This project aims at developing an AI system to provide a reliable assessment of the structural damage caused by wildfires in the first burn period. Our approach uses multimodal data, including multispectral aerial images, historical post-fire damage assessment data, and building footprints, to create an association between damage data and structure footprints. We use these associations to generate features and use machine learning methods to assess the level of damage to structures. The resulting AI-driven system can be used to provide wildfire-induced structural damage assessments in near-real-time using only aerial images for future fires. We provide damage assessment results on several megafires in California to demonstrate the applicability of our approach to real wildfire scenarios.

Timely prediction of debris flow probabilities in areas impacted by wildfires is crucial to mitigate public exposure to this hazard during post-fire rainstorms. This paper presents a machine learning approach to amend an existing dataset of post-fire debris flow events with additional features reflecting existing vegetation type and geology, and train traditional and deep learning methods on a randomly selected subset of the data. The developed methods achieve AUC (area under the receiver operational characteristic curve) values of 0.93 (random forest) and 0.92 (neural network) on the test set, representing a significant improvement over a logistic regression model currently used (AUC 0.79). The paper also overviews a distributed, Kubernetesbased big data processing pipeline to efficiently retrieve features in areas impacted by new fires, and deploy the methods for real-time prediction of debris flow hazards.

High-resolution satellite imagery is a rich source of data applicable to a variety of domains, ranging from demo-graphics and land use to agriculture and hazard assessment. We have developed an end-to-end analysis pipeline that uses deep learning and unsupervised learning to process high-resolution satellite imagery and have applied it to various applications in previous work. As high-resolution satellite imagery is large-volume data, scalability is important to be able to analyze data from large geographical areas. To add scalability to our process, we converted our original pipeline, implemented using the Caffe deep learning library and the Python machine learning library Scikit-Learn, to other platforms that make use of distributed computation. Specifically, to add scalability, we use Keras for deep learning, and evaluate two different distributed platforms, Spark and Dask, for unsupervised learning. We report on results in scaling up our satellite analysis pipeline.

Land cover classification analysis from satellite imagery is important for monitoring change in ecosystems and urban growth over time. However, the land cover classifications that are widely available in the United States are generated at a low spatial and temporal resolution, so that the spatial distribution between vegetation and urban areas in the wildland urban interface is difficult to measure. High spatial and temporal resolution analysis is essential for understanding and managing changing environments in these regions. This paper describes an end to end satellite data ingestion and analysis pipeline using deep learning on high resolution satellite imagery for generating pixel-based land cover classification.

Advances in satellite imagery presents unprecedented opportunities for understanding natural and social phenomena at global and regional scales. Although the field of satellite remote sensing has evaluated imperative questions to human and environmental sustainability, scaling those techniques to very high spatial resolutions at regional scales remains a challenge. Satellite imagery is now more accessible with greater spatial, spectral and temporal resolution creating a data bottleneck in identifying the content of images. Because satellite images are unlabeled, unsupervised methods allow us to organize images into coherent groups or clusters. However, the performance of unsupervised methods, like all other machine learning methods, depends on features. Recent studies using features from pre-trained networks have shown promise for learning in new datasets. This suggests that features from pre-trained networks can be used for learning in temporally and spatially dynamic data sources such as satellite imagery. It is not clear, however, which features from which layer and network architecture should be used for learning new tasks. In this paper, we present an approach to evaluate the transferability of features from pre-trained Deep Convolutional Neural Networks for satellite imagery. We explore and evaluate different features and feature combinations extracted from various deep network architectures, and systematically evaluate over 2,000 network-layer combinations. In addition, we test the transferability of our engineered features and learned features from an unlabeled dataset to a different labeled dataset. Our feature engineering and learning are done on the unlabeled Draper Satellite Chronology dataset, and we test on the labeled UC Merced Land dataset to achieve near state-of-the-art classification results. These results suggest that even without any or minimal training, these networks can generalize well to other datasets. This method could be useful in the task of clustering unlabeled images and other unsupervised machine learning tasks.