Accurate flood delineation is crucial in many disaster management tasks, such as risk map production and update, impact estimation, claim verification, or planning of countermeasures for disaster risk reduction. Open remote sensing resources such as the data provided by the Copernicus ecosystem enable to carry out this activity, which benefits from frequent revisit times on a global scale. In the last decades, satellite imagery has been successfully applied to flood delineation problems, especially considering Synthetic Aperture Radar (SAR) signals. However, current remote mapping services rely on time-consuming manual or semi-automated approaches, requiring the intervention of domain experts. The implementation of accurate and scalable automated pipelines is hindered by the scarcity of large-scale annotated datasets. To address these issues, we propose MMFlood, a multimodal remote sensing dataset purposely designed for flood delineation. The dataset contains 1,748 Sentinel-1 acquisitions, comprising 95 flood events distributed across 42 countries. Along with satellite imagery, the dataset includes the Digital Elevation Model (DEM), hydrography maps, and flood delineation maps provided by Copernicus EMS, which is considered as ground truth. To provide baseline performances on the MMFlood test set, we conduct a number of experiments of the flood delineation task using state-of-art deep learning models, and we evaluate the performance gains of entropy-based sampling and multi-encoder architectures, which are respectively used to tackle two of the main challenges posed by MMFlood, namely the class unbalance and the multimodal setting. Lastly, we provide a future outlook on how to further improve the performance of the flood delineation task. Dataset and code can be found at https://github.com/edornd/mmflood .

Floods are major natural disasters which cause deaths and material damages every year. Monitoring these events is crucial in order to reduce both the affected people and the economic losses. In this work we train and test three different Deep Learning segmentation algorithms to estimate the water area from river images, and compare their performances. We discuss the implementation of a novel data chain aimed to monitor river water levels by automatically process data collected from surveillance cameras, and to give alerts in case of high increases of the water level or flooding. We also create and openly publish the first image dataset for river water segmentation.

In the last years, we are witnessing an increasing availability of geolocated data, ranging from satellite images to user generated content (e.g., tweets). This big amount of data is exploited by several cloud-based applications to deliver effective and customized services to end users. In order to provide a good user experience, a low-latency response time is needed, both when data are retrieved and provided. To achieve this goal, current geospatial applications need to exploit efficient and scalable geospatial databases, the choice of which has a high impact on the overall performance of the deployed applications. In this paper, we compare, from a qualitative point of view, four state-of-the-art SQL and NoSQL databases with geospatial features, and then we analyze the performances of two of them, selecting the ones based on the Database-as-a-service (DBaaS) model: Azure SQL Database and Azure DocumentDB (i.e., an SQL database versus a NoSQL one). The empirical evaluation shows pros and cons of both solutions and it is performed on a real use case related to an emergency management application.

Early structural anomalies identification allows to hold maintenance activities that avoid loss of both economic resources and human life. This is extremely important for crucial infrastructures like railway bridges. This paper illustrates the structural health monitoring approach applied to a simply supported prestressed concrete railway bridge. In the framework of long-term monitoring, both static quantities (displacements, strains, and rotations) and environmental measurements (temperatures) have been recorded. Machine learning techniques, Extreme Gradient boosting machine and Multi-Layer Perceptron, have been exploited to build regression correlation models associated with the undamaged structural condition after adequate pre-processing operations. In this way, alarm thresholds based on the expected residuals between the predicted structural quantities and the measured ones, have been defined. The thresholds turned out to be able to catch early-stage anomalies not pointed out by traditional damage thresholds based on the design values. The proposed damage index is chosen as the moving median of the residuals, allowing a significant reduction of false alarms. The used correlation models and the obtained results represent a starting point for the generalization of this approach to the bridges belonging to the same static typology.