Detecting Looted Archaeological Sites from Satellite Image Time Series

• URL: http://arxiv.org/abs/2409.09432v1
• Date: Sat, 14 Sep 2024 13:13:33 GMT
• Title: Detecting Looted Archaeological Sites from Satellite Image Time Series
• Abstract summary: This dataset is a labeled multi-temporal remote sensing dataset containing 55,480 images acquired monthly over 8 years across 675 Afghan archaeological sites. datasetnameis particularly challenging because of the limited number of training samples, the class imbalance, the weak binary annotations only available at the level of the time series. We evaluate a large set of baselines, outline the substantial benefits of using foundation models and show the additional boost that can be provided by using complete time series instead of using a single image.
• Score: 29.881103145285252
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Archaeological sites are the physical remains of past human activity and one of the main sources of information about past societies and cultures. However, they are also the target of malevolent human actions, especially in countries having experienced inner turmoil and conflicts. Because monitoring these sites from space is a key step towards their preservation, we introduce the DAFA Looted Sites dataset, \datasetname, a labeled multi-temporal remote sensing dataset containing 55,480 images acquired monthly over 8 years across 675 Afghan archaeological sites, including 135 sites looted during the acquisition period. \datasetname~is particularly challenging because of the limited number of training samples, the class imbalance, the weak binary annotations only available at the level of the time series, and the subtlety of relevant changes coupled with important irrelevant ones over a long time period. It is also an interesting playground to assess the performance of satellite image time series (SITS) classification methods on a real and important use case. We evaluate a large set of baselines, outline the substantial benefits of using foundation models and show the additional boost that can be provided by using complete time series instead of using a single image.

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