This is the software used at the time of publication for the article titled, "Remote Sensing Improves Multi-Hazard Flooding and Extreme Heat Detection by Fivefold over Current Estimates." The abstract for this submission detailing the purpose of this study is as follows: The co-occurrence of multiple hazards is of growing concern globally as the frequency and magnitude of extreme climate events increases. Despite studies examining the spatial distribution of such events, there has been little work in examining if all relevant life threatening and damaging hazards are captured in existing hazard databases and by common hazard metrics. For example, local/regional flash flooding events are seldom captured by optical satellite instruments and are subsequently excluded from global hazard databases. Similarly, the heat hazard definitions most frequently used in multi-hazard studies inherently fail to capture events that are life-threatening but climatologically within an expected range. Our goal is to determine the potential for increasing multi-hazard event detection capabilities by inferring additional hazard footprints from widely accessible satellite data. We use daily precipitation and temperature satellite data to develop an open-source framework that infers additional hazard footprints that are not included in traditional methods. With the state of Texas as our study area, we detected 2.5 times as many flood hazards, equivalent to $320 million in property and crop damages. Furthermore, our expanded heat hazard definition increases the impacted area by 56.6%, equivalent to 91.5 million km^2 over an 18 year period. Increasing hazard detection capabilities and expanding existing definitions of hazards using daily satellite data increases the temporal and spatial resolutions at which multi-hazard events are detected. Having more complete datasets of all relevant hazard extents improves our ability to track global trends and more accurately determine the magnitude of hazard exposure inequities.