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.

Southern California's dry climate and fire-prone vegetation make the area vulnerable to extreme wildfire conditions. These conditions are exacerbated by Santa Ana weather patterns, which are characterized by very low humidity and gusty winds blowing in from the deserts. We present an approach using unsupervised learning to model and detect Santa Ana conditions based on sensor measurements from weather stations. Our approach uses cluster analysis to capture weather patterns specific to the region surrounding each weather station. A method is provided to automatically determine the Santa Ana cluster for each cluster model using dynamic, data-driven criteria. The resulting cluster models are applied to real-time sensor measurements to provide location-specific and time-specific detection of Santa Ana conditions. The Spark distributed platform is leveraged to scale the system to large datasets from multiple weather stations, and the Kepler workflow system is used to provide a GUI-based, easy-to-use interface to the underlying system. Results of testing our approach on an existing network of weather stations are presented. Our scalability experiment shows that the approach can process up to one million live sensor measurements in less than one minute on one machine. The proposed system can be used to aid in wildfire management and prevention by focusing firefighting efforts on regions with increased wildfire risks.

Santa Ana conditions are hot, dry, windy weather conditions that can greatly increase the dangers of wildfires in southern California. We present a machine learning approach to detect Santa Ana conditions based on sensor measurements from weather stations. Cluster analysis is performed on historical weather data to build models to identify Santa Ana patterns. A separate model is built using data from each weather station to capture the patterns specific to the microclimate of each region. Real-time sensor data from a weather station can then be processed to determine if the region surrounding that station is experiencing Santa Ana conditions. Results can be used as a warning system to focus firefighting efforts on regions with increased wildfire risks. Through the use of the Kepler workflow system and distributed computing with Spark, data from several weather stations can be processed in parallel using a scalable clustering algorithm, allowing our approach to scale to large datasets from multiple weather stations.

Ability to track provenance is a key feature of scientific workflows to support data lineage and reproducibility. The challenges that are introduced by the volume, variety and velocity of Big Data, also pose related challenges for provenance and quality of Big Data, defined as veracity. The increasing size and variety of distributed Big Data provenance information bring new technical challenges and opportunities throughout the provenance lifecycle including recording, querying, sharing and utilization. This paper discusses the challenges and opportunities of Big Data provenance related to the veracity of the datasets themselves and the provenance of the analytical processes that analyze these datasets. It also explains our current efforts towards tracking and utilizing Big Data provenance using workflows as a programming model to analyze Big Data.