Human life is precious and in the event of any unfortunate occurrence, highest efforts are made to safeguard it. To provide timely aid or undertake extraction of humans in distress, it is critical to accurately locate them. There has been an increased usage of drones to detect and track humans in such situations. Drones are used to capture high resolution images during search and rescue purposes. It is possible to find survivors from drone feed, but that requires manual analysis. This is a time taking process and is prone to human errors. This model can detect humans by looking at drone imagery and can draw bounding boxes around the location. This model is trained on IPSAR and SARD datasets where humans are on macadam roads, in quarries, low and high grass, forest shade, and Mediterranean and Sub-Mediterranean landscapes. Deep learning models are highly capable of learning complex semantics and can produce superior results. Use this deep learning model to automate the task of detection, reducing the time and effort required significantly.Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Fine-tuning the modelThis model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.InputHigh resolution (1-5 cm) individual drone images or an orthomosaic.OutputFeature class containing detected humans.Applicable geographiesThe model is expected to work well in Mediterranean and Sub-Mediterranean landscapes but can also be tried in other areas.Model architectureThis model uses the FasterRCNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThis model has an average precision score of 82.2 percent for human class.Training dataThis model is trained on search and rescue dataset provided by IPSAR and SARD.LimitationsThis model has a tendency to maximize detection of humans and errors towards producing false positives in rocky areas.Sample resultsHere are a few results from the model.

Human life is precious and in the event of any unfortunate occurrence, highest efforts are made to safeguard it. To provide timely aid or undertake extraction of humans in distress, it is critical to accurately locate them. There has been an increased usage of drones to detect and track humans in such situations. Drones are used to capture high resolution images after natural and manmade disasters. It is possible to find survivors from drone feed, but that requires manual analysis. This is a time taking process and is prone to human errors. This model is capable of detecting humans by looking at drone imagery and can draw bounding boxes around their exact location. Deep learning models are highly capable of learning complex semantics and can produce superior results. Use this deep learning model to automate the task of detection, reducing time and effort required significantly.Licensing requirementsArcGIS Desktop – ArcGIS Image Analyst extension for ArcGIS ProArcGIS Enterprise – ArcGIS Image Server with raster analytics configuredArcGIS Online – ArcGIS Image for ArcGIS OnlineUsing the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Note: Deep learning is computationally intensive, and a powerful GPU is recommended to process large datasets.InputHigh resolution (1-5 cm) individual drone images or an orthomosaic.OutputFeature class containing detected humansApplicable geographiesThe model is expected to work well in coastal areas of Africa but can also be tried in other areas.Model architectureThis model uses the FasterRCNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThis model has an average precision score of 72.8 percent for humans and 67.1 for possibly a human class.Limitations • This model has a tendency to maximize detection of humans and errs towards producing false positives. • It has been noticed that a few features get missed when a cluster of features is reported.Sample results