This article describes a method for early detection of disaster-related damage to cultural heritage. It is based on data from social media, a timely and large-scale data source that is nevertheless quite noisy. First, we collect images posted on social media that may refer to a cultural heritage site. Then, we automatically categorize these images according to two dimensions: whether they are indeed a photo in which a cultural heritage resource is the main subject, and whether they represent damage. Both categorizations are challenging image classification tasks, given the ambiguity of these visual categories; we tackle both tasks using a convolutional neural network. We test our methodology on a large collection of thousands of images from the web and social media, which exhibit the diversity and noise that is typical of these sources, and contain buildings and other architectural elements, heritage and not-heritage, damaged by disasters as well as intact. Our results show that while the automatic classification is not perfect, it can greatly reduce the manual effort required to find photos of damaged cultural heritage by accurately detecting relevant candidates to be examined by a cultural heritage professional.

中文翻译：

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使用深度学习技术从社交媒体图像中检测受灾文化遗产

本文介绍了一种早期发现与灾害相关的文化遗产损坏的方法。它基于来自社交媒体的数据，这是一个及时的、大规模的数据源，但仍然相当嘈杂。首先，我们收集发布在社交媒体上的可能涉及文化遗产的图像。然后，我们根据两个维度自动对这些图像进行分类：它们是否确实是一张以文化遗产资源为主体的照片，以及它们是否代表损坏。鉴于这些视觉类别的模糊性，这两种分类都是具有挑战性的图像分类任务；我们使用卷积神经网络处理这两个任务。我们在来自网络和社交媒体的数千张图像的大集合上测试我们的方法，这些图像展示了这些来源典型的多样性和噪音，并包含建筑物和其他建筑元素，遗产和非遗产，被灾害破坏以及完好无损。我们的研究结果表明，虽然自动分类并不完美，但它可以通过准确检测文化遗产专业人员检查的相关候选者，大大减少查找受损文化遗产照片所需的人工工作。