In recent years, monitoring the world wide area with satellite images has been emerged as an important issue. Site monitoring task can be divided into two independent tasks; 1) Change Detection and 2) Anomaly Event Detection. Unlike to change detection research is actively conducted based on the numerous datasets(\eg LEVIR-CD, WHU-CD, S2Looking, xView2 and etc...) to meet up the expectations of industries or governments, research on AI models for detecting anomaly events is passively and rarely conducted. In this paper, we introduce a novel satellite imagery dataset(AED-RS) for detecting anomaly events on the open public places. AED-RS Dataset contains satellite images of normal and abnormal situations of 8 open public places from all over the world. Each places are labeled with different criteria based on the difference of characteristics of each places. With this dataset, we introduce a baseline model for our dataset TB-FLOW, which can be trained in weakly-supervised manner and shows reasonable performance on the AED-RS Dataset compared with the other NF(Normalizing-Flow) based anomaly detection models. Our dataset and code will be publicly open in \url{https://github.com/SIAnalytics/RS_AnomalyDetection.git}.

翻译：近年来，利用卫星图像进行全球范围监测已逐渐成为重要课题。站点监测任务可划分为两个独立方向：1）变化检测；2）异常事件检测。与基于海量数据集（如LEVIR-CD、WHU-CD、S2Looking、xView2等）为满足行业与政府需求而积极研究的变化检测不同，针对异常事件检测的人工智能模型研究进展缓慢且相对匮乏。本文提出一种用于检测公共开放空间异常事件的新型卫星图像数据集（AED-RS），该数据集包含全球8类公共开放场所在正常与异常状态下的卫星图像。基于各地场所特征差异，每类场景采用不同标注标准。我们进一步提出该数据集的基线模型TB-FLOW，该模型可通过弱监督方式进行训练，在AED-RS数据集上相较于其他基于归一化流（Normalizing-Flow）的异常检测模型展现出合理性能。我们的数据集与代码将公开于：\url{https://github.com/SIAnalytics/RS_AnomalyDetection.git}。