地震中的建筑变化检测：一种多尺度交互网络和一个变化检测数据集

QuickQuakeBuildings：用于快速受损建筑物检测的震后SAR-光学数据集

QuickQuakeBuildings: Post-earthquake SAR-Optical Dataset for Quick Damaged-building Detection

本文发布QuickQuakeBuildings数据集，提供2023年土耳其-叙利亚地震后配准的VHR SAR与光学影像及四千余栋建筑标注，并给出二分类/异常检测基线以支持快速震损建筑检测算法研发。

Deep vs. Shallow Learning: A Benchmark Study in Low Magnitude Earthquake Detection

While deep learning models have seen recent high uptake in the geosciences, and are appealing in their ability to learn from minimally processed input data, as black box models they do not provide an easy means to understand how a decision is reached, which in safety-critical tasks especially can be problematical. An alternative route is to use simpler, more transparent white box models, in which task-specific feature construction replaces the more opaque feature discovery process performed automatically within deep learning models. Using data from the Groningen Gas Field in the Netherlands, w…

Deep convolutional neural network application on rooftop detection for aerial image

As one of the most destructive disasters in the world, earthquake causes death, injuries, destruction and enormous damage to the affected area. It is significant to detect buildings after an earthquake in response to reconstruction and damage evaluation. In this research, we proposed an automatic rooftop detection method based on the convolutional neural network (CNN) to extract buildings in the city of Christchurch and tuned hyperparameters to detect small detached houses from the aerial image. The experiment result shows that our approach can effectively and accurately detect and segment bui…

Building Damage Detection in Satellite Imagery Using Convolutional Neural Networks

In all types of disasters, from earthquakes to armed conflicts, aid workers need accurate and timely data such as damage to buildings and population displacement to mount an effective response. Remote sensing provides this data at an unprecedented scale, but extracting operationalizable information from satellite images is slow and labor-intensive. In this work, we use machine learning to automate the detection of building damage in satellite imagery. We compare the performance of four different convolutional neural network models in detecting damaged buildings in the 2010 Haiti earthquake. We…

Winning with Simple Learning Models: Detecting Earthquakes in Groningen, the Netherlands

Deep learning is fast emerging as a potential disruptive tool to tackle longstanding research problems across the sciences. Notwithstanding its success across disciplines, the recent trend of the overuse of deep learning is concerning to many machine learning practitioners. Recently, seismologists have also demonstrated the efficacy of deep learning algorithms in detecting low magnitude earthquakes. Here, we revisit the problem of seismic event detection but using a logistic regression model with feature extraction. We select well-discriminating features from a huge database of time-series ope…