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We propose a new convolutional neural networks method in combination with ordinal regression aiming at assessing the degree of building damage caused by earthquakes with aerial imagery. The ordinal regression model and a deep learning algorithm are incorporated to make full use of the information to improve the accuracy of the assessment. A new loss function was introduced in this paper to combine convolutional neural networks and ordinal regression. Assessing the level of damage to buildings can be considered as equivalent to predicting the ordered labels of buildings to be assessed. In the existing research, the problem has usually been simplified as a problem of pure classification to be further studied and discussed, which ignores the ordinal relationship between different levels of damage, resulting in a waste of information. Data accumulated throughout history are used to build network models for assessing the level of damage, and models for assessing levels of damage to buildings based on deep learning are described in detail, including model construction, implementation methods, and the selection of hyperparameters, and verification is conducted by experiments. When categorizing the damage to buildings into four types, we apply the method proposed in this paper to aerial images acquired from the 2014 Ludian earthquake and achieve an overall accuracy of 77.39%; when categorizing damage to buildings into two types, the overall accuracy of the model is 93.95%, exceeding such values in similar types of theories and methods. a set of 13 change detention features and support vector machine (SVM). Simon Plank [12] reviewed the methods of rapid damage assessment using multitemporal Synthetic Aperture Radar(SAR) data. Gupta et al. [13] present a satellite imagery dataset for building damage assessment with over 700,000 labeled building instances covering over 5000 km 2 of imagery.Recent studies show that the machine learning algorithm performs well in earthquake damage assessment. Li [14] assessed building damage with one-class SVM using pre-and post-earthquake QuickBird imagery and assessed the discrimination power of different level (pixel-level, texture, and object-based) features. Haiyang et al. [15] combined SVM and the image segmentation method to detect building damage. Cooner et al. [16] evaluate the effectiveness of machine learning algorithms in detecting earthquake damage. A series of textural and structural features were used in this study. A SVM and feature selection approach was carried out for damage mapping with post-event very high spatial resolution(VHR) image and obtained overall accuracy (OA) of 96.8% and Kappa of 0.5240 [11]. Convolutional neural networks (CNN) was utilized to identify collapsed buildings from post-event satellite imagery and obtained an OA of 80.1% and Kappa of 0.46 [17]. Multiresolution feature maps were derived and fused with CNN for the image classification of building damages in [18], and an OA of 88.7% was obtained.Most of the above-mentioned damage information extracti...

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The Amery Ice Shelf is the largest ice shelf in East Antarctica. It drains continental ice from an area of more than one million square kilometres through a section of coastline that represents approximately 2% of the total circumference of the Antarctic continent. In this study, we used a time series of ENVISAT ASAR images from 2004-2012 and flow lines derived from surface velocity data to monitor the changes in 12 tributaries of the Amery Ice Shelf front. The results show that the Amery Ice Shelf has been expanding and that the rates of expansion differ across the shelf. The highest average annual rate of advance from 2004-2012 was 3.36 m•d-1 and the lowest rate was 1.65 m•d-1. The rates in 2009 and 2010 were generally lower than those in other years. There was a low correlation between the rate of expansion and the atmospheric temperature recorded at a nearby research station, however the mechanism of the relationship was complex. This study shows that the expansion of the Amery Ice Shelf is slowing down, reflecting a changing trend in climate and ice conditions in East Antarctica.

The rapid assessment of building damage in earthquake-stricken areas is of paramount importance for emergency response. The development of remote sensing technology has aided in deriving reliable and precise building damage assessments of extensive areas following disasters. It is well documented that convolutional neural network methods have superior performance in earthquake building damage assessment compared with traditional machine learning methods. However, deep learning models require a large number of samples, and sufficient numbers of samples are usually not available in the newly earthquake-stricken areas rapidly enough. At the same time, the historical samples inevitably differ from the new earthquake-affected areas due to the discrepancy of regional building characteristics. For this purpose, this study proposes a data transfer algorithm for evaluating the impact of a single historical training sample on the model performance. Then, beneficial samples are selected to transfer knowledge from the historical data for facilitating the calibration of the new model. Four models are designed with two earthquake damage building datasets and the performance of the models is compared and evaluated. The results show that the data transfer algorithm proposed in this work improves the reliability of the building damage assessment model significantly by filtering samples from the historical data that are suitable for the new task. The performance of the model built based on the data transfer method on the test set of new earthquakes task is approximately 8% higher in overall accuracy compared with the model trained directly with the new earthquake samples when the training data for the new task is only 10% of the historical data and is operating under the objective of four classes of building damage. The proposed data transfer algorithm has effectively enhanced the precision of the seismic building damage assessment in a data-limited context. Thus, it could be applicable to the building damage assessment of new disasters.

ABSTRACT. During the 30th Chinese Antarctic Expedition in 2013/14, the Chinese icebreaker RV Xuelong answered a rescue call from the Russian RV Akademik Shokalskiy. While assisting the repatriation of personnel from the Russian vessel to the Australian RV Aurora Australis, RV Xuelong itself became entrapped within the compacted ice in the Adélie Depression region. Analysis of MODIS and SAR imagery provides a detailed description of the regional sea-ice conditions which led to the 6 day long besetment of RV Xuelong. The remotely sensed imagery revealed four stages of sea-ice characteristics during the entrapment: the gathering, compaction, dispersion and calving stages. Four factors characterizing the local sea-ice conditions during late December 2013 and early January 2014 were identified: surface component of the coastal current; near-surface wind; ocean tides; and surface air temperature. This study demonstrates that shipping activity in ice-invested waters should be underpinned by general knowledge of the ice situation. In addition, during such activity high spatiotemporal resolution remotely sensed data should be acquired regularly to monitor local and regional sea-ice changes with a view to avoiding the besetment of vessels.

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