In recent years, remote sensing analysis in the context of armed conflicts have gained increasing interest due to the capability of providing continuous and objective information over large areas. The destruction of the Nova Kakhovka dam and the subsequent discharge of the Kahkovske reservoir marked an significant event in the ongoing conflict in Ukraine with far-reaching consequences for water management, agriculture and energy planning. In this contribution, we reconstruct the drying out of the Kakhovske reservoir using Sentinel-1/2 time series data. We utilize existing, well-established methodologies to extract surface water information from optical and SAR satellite imagery and build multi-sensor time series. Water segmentation results are validated using very high resolution (VHR) WorldView-2 acquisitions.

Remote sensing data has become an essential part of today’s disaster management activities. In recent years, the German Aerospace Center (DLR) has developed a multi-sensor system for automatic and large-scale surface water extraction to support disaster management activities in the context of hydrological extreme events on a global level. The system consists of several cloud-based, automated processing chains that uses convolutional neural networks (CNN) to extract the surface water extent from radar and multi-spectral satellite data (e.g. Sentinel-1/2, Landsat, TerraSAR-X, very high-resolution (VHR) optical satellite missions). Within this contribution, four application examples of the proposed system in the frame of floods and hydrologic drought events are given.

Current research and development efforts at DLR`s Center for Satellite Based Crisis Information (ZKI) focus on deploying automated image analysis methods as part of rapid mapping processing routines. The use of machine learning methods enables processing of large amounts of heterogeneous satellite, aerial and drone images at varying spatial scales and temporal frequencies. In this work, we introduce an automated and scalable image processing chain for rapid building damage assessment, optimize it for inference on different hardware and provide application examples from recent natural disasters. We show the scalability of the method from high-frequency live-mapping with drones on a laptop to large-scale processing of satellite and aerial images on a high-performance computing cluster.

A key factor in successful flood mapping is the fast and easy access to water extent information at normal, non-flood conditions. In this study, we propose a fully automated and scalable methodology for generating and publishing information on permanent and seasonal surface water extent by leveraging cloud technologies such as Docker and Kubernetes. Products can be computed at global scale and made accessible free of cost via standardized interfaces. With this approach, the disaster response community benefits from easy data access and flexible integration into automated rapid mapping workflows. To showcase our approach, we generate reference water information in the scope of the 2022 flooding in Pakistan.

In October 2021 and 2022 two field exercises, led by the Bavarian Red Cross (BRK) and German Aerospace Center (DLR) took place in the Ahr Valley, Germany, which was hit by severe floods in July 2021. Focus thereby was the use of Unmanned Aerial Vehicles (UAV) for crisis mapping to enhance situational awareness for first responders in disaster management. The case study highlights the benefits of decentralized UAV data acquisition, processing, and transfer. This includes subsequent mapping of flooded areas as well as automatically detecting buildings and vehicles using machine learning workflows. Additionally, navigable and analyzable 3D scenes are calculated from the drone data and provided to the first responders. The exercise in the Ahr Valley showed that the acquisition of structured UAV data can improve situational awareness, disaster management strategies, and response capabilities, leading to more effective and efficient emergency management.

Recent advances in machine learning and the rise of new large-scale remote sensing datasets have opened new possibilities for automation of remote sensing data analysis that make it possible to cope with the growing data volume and complexity and the inherent spatio-temporal dynamics of disaster situations. In this work, we provide insights into machine learning methods developed by the German Aerospace Center (DLR) for rapid mapping activities and used to support disaster response efforts during the 2021 flood in Western Germany. These include specifically methods related to systematic flood monitoring from Sentinel-1 as well as road-network extraction, object detection and damage assessment from very high-resolution optical satellite and aerial images. We discuss aspects of data acquisition and present results that were used by first responders during the flood disaster.

In order to support disaster management activities related to flood situations, an automatic system for near-real time mapping of flood extent and duration using multi-sensor satellite data is developed. The system is based on four processing chains for the automatic derivation of the inundation extent from Sentinel-1 and TerraSAR-X radar as well as from optical Sentinel-2 and Landsat data. While the systematic acquisition plan of the Sentinel-1/2 and Landsat satellites allows a continuous monitoring of inundated areas at an interval of a few days, the TerraSAR-X processing chain has to be triggered on-demand over the disaster-affected areas. Beyond flood extent masks, flood duration products are generated to indicate the temporal stability and evolution of flood events. The flood monitoring system is demonstrated on a severe flood situation in Mozambique related to cyclone Idai in 2019.

Local governments are often unable to keep track of the building inventory exposed to seismic hazard, due to high urbanization rates and an increasingly high spatio-temporal variability in many present-day cities. In order to provide a time- and cost-efficient approach to estimate building inventory and thus structural vulnerability that allows for assessing and monitoring seismic risk on different scales over large areas, the use of satellite remote sensing in combination with ground-based omnidirectional imaging is tested and applied. Latest image processing and statistical learning algorithms are used on multiple imaging sources in the framework of an integrated sampling scheme. Each imaging source and technique is used to capture specific, scale-dependent information of the exposed building stock. Preliminary results from an application within the Earthquake Model Central Asia (EMCA) are presented.