Next Day Wildfire Spread: A Machine Learning Data Set to Predict Wildfire Spreading from Remote-Sensing Data

Predicting wildfire spread is critical for land management and disaster preparedness. To this end, we present `Next Day Wildfire Spread,' a curated, large-scale, multivariate data set of historical wildfires aggregating nearly a decade of remote-sensing data across the United States. In contrast to existing fire data sets based on Earth observation satellites, our data set combines 2D fire data with multiple explanatory variables (e.g., topography, vegetation, weather, drought index, population density) aligned over 2D regions, providing a feature-rich data set for machine learning. To demonstrate the usefulness of this data set, we implement a neural network that takes advantage of the spatial information of this data to predict wildfire spread. We compare the performance of the neural network with other machine learning models: logistic regression and random forest. This data set can be used as a benchmark for developing wildfire propagation models based on remote sensing data for a lead time of one day.Comment: submitted to IEEE Transactions on Geoscience and Remote Sensin

A scalable system to measure contrail formation on a per-flight basis

Persistent contrails make up a large fraction of aviation's contribution to global warming. We describe a scalable, automated detection and matching (ADM) system to determine from satellite data whether a flight has made a persistent contrail. The ADM system compares flight segments to contrails detected by a computer vision algorithm running on images from the GOES-16 Advanced Baseline Imager. We develop a 'flight matching' algorithm and use it to label each flight segment as a 'match' or 'non-match'. We perform this analysis on 1.6 million flight segments. The result is an analysis of which flights make persistent contrails several orders of magnitude larger than any previous work. We assess the agreement between our labels and available prediction models based on weather forecasts. Shifting air traffic to avoid regions of contrail formation has been proposed as a possible mitigation with the potential for very low cost/ton-CO2e. Our findings suggest that imperfections in these prediction models increase this cost/ton by about an order of magnitude. Contrail avoidance is a cost-effective climate change mitigation even with this factor taken into account, but our results quantify the need for more accurate contrail prediction methods and establish a benchmark for future development.Comment: 25 pages, 6 figure

The effect of uncertainty in humidity and model parameters on the prediction of contrail energy forcing

Previous work has shown that while the net effect of aircraft condensation trails (contrails) on the climate is warming, the exact magnitude of the energy forcing per meter of contrail remains uncertain. In this paper, we explore the skill of a Lagrangian contrail model (CoCiP) in identifying flight segments with high contrail energy forcing. We find that skill is greater than climatological predictions alone, even accounting for uncertainty in weather fields and model parameters. We estimate the uncertainty due to humidity by using the ensemble ERA5 weather reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF) as Monte Carlo inputs to CoCiP. We unbias and correct under-dispersion on the ERA5 humidity data by forcing a match to the distribution of in situ humidity measurements taken at cruising altitude. We take CoCiP energy forcing estimates calculated using one of the ensemble members as a proxy for ground truth, and report the skill of CoCiP in identifying segments with large positive proxy energy forcing. We further estimate the uncertainty due to model parameters in CoCiP by performing Monte Carlo simulations with CoCiP model parameters drawn from uncertainty distributions consistent with the literature. When CoCiP outputs are averaged over seasons to form climatological predictions, the skill in predicting the proxy is 44%, while the skill of per-flight CoCiP outputs is 84%. If these results carry over to the true (unknown) contrail EF, they indicate that per-flight energy forcing predictions can reduce the number of potential contrail avoidance route adjustments by 2x, hence reducing both the cost and fuel impact of contrail avoidance

Eagle Robotics Fire-Fighting Robot

The Eagle Robotics team has designed and built a robotic system to participate in the Trinity College Fire-Fighting Home Robot competition. The competition challenges teams of students to create a fully autonomous robot capable of starting at the sound of an alarm, navigating through a random maze of rooms, locating a lit candle, and extinguishing the flame. The team has implemented a variety of sensors that allow the robot to accurately navigate the maze in search of the fire. A 360o laser distance scanner allows the robot to track its location and orientation while scanning the environment for any obstacles that may be present. An array of infrared (IR) sensors continuously monitors light intensity to indicate the presence and direction of the fire relative to the robot. Finally, a servo controlled valve extinguishes the fire using compressed CO2. The competition poses an additional challenge by scoring teams based upon the ability to return to the starting position once the fire is extinguished. This is made possible through an advanced control algorithm that not only tracks the position, but stores the location as a digital map and allows the robot to achieve localization. Returning to the start location becomes as simple as following the map in reverse. Demonstration EAGLE PRIZE AWAR

A scalable system to measure contrail formation on a per-flight basis

Persistent contrails make up a large fraction of aviation's contribution to global warming. We describe a scalable, automated detection and matching (ADM) system to determine from satellite data whether a flight has made a persistent contrail. The ADM system compares flight segments to contrails detected by a computer vision algorithm running on images from the GOES-16 Advanced Baseline Imager. We develop a flight matching algorithm and use it to label each flight segment as a match or non-match. We perform this analysis on 1.6 million flight segments. The result is an analysis of which flights make persistent contrails several orders of magnitude larger than any previous work. We assess the agreement between our labels and available prediction models based on weather forecasts. Shifting air traffic to avoid regions of contrail formation has been proposed as a possible mitigation with the potential for very low cost/ton-CO2e. Our findings suggest that imperfections in these prediction models increase this cost/ton by about an order of magnitude. Contrail avoidance is a cost-effective climate change mitigation even with this factor taken into account, but our results quantify the need for more accurate contrail prediction methods and establish a benchmark for future development

AIAA Design Build Fly

Design-Build-Fly is an annual contest hosted by the American Institute for Aeronautics and Astronautics (AIAA), and sponsored by Cessna Aircraft Company and Raytheon Missile Systems. Design-Build-Fly challenges college students to create a small aircraft to meet a set of requirements. During the 2016-2017 academic year, more than 100 undergraduate student teams from around the world have been challenged to design, build, and flight test an aircraft which can carry a payload of hockey pucks and which can be folded to fit into a small storage tube. Eight months of designing and building will culminate in a 4-day fly-off in April in Tucson, AZ. This competition enables students to extend and demonstrate technical skills learned in the classroom, as well as develop practical teamwork skills. Eagle Prize Awar