Prototype-oriented Unsupervised Change Detection for Disaster Management

Climate change has led to an increased frequency of natural disasters such as floods and cyclones. This emphasizes the importance of effective disaster monitoring. In response, the remote sensing community has explored change detection methods. These methods are primarily categorized into supervised techniques, which yield precise results but come with high labeling costs, and unsupervised techniques, which eliminate the need for labeling but involve intricate hyperparameter tuning. To address these challenges, we propose a novel unsupervised change detection method named Prototype-oriented Unsupervised Change Detection for Disaster Management (PUCD). PUCD captures changes by comparing features from pre-event, post-event, and prototype-oriented change synthesis images via a foundational model, and refines results using the Segment Anything Model (SAM). Although PUCD is an unsupervised change detection, it does not require complex hyperparameter tuning. We evaluate PUCD framework on the LEVIR-Extension dataset and the disaster dataset and it achieves state-of-the-art performance compared to other methods on the LEVIR-Extension dataset.

Improved Flood Insights: Diffusion-Based SAR to EO Image Translation

Driven by rapid climate change, the frequency and intensity of flood events are increasing. Electro-Optical (EO) satellite imagery is commonly utilized for rapid response. However, its utilities in flood situations are hampered by issues such as cloud cover and limitations during nighttime, making accurate assessment of damage challenging. Several alternative flood detection techniques utilizing Synthetic Aperture Radar (SAR) data have been proposed. Despite the advantages of SAR over EO in the aforementioned situations, SAR presents a distinct drawback: human analysts often struggle with data interpretation. To tackle this issue, this paper introduces a novel framework, Diffusion-Based SAR to EO Image Translation (DSE). The DSE framework converts SAR images into EO images, thereby enhancing the interpretability of flood insights for humans. Experimental results on the Sen1Floods11 and SEN12-FLOOD datasets confirm that the DSE framework not only delivers enhanced visual information but also improves performance across all tested flood segmentation baselines.

Focus or Not: A Baseline for Anomaly Event Detection On the Open Public Places with Satellite Images

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}.