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Gaining timely and reliable situation awareness after hazard events such as a hurricane is crucial to emergency managers and first responders. One effective way to achieve that goal is through damage assessment. Recently, disaster researchers have been utilizing imagery captured through satellites or drones to quantify the number of flooded/damaged buildings. In this paper, we propose a mixed‐data approach, which leverages publicly available satellite imagery and geolocation features of the affected area to identify damaged buildings after a hurricane. The method demonstrated significant improvement from performing a similar task using only imagery features, based on a case study of Hurricane Harvey affecting Greater Houston area in 2017. This result opens door to a wide range of possibilities to unify the advancement in computer vision algorithms such as convolutional neural networks and traditional methods in damage assessment, for example, using flood depth or bare‐earth topology. In this work, a creative choice of the geolocation features was made to provide extra information to the imagery features, but it is up to the users to decide which other features can be included to model the physical behavior of the events, depending on their domain knowledge and the type of disaster. The data set curated in this work is made openly available (DOI: 10.17603/ds2‐3cca‐f398). 

Gaussian processes offer a flexible kernel method for regression. While Gaussian processes have many useful theoretical properties and have proven practically useful, they suffer from poor scaling in the number of observations. In particular, the cubic time complexity of updating standard Gaussian process models can be a limiting factor in applications. We propose an algorithm for sequentially partitioning the input space and fitting a localized Gaussian process to each disjoint region. The algorithm is shown to have superior time and space complexity to existing methods, and its sequential nature allows the model to be updated efficiently. The algorithm constructs a model for which the time complexity of updating is tightly bounded above by a pre-specified parameter. To the best of our knowledge, the model is the first local Gaussian process regression model to achieve linear memory complexity. Theoretical continuity properties of the model are proven. We demonstrate the efficacy of the resulting model on several multi-dimensional regression tasks.