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.

The Human Liver Model Market is projected to grow at 13.8% CAGR, reaching $4.41 Billion by 2029. Where is the industry heading next? Get the sample report now!

Human settlements maps are useful in understanding growth patterns, population distribution, resource management, change detection, and a variety of other applications where information related to earth surface is required. Human settlements classification is a complex exercise and is hard to capture using traditional means. Deep learning models are highly capable of learning these complex semantics and can produce superior results.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.InputRaster, mosaic dataset, or image service. (Preferred cell size is 10 meters.)Note: This model is trained to work on Sentinel-2 Imagery datasets which are in WGS 1984 Web Mercator (auxiliary sphere) coordinate system (WKID 3857).OutputClassified raster containing two classes: settlement and other.Applicable geographiesThis model is expected to work well in Europe.Model architectureThis model uses the UNet model architecture implemented in ArcGIS API for Python.Accuracy metrics This model has an overall accuracy of 94.1 percent.Sample resultsHere are a few results from the model.

Humanity's Last Exam

🌐 Website | 📄 Paper | GitHub Center for AI Safety & Scale AI

Humanity's Last Exam (HLE) is a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. Humanity's Last Exam consists of 2,700 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of… See the full description on the dataset page: https://huggingface.co/datasets/cais/hle.