Recent advances in search and rescue methods include the use of unmanned aerial vehicles (UAVs), to carry out aerial monitoring of terrains to spot lost individuals. To date, such searches have been conducted by human observers who view UAV-acquired videos or images. Alternatively, lost persons may be detected by automated algorithms. Although some algorithms are implemented in software to support search and rescue activities, no successful rescue case using automated human detectors has been reported on thus far in the scientific literature. This paper presents a report from a search and rescue mission carried out by Bieszczady Mountain Rescue Service near the village of Cergowa in SE Poland, where a 65-year-old man was rescued after being detected via use of SARUAV software. This software uses convolutional neural networks to automatically locate people in close-range nadir aerial images. The missing man, who suffered from Alzheimer’s disease (as well as a stroke the previous day) spent more than 24 h in open terrain. SARUAV software was allocated to support the search, and its task was to process 782 nadir and near-nadir JPG images collected during four photogrammetric flights. After 4 h 31 min of the analysis, the system successfully detected the missing person and provided his coordinates (uploading 121 photos from a flight over a lost person; image processing and verification of hits lasted 5 min 48 s). The presented case study proves that the use of an UAV assisted by SARUAV software may quicken the search mission.

A new system for computing the multimodel ensemble predictions of water levels, known as HydroProg, is applied in the process of real-time modelling and forecasting riverflow in the upper Nysa Kłodzka basin (SW Poland). The HydroProg system automatically produces early warnings against high flows on a basis of prognoses from external hydrologic models which are run in a cloud-like fashion using the real-time hydrometeorological observations. The predictions offered by the models serve as ensemble members and become inputs to HydroProg which does data pre- and post-processing as well as computes multimodel ensemble predictions. The HydroProg system is used in this paper to verify the operational hypothesis that a two-model ensemble hydrologic prediction, solely based on two prognoses computed from the multi- and univariate autoregressive statistical models, reveals better skills than the individual members. The analysis is conducted for all lead times and selected phases of high flow development, including a rising limb of hydrograph but excluding a peak flow. The investigation is carried out using the first HydroProg prototype that works experimentally for the upper Nysa Kłodzka basin (SW Poland). The implementation offers 3-h predictions of water levels, based on the regularly (15 mins) calibrated and updated individual models (the emphasis is put on two models, but other models are also presented for reference) which become inputs to the multimodel ensemble solution (mainly the two-model approach, with the six-model solution presented for comparison). The most significant high flows that occurred in the study area between 01/09/2013 and 31/08/2015 have been investigated. It has been found that for slightly rising water level the two-model ensemble is recommended which, for a subsequently moderately or rapidly rising limb of hydrograph, should be replaced by the vector autoregressive model.