Pre-processing spatial data for machine learning applications often includes combining
different datasets into a form usable by the machine learning algorithms. Spatial data is …

Designing effective emergency response management (ERM) systems to respond to
incidents such as road accidents is a major problem faced by communities. In addition to …

Spatial data is traditionally represented using two data models, raster and vector. Raster
data refers to satellite imagery while vector data includes GPS data, Tweets, and regional …

Increasing wildfire risks have significantly disrupted supply chain systems in the United
States, which accounts for a large portion of wildfire-induced economic losses and affects …

Birth and death Markov processes can model stochastic physical systems from percolation
to disease spread and, in particular, wildfires. We introduce and analyze a birth-death …

Well-executed emergency evacuations can save lives and reduce suffering. However,
decision makers struggle to determine optimal evacuation policies given the chaos …

In this work, we consider a team of heterogeneous robots equipped with various types and
quantities of resources, and tasked with supplying these resources to multiple dynamic …

Modeling fire spread is critical in fire risk management. Creating data-driven models to
forecast spread remains challenging due to the lack of comprehensive data sources that …

There has been an increase in the amount of spatial data in the recent years due to the
advancements in remote sensing technology and the widespread use of smart phones and …

Modeling fire spread is critical in fire risk management. Creating data-driven models to
forecast spread remains challenging due to the lack of comprehensive data sources that …