Rapid Mapping of Landslides on SAR Data by Attention U-Net
Por:
Nava, L, Bhuyan, K, Meena, SR, Monserrat, O, Catani, F
Publicada:
1 mar 2022
Categoría:
Earth and planetary sciences (miscellaneous)
Resumen:
Multiple landslide events are common around the globe. They can cause severe damage to both human lives and infrastructures. Although a huge quantity of research has been shaped to address rapid mapping of landslides by optical Earth Observation (EO) data, various gaps and uncertainties are still present when dealing with cloud obscuration and 24/7 operativity. To address the issue, we explore the usage of SAR data over the eastern Iburi sub-prefecture of Hokkaido, Japan. In the area, about 8000 co-seismic landslides were triggered by an Mw 6.6 earthquake on 6 September 2018, at 03.08 local time (JST). In the following study, we modify a Deep Learning (DL) convolutional neural network (CNN) architecture suited for pixel-based classification purposes, the so-called Attention U-Net (Attn-U-Net) and we employ it to evaluate the potential of bi- and tri-temporal SAR amplitude data from the Sentinel-1 satellite and slope angle to map landslides even under thick cloud cover. Four different datasets, composed of two different band combinations per two satellite orbits (ascending and descending) are analyzed. Moreover, the impact of augmentations is evaluated independently for each dataset. The models' predictions are compared against an accurate landslide inventory obtained by manual mapping on pre-and post-event PlanetScope imagery through F1-score and other common metrics. The best result was yielded by the augmented ascending tri-temporal SAR composite image (61% F1-score). Augmentations have a positive impact on the ascending Sentinel-1 orbit, while metrics decrease when augmentations are applied on descending path. Our findings demonstrate that combining SAR data with other data sources may help to map landslides quickly, even during storms and under deep cloud cover. However, further investigations and improvements are still needed, this being one of the first attempts in which the combination of SAR data and DL algorithms are employed for landslide mapping purposes.
Filiaciones:
Nava, L:
Univ Padua, Dept Geosci, Machine Intelligence & Slope Stabil Lab, I-35129 Padua, Italy
Bhuyan, K:
Univ Padua, Dept Geosci, Machine Intelligence & Slope Stabil Lab, I-35129 Padua, Italy
Meena, SR:
Univ Padua, Dept Geosci, Machine Intelligence & Slope Stabil Lab, I-35129 Padua, Italy
Univ Twente, Fac Geoinformat Sci & Earth Observat ITC, Dept Appl Earth Sci, NL-7514 AE Enschede, Netherlands
Monserrat, O:
Ctr Tecnol Telecomunicac Catalunya CTTC, Dept Remote Sensing, Geomat Res Unit, Barcelona 08860, Spain
Catani, F:
Univ Padua, Dept Geosci, Machine Intelligence & Slope Stabil Lab, I-35129 Padua, Italy
Green Published, gold, All Open Access; Gold Open Access; Green Open Access
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