Optimizing the Use of Drones in Humanitarian Endeavors

Leveraging AI models for feature detection in the humanitarian sector presents valuable opportunities, with customized applications for disaster response, including the identification of damaged structures, and the use of AI-equipped drones

FREMONT, CA: Widely recognized for their role in defense, Unmanned Aerial Vehicles (UAVs) have presently transcended their application to include humanitarian ventures. Remote-controlled or autonomously operated, these aircraft’s use in civilian sectors is attributed to the advances in hardware including increased potent batteries, image quality, and capability to transport heavier loads.

The increased adoption of drones in disaster emergency response plans is driven by their proven benefits. The six key categories where there is a burgeoning drone utilization are mapping, delivering supplies to underserved areas, search and rescue operations, damage assessment, monitoring changes in various scenarios, and situational awareness. 

By reaching disaster sites ahead of humans, UAVs share vital information to ensure the safety of first responders. Drones navigate areas inaccessible to humans, flying under clouds and approaching affected regions closely. Their compact size and diverse camera capabilities enable precise analysis of endangered areas. Its adeptness in accessing challenging territory permits it to promptly impart live data, making it brisk compared to its human counterparts. 

Automated detections are a necessity for disaster response and disaster risk management. Drones, acknowledged as indispensable apparatus for swift and accurate evaluation of post-disaster damage aid in minimizing the time and effort for analysis and retrieving data with mapping technology. 

The gathering of high-resolution data necessitates a speedy analysis and conversion of information into action. Following disasters like hurricanes, floods, earthquakes, or wildfires, the integration of machine learning (ML) in drones facilitates swift and accurate damage assessment and decision-making.  Subsequently, budgets allocated to UAVs in disaster response have surged, permitting first responders to collaborate with innovative private companies that develop AI models specifically for drone use in disaster response.

Computer vision, on the other hand, enables visualization and analysis of shifts in urban and camp expansion, agricultural activities, or infrastructure development. The captured images are eventually used to extract meaningful information enabling innovative functionalities like automated mapping, object/individual recognition, and motion analysis.

Furthermore, drones help reduce risk by aiding in flood modeling, vulnerability mapping, detection of landmines, identification of non-timber trees, and mosquito eradication. The dynamic use of AI algorithms and technology also contributes to better-informed emergency alerts and urgent measures.

While satellite imagery and drone imagery are valuable, challenges such as limited availability, overcast skies, and restricted local drone capacity persist. Drones equipped with state-of-the-art technology offer high-resolution imagery, but their coverage is constrained by battery limits. Likewise, though satellite imagery covers larger areas, it lacks the required resolution for detailed analysis. Efforts are being made to bridge this gap, with companies working on making satellite imagery more affordable and developing ML algorithms that produce higher-resolution imagery from lower-resolution sources. 

The ongoing advancements in UAVs, satellite technology, and ML signals a promising future for improved disaster response capabilities. The democratization of these technologies, facilitated by both private companies and internal tools within humanitarian organizations, indicates a continued expansion of opportunities in this field.