Building Age Estimation: A New Multi-Modal Benchmark Dataset and Community Challenge
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2025
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| author | Dionelis, Nikolaos Feliciotti, Alessandra Marconcini, Mattia Peressutti, Devis Kadunc, Nika Oman Park, JaeWan Sinulingga, Hagai Raja Immanuel, Steve Andreas Tran, Ba Arnold, Caroline Longépé, Nicolas |
| author_facet | Dionelis, Nikolaos Feliciotti, Alessandra Marconcini, Mattia Peressutti, Devis Kadunc, Nika Oman Park, JaeWan Sinulingga, Hagai Raja Immanuel, Steve Andreas Tran, Ba Arnold, Caroline Longépé, Nicolas |
| contents | Estimating the construction year of buildings is critical for advancing sustainability, as older structures often lack energy-efficient features. Sustainable urban planning relies on accurate building age data to reduce energy consumption and mitigate climate change. In this work, we introduce MapYourCity, a novel multi-modal benchmark dataset comprising top-view Very High Resolution (VHR) imagery, multi-spectral Earth Observation (EO) data from the Copernicus Sentinel-2 satellite constellation, and co-localized street-view images across various European cities. Each building is labeled with its construction epoch, and the task is formulated as a seven-class classification problem covering periods from 1900 to the present. To advance research in EO generalization and multi-modal learning, we organized a community-driven data challenge in 2024, hosted by ESA $Φ$-lab, which ran for four months and attracted wide participation.
This paper presents the Top-4 performing models from the challenge and their evaluation results. We assess model generalization on cities excluded from training to prevent data leakage, and evaluate performance under missing modality scenarios, particularly when street-view data is unavailable. Results demonstrate that building age estimation is both feasible and effective, even in previously unseen cities and when relying solely on top-view satellite imagery (i.e. with VHR and Sentinel-2 images). The MapYourCity dataset thus provides a valuable resource for developing scalable, real-world solutions in sustainable urban analytics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_13818 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Building Age Estimation: A New Multi-Modal Benchmark Dataset and Community Challenge Dionelis, Nikolaos Feliciotti, Alessandra Marconcini, Mattia Peressutti, Devis Kadunc, Nika Oman Park, JaeWan Sinulingga, Hagai Raja Immanuel, Steve Andreas Tran, Ba Arnold, Caroline Longépé, Nicolas Computer Vision and Pattern Recognition Machine Learning Estimating the construction year of buildings is critical for advancing sustainability, as older structures often lack energy-efficient features. Sustainable urban planning relies on accurate building age data to reduce energy consumption and mitigate climate change. In this work, we introduce MapYourCity, a novel multi-modal benchmark dataset comprising top-view Very High Resolution (VHR) imagery, multi-spectral Earth Observation (EO) data from the Copernicus Sentinel-2 satellite constellation, and co-localized street-view images across various European cities. Each building is labeled with its construction epoch, and the task is formulated as a seven-class classification problem covering periods from 1900 to the present. To advance research in EO generalization and multi-modal learning, we organized a community-driven data challenge in 2024, hosted by ESA $Φ$-lab, which ran for four months and attracted wide participation. This paper presents the Top-4 performing models from the challenge and their evaluation results. We assess model generalization on cities excluded from training to prevent data leakage, and evaluate performance under missing modality scenarios, particularly when street-view data is unavailable. Results demonstrate that building age estimation is both feasible and effective, even in previously unseen cities and when relying solely on top-view satellite imagery (i.e. with VHR and Sentinel-2 images). The MapYourCity dataset thus provides a valuable resource for developing scalable, real-world solutions in sustainable urban analytics. |
| title | Building Age Estimation: A New Multi-Modal Benchmark Dataset and Community Challenge |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2502.13818 |