Remote Sensing Benchmark Dataset for Cross-City Semantic Segmentation using High-Resolution Domain Adaptation Networks [82.82866901799565]
  We build a new set of multimodal remote sensing benchmark datasets (including hyperspectral, multispectral, SAR) for the study purpose of the cross-city semantic segmentation task. Beyond the single city, we propose a high-resolution domain adaptation network, HighDAN, to promote the AI model's generalization ability from the multi-city environments. HighDAN is capable of retaining the spatially topological structure of the studied urban scene well in a parallel high-to-low resolution fusion fashion.
  arXiv  Detail & Related papers  (2023-09-26T23:55:39Z)
• Semi-supervised Learning from Street-View Images and OpenStreetMap for Automatic Building Height Estimation [59.6553058160943]
  We propose a semi-supervised learning (SSL) method of automatically estimating building height from Mapillary SVI and OpenStreetMap data. The proposed method leads to a clear performance boosting in estimating building heights with a Mean Absolute Error (MAE) around 2.1 meters. The preliminary result is promising and motivates our future work in scaling up the proposed method based on low-cost VGI data.
  arXiv  Detail & Related papers  (2023-07-05T18:16:30Z)
• A CNN regression model to estimate buildings height maps using Sentinel-1 SAR and Sentinel-2 MSI time series [0.0]
  In this study, we propose a supervised Multimodal Building Height Network (MBHR-Net) for estimating building heights at 10m spatial resolution using Sentinel-1 (S1) and Sentinel-2 (S2) time series. Our MBHR-Net aims to extract meaningful features from the S1 and S2 images to learn complex-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.
  arXiv  Detail & Related papers  (2023-07-03T22:16:17Z)
• Building Coverage Estimation with Low-resolution Remote Sensing Imagery [65.95520230761544]
  We propose a method for estimating building coverage using only publicly available low-resolution satellite imagery. Our model achieves a coefficient of determination as high as 0.968 on predicting building coverage in regions of different levels of development around the world.
  arXiv  Detail & Related papers  (2023-01-04T05:19:33Z)
• Strict baselines for Covid-19 forecasting and ML perspective for USA and Russia [105.54048699217668]
  Covid-19 allows researchers to gather datasets accumulated over 2 years and to use them in predictive analysis. We present the results of a consistent comparative study of different types of methods for predicting the dynamics of the spread of Covid-19 based on regional data for two countries: the United States and Russia.
  arXiv  Detail & Related papers  (2022-07-15T18:21:36Z)
• A Novel CNN-LSTM-based Approach to Predict Urban Expansion [1.2233362977312943]
  Time-series remote sensing data offer a rich source of information that can be used in a wide range of applications. This paper addresses the challenge of using time-series satellite images to predict urban expansion. We propose a novel two-step approach based on semantic image segmentation in order to predict urban expansion.
  arXiv  Detail & Related papers  (2021-03-02T12:58:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.