Thermal Detection for Free Flight

Abstract

Thermals are regions of rising hot air formed on the ground through the warming of the surface by the sun. Thermals are commonly used by birds and glider pilots to extend flight duration, increase cross-country distance, or simply to conserve energy. This kind of powerless flight using natural sources of lift is called soaring. Once a thermal is encountered, the pilot flies in circles to keep within the thermal, so gaining altitude before flying off to the next thermal and towards the destination. A single thermal can net a pilot thousands of meters of elevation gain. Estimating thermal locations is not an easy task, pilots look for different indicators like color variation on the ground because the difference in the amount of heat absorbed by the ground varies based on the color/composition, birds circling in an area, and certain types of cloud formations (cumulus clouds). The above methods are not always reliable enough and pilots study the conditions for thermals by estimating solar heating of ground (cloud cover and time year/date) and also the lapse rate and dew point of air. In this paper, we present a Machine Learning based solution to forecast thermals. Since pilots in general record many of their flights locally and sometimes upload them to databases, we use the flight data uploaded to determine where the pilot encountered thermals and together with other information (weather and satellite images corresponding to the location and time of the flight) train an algorithm to automatically predict the location of thermals given as input the current weather conditions and terrain information (obtained from Google Earth Engine). Results show that our model is able to converge on the training and validation set with a loss bellow 1%.