Building Age Estimation: A New Multi-Modal Benchmark Dataset and Community Challenge

원문 초록

Estimating the construction year of buildings is of great importance for sustainability. Sustainable buildings minimize energy consumption and are a key part of responsible and sustainable urban planning and development to effectively combat climate change. By using Artificial Intelligence (AI) and recently proposed powerful Transformer models, we are able to estimate the construction epoch of buildings from a multi-modal dataset. In this paper, we introduce a new benchmark multi-modal dataset, i.e. the Map your City Dataset (MyCD), containing top-view Very High Resolution (VHR) images, Earth Observation (EO) multi-spectral data from the Copernicus Sentinel-2 satellite constellation, and street-view images in many different cities in Europe that are co-localized with respect to the building under study and labelled with the construction epoch. We assess EO generalization performance on new/ previously unseen cities that have been held-out from training and appear only during inference. In this work, we present the community-based data challenge we organized based on MyCD. The AI4EO Challenge ESA MapYourCity was opened in 2024 for 4 months. In this paper, we present the Top-4 performing models of the challenge, and the evaluation results. During inference, the performance of the models using: i) both all three input modalities, and ii) only the two top-view modalities, i.e. without the street-view ground images, is examined. The evaluation results in this work show that the models to estimate the construction year of buildings are effective and can achieve good performance on this difficult important real-world task, even when inference is on previously unseen cities, as well as even when using only the two top-view modalities (i.e. VHR and Sentinel-2) during inference.