The development of the UASea toolbox is part of Ph.D. research in marine Remote Sensing. UASea is a toolbox that identifies the optimal flight times in a given day for an efficient UAS survey and the acquisition of reliable aerial imagery in the coastal environment. The suggestions are derived using weather forecast data and adaptive thresholds in a ruleset. The parameters that have been proven to affect the quality of UAS imagery and flight safety have been used as variables in the ruleset. The proposed thresholds are used to exclude inconsistent and outlier values that may affect the quality of the acquired images and the safety of the survey. Considering the above, the ruleset is designed in such a way that outlines the optimal weather conditions, suitable for reliable and accurate data acquisition as well as for efficient short-range flight scheduling.


To identify the optimal flight times for marine applications, the UASea toolbox uses short-range forecast data. In this context, we have used a) Dark Sky (DS) API for two days of forecast data on an hourly basis and b) Open Weather Map (OWM) API five days forecast with three-hour step. Both services provide a limited free-of-charge usage of their APIs; DS allows up to 1,000 free API calls per day and OWM provides 60 calls per minute. The forecast data are provided in lightweight and easy-to-handle JavaScript Object Notation (JSON) file format, on asynchronous API requests. DS API uses a great variety of data sources either globally such as NOAA’s GFS model, German Meteorological Office’s ICON model, or regionally such as NOAA's NAMM available in North America and aggregates them to provide a reliable and accurate forecast for any given location. OWM also uses several data sources such as NOAA GFS, ECMWF ERA, data from weather stations (companies, users, etc.) as well as satellite and weather radar data. Their numerical weather prediction (NWP) model was developed based on machine learning techniques.