The earthquake potential of the Lembang Fault, located near the city of Bandung in West Java, Indonesia, is poorly understood. Bandung has a population of over 8 million people. We used satellite data to estimate the energy storage on the fault and calculate the likely size of potential future earthquakes. We use simulations to show that 1.9–2.7 million people would be exposed to high levels of ground shaking in the event of a major earthquake on the fault.

This study explores the efficacy of several machine learning models for damage characterization, trained and tested on the Database of Observed Damage (DaDO) for Italian earthquakes. Reasonable damage prediction effectiveness (68 % accuracy) is observed, particularly when considering basic structural features and grouping the damage according to the traffic-light-based system used during the post-disaster period (green, yellow, and red), showing higher relevancy for rapid damage prediction.

We analysed the background seismic noise for the Italian strong motion network by developing the Italian accelerometric low- and high-noise models. Spatial and temporal variations of the noise levels have been analysed. Several stations located near urban areas are affected by human activities, with high noise levels in the low periods. Our results provide an overview of the background noise of the strong motion network and can be used as a station selection criterion for future research.

Coastal areas are fragile ecosystems that face multiple hazards. In this study, we measured the downward motion of the Nice Côte d'Azur Airport (France) that was built on reclaimed area and found that it has subsided from 16 mm yr^-1 in the 1990s to 8 mm yr^-1 today. A continuous remote monitoring of the platform will provide key data for a detailed investigation of future subsidence maps, and this contribution will help to evaluate the potential failure of part of the airport platform.

This paper evaluates the flood risk and the resulting patterns in buildings following low-probability, high-impact flood scenarios by a risk analysis chain in Shanghai. The results provide a benchmark and also a clear future for buildings with respect to flood risks in Shanghai. This study links directly to disaster risk management, e.g., the Shanghai Master Plan. We also discussed different potential adaptation options for flood risk management.

Landslide warning systems often use statistical models to predict landslides based on rainfall. They are typically trained on large datasets with many landslide occurrences, but in rural areas large datasets may not exist. In this study, we evaluate which statistical model types are best suited to predicting landslides and demonstrate that even a small landslide inventory (five storms) can be used to train useful models for landslide early warning when non-landslide events are also included.

We present cost-effective photogrammetric systems for high-resolution rockfall monitoring. The paper outlines the components, assembly, and programming codes required. The systems utilize prime cameras to generate 3D models and offer comparable performance to lidar for change detection monitoring. Real-world applications highlight their potential in geohazard monitoring which enables accurate detection of pre-failure deformation and rockfalls with a high temporal resolution.

In August and September 2022, millions of people were impacted by a severe flood event in Pakistan. Since many roads and other infrastructure were destroyed, satellite data were the only way of providing large-scale information on the flood's impact. Based on the flood mapping algorithm developed at Technische Universität Wien (TU Wien), we mapped an area of 30 492 km² that was flooded at least once during the study's time period. This affected area matches about the total area of Belgium.