Building damage assessment for rapid disaster response with a deep object-based semantic change detection framework: From natural disasters to man-made disasters

Landslide failures detection and mapping using Synthetic Aperture Radar: Past, present and future

Earth observation (EO) technologies, such as optical imaging and synthetic aperture radar (SAR), provide excellent means to continuously monitor ever-growing urban environments. Notably, in the case of large-scale disasters (e.g., tsunamis and earthquakes), in which a response is highly time-critical, images from both data modalities can complement each other to accurately convey the full damage condition in the disaster aftermath. However, due to several factors, such as weather and satellite coverage, which data modality will be the first available for rapid disaster response efforts is often uncertain. Hence, novel methodologies that can utilize all accessible EO datasets are essential for disaster management. In this study, we developed a global multimodal and multitemporal dataset for building damage mapping. We included building damage characteristics from three disaster types, namely, earthquakes, tsunamis, and typhoons, and considered three building damage categories. The global dataset contains high-resolution (HR) optical imagery and high-to-moderate-resolution SAR data acquired before and after each disaster. Using this comprehensive dataset, we analyzed five data modality scenarios for damage mapping: single-mode (optical and SAR datasets), cross-modal (pre-disaster optical and post-disaster SAR datasets), and mode fusion scenarios. We defined a damage mapping framework for semantic segmentation of damaged buildings based on a deep convolutional neural network (CNN) algorithm. We also compared our approach to another state-of-the-art model for damage mapping. The results indicated that our dataset, together with a deep learning network, enabled acceptable predictions for all the data modality scenarios. We also found that the results from cross-modal mapping were comparable to the results obtained from a fusion sensor and optical mode analysis.

Learning from multimodal and multitemporal earth observation data for building damage mapping

A review on synthetic aperture radar-based building damage assessment in disasters

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.

/pdf/rapid-mapping-of-landslides-on-sar-data-by-attention-u-net-1owjdhml.pdf

Rapid Mapping of Landslides on SAR Data by Attention U-Net

A rapid mapping of landslides following a disaster is important for coordinating emergency response and limiting rescue delays. A synthetic aperture radar (SAR) can provide a solution even in harsh weather and at night, due to its independence of weather and light, quick response, no contact and broad coverage. This study aimed to conduct a comprehensive exploration on the intensity and coherence information of three Advanced Land Observing Satellite-2 (ALOS-2) SAR images, for rapid massive landslide mapping in a pixel level, in order to provide a reference for future applications. Applied data were two pre-event and one post-event high-resolution ALOS-2 products. Studied area was in the east of Iburi, Hokkaido, Japan, where massive shallow landslides were triggered in the 2018 Hokkaido Eastern Iburi Earthquake. Potential parameters, including intensity difference (d), co-event correlation coefficient (r), correlation coefficient difference ( ∆ r ), co-event coherence ( γ ), and coherence difference ( ∆ γ ), were first selected and calculated based on a radar reflection mechanism, to facilitate rapid detection. Qualitative observation was then performed by overlapping ground truth landslides to calculated parameter images. Based on qualitative observation, an absolute value of d ( d a b s 1 ) was applied to facility analyses, and a new parameter ( d a b s 2 ) was proposed to avoid information loss in the calculation. After that, quantitative analyses of the six parameters ( d a b s 1 , d a b s 2 , r, ∆ r , γ and ∆ γ ) were performed by receiver operating characteristic. d a b s 2 and ∆ r were found to be favorable parameters, which had the highest AUC values of 0.82 and 0.75, and correctly classified 69.36% and 64.57% landslide and non-landslide pixels by appropriate thresholds. Finally, a discriminant function was developed, combining three relatively favorable parameters ( d a b s 2 , ∆ r , and ∆ γ ) with one in each type, and achieved an overall accuracy of 74.31% for landslide mapping.

Study on the Intensity and Coherence Information of High-Resolution ALOS-2 SAR Images for Rapid Massive Landslide Mapping at a Pixel Level

When carrying out change detection for building damage assessment using synthetic aperture radar (SAR) intensity images, it is desirable that the observation conditions of the images are similar and acquisition time is close to the earthquake occurrence time. In this way, the influence of the radar operating system and ground temporal changes can be minimized, facilitating high-accuracy assessment results. However, in practice, especially in poor developing areas, it is difficult to obtain ideal images owing to limited pre-event data archives. In the 2015 Gorkha, Nepal earthquake, the TerraSAR-X satellite captured the influenced Sankhu area before and after the earthquake on May 30, 2010 and May 13, 2015, respectively. The pre-event data was obtained in an ascending path with an incidence angle of 41°, whereas the post-event data was obtained in a descending path with an incidence angle of 33°. To apply the obtained data that had different observation conditions and longtime intervals for building damage assessment, two ways were considered and studied. On one hand, the feasibility of change detection considering these factors was investigated. Pixel statistic characteristics were analyzed in twelve test areas to check the influence of temporal changes, and building footprints were buffered considering two different incidence angles. On the other hand, the reliability of classification based on only post-event data was studied. The results showed good classification performance of some texture parameters, such as the “range value” and “standard deviation,” which are worthy of further study. Moreover, the classification results obtained using the post-event data achieved similar accuracy to that using both the pre- and post-event data, preliminarily indicating the research value of post-event data-based building damage detection as it can solve the pre-event data limitation problem once and for all.

Building Damage Assessment Using Intensity SAR Data with Different Incidence Angles and Longtime Interval

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A review on synthetic aperture radar-based building damage assessment in disasters

Study on the Intensity and Coherence Information of High-Resolution ALOS-2 SAR Images for Rapid Massive Landslide Mapping at a Pixel Level

Building Damage Assessment Using Intensity SAR Data with Different Incidence Angles and Longtime Interval