Humans have always populated in the vicinity of river systems, where thesupply of water, nourishment and transportation is obtained from the river.However, inundation is a re-occurring problem and impact of floods are ex-pected to increase due to climate change. Accurate flood forecasting andearly warning is critical for disaster risk management. Tackling the problemof forecasting, in data scarce environments, has become increasingly impor-tant due to the changing climate. Remotely sensed river monitoring can bean effective, systematic and time-efficient technique to monitor and forecastextreme floods. Conventional flood forecasting systems require extensivedata inputs and software to model floods. Moreover, most models rely ondischarge data, which is not always available and is less accurate in a over-bank flow situations. There is a need for an alternative method which de-tects riverine inundation, using open-source data and software. This thesisaims to research the use of passive microwave radiometry for the detection,classification and forecasting of inundation.Brightness temperatures are extracted from the passive microwave radiom-etry and are converted in a discharge estimator: the C/M-ratio. Surfacewater has a low emission, thus let the C/M-ratio increase as the surfacewater percentage in the pixel increases. Sharp increases are observed forover-bank flow conditions. The research combines the identification of in-undation with a probability analysis via a quantile regressional fit. Floodforecasts can be obtained from an upstream catchment area. In the mostideal situation with a delay of2,5hours. This allows for probabilistic earlywarning decision making, with a lead time up to14days. (location specific)Strong Spearmans correlation coefficients between the discharge and C/M-ratio are found (>0.883). Allowing the model to forecast floods as gaugeddischarge records do. The model used has a comparable skill to the localGloFas forecast. This research investigated the impact the remote sensedtechnology could have on the flood forecast, response and warning system.An added model to an Early Action Protocol has the ability to lower uncer-tainty within decision making and enlarges the intervention window. Theadvice is to use such a model in combination with other forecasting modelssuch as GloFas.The challenge using this technology is the integration of hydrological com-plexity. The method allows for automated, global-covered creation of gridbased flood forecasts, independent to cloud coverage. Creating low spatialresolution flood forecasts combined with a probability bound in hours aftersatellite detection. The method has a high potential for data scarce flood-prone river basins around the world. The future for this technology lies inthe global daily availability of the data. With satellite sensors improving,spatial resolution is expected to increase. Allowing for even better floodforecasting ability.

Situation: Information management enables humanitarian organizations to make adequate interventions based on timely, appropriate and trustworthy information. A crucial type of information are identities, because they can be used to assess vulnerability and efficiently manage aid distribution. Vulnerability determines who receives aid first because resources are always limited. This information is increasingly being stored and processed in identity systems.
Complication: Most identity systems are centralized and produce analogue proofs of identity such as passports or ID cards. These systems are susceptible to privacy and data breaches. Centralization leads to single-points-of-failure and could lead to fraudulent behavior resulting in people lacking formal proofs of identity. In general there is limited interoperability between identity systems and limited collaboration between the owners of these systems.
Approach: To create an interoperable and shared digital identity system using a Design Science Research strategy and systems engineering approach. This system must be distributed, protect privacy and put the identity owner in control of his or her data. The foundation of the system consists of Humanitarian Information Management principles, Privacy-by-Design principles and Self-Sovereign Identity principles. This research creates a functional blockchain based system, that enables identities for the use-case of Cash Transfer Programs.
Results: We present a validated set of ten design decisions that represent the trade-offs that have been made and prescribe a blueprint for a technical design.
Next steps:Future research should be done on how such a system could be implemented and used. This would require a process design approach that has to be developed, Also, elaborate research into user experience and user interfaces should be conducted.

In the second half of the 20th and beginning of the 21st century the amount of natural disasters has increased rapidly. Due to this rise in occurrences, more people are affected. An important indicator for people affected is the amount of damage to buildings. To gather this information aid workers now have to go into the field to gather data on the amount of destruction. In response to the possible dangers these people encounter in the field, remote sensing and analysis techniques have been developed for automated damage detection. However, due to various limitations on the implementation, these techniques are not yet widely adopted in emergency response and humanitarian aid.

This work compares two methods and two data sources for the detection of building damage. The methods are evaluated on their accuracy and implementability within humanitarian aid in disaster situations. The main methods considered are equalisation of histograms of pre-event and post-event imagery, followed by Univariate Image Differencing; and a convolutional neural network on features withdrawn from post-event imagery, using OpenStreetMap data. Remotely sensed data sources considered are synthetic aperture radar and very high resolution optical imagery. All results are analysed and compared to current standards in damage detection.

From the results it can be concluded that more research is required for a practical implementation of deep learning techniques. The constraint posed by the requirement of large datasets, make these methods impracticable without sufficient preparation and resources. More simpler methods, like Univariate Image Differencing, can be validated on smaller ground-truth datasets, and are therefore easier in implementation when resources are limited. The possible accuracy increase of deep learning methods does, at this moment, not outweigh the ease of an elementary differencing approach.