Building Age Estimation: A New Multi-Modal Benchmark Dataset and Community Challenge
Journal:
arXiv
Published Date:
Feb 19, 2025
Abstract
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.
