Drone Imagery: From Capture to Automated GeoAI Analysis

Reality mapping is defined as a process of creating a spatially accurate 2D and 3D digital representation of the physical world using images or lidar. It’s very beneficial when using geographic information system (GIS) technology for establishing foundational content—where it isn’t available yet—to gain a realistic view of critical sites and begin problem-solving. So by using drones to capture current imagery, we gain real-world context for spatial data. We can then layer additional data points over the digital representation for deeper analysis and to facilitate a holistic view for a wide variety of stakeholders.

Geospatial artificial intelligence (GeoAI) is the integration of artificial intelligence (AI) with spatial data, science, and technology to increase understanding, solve spatial problems, and automate information extraction.

With ArcGIS, you can analyze a wide variety of imagery—anything from a point cloud to oriented imagery to video. Perform a variety of tasks with imagery including change detection, site suitability, vegetation monitoring, object detection, image redaction, or feature extraction and classification.

For public and commercial property management, organizations can use still or motion imagery to detect, classify, and count vehicles to learn when parking lots reach capacity. For utility right-of-way monitoring, point clouds can be used to classify utility assets and identify vegetation encroachments in the network to help prevent wildfires. In environmental research, orthoimagery or video imagery and classification algorithms can detect changes in animal populations over time to understand how they are evolving.

The time-saver: Pretrained AI models
Esri now provides over 40 pretrained deep learning models that are ready to use in ArcGIS Living Atlas of the World. These pretrained deep learning models eliminate the need for huge volumes of training data, massive compute resources, and extensive artificial intelligence (AI) knowledge. They enable you to accelerate your geospatial workflows with built-in expertise and resources designed specifically for image feature extraction, land-cover classification, image redaction, and object detection. Automate the way you extract meaningful insights from imagery, point clouds, and video.

Pretrained deep learning models from Esri include car detection, building detection, land-cover classification, power line detection, and pavement crack detection. Simply grab a pretrained model of your choice and run it on your imagery using ArcGIS Image for ArcGIS Online or ArcGIS Pro. You can then fine-tune it to fit your needs and location. No training is needed.

Watch the following demo videos to learn how AI and drone imagery are being used in different industries to solve complex problems.

Electric transmission towers use insulators to prevent the leakage of current from the conductors to the ground. Power corporations perform regular inspections of transmission and distribution infrastructure to ensure safety and efficiency. 

This video demonstrates how to utilize drone images with Esri’s deep learning model to detect insulators and classify defects. By identifying the visual features of insulators, the model can accurately locate and classify them within complex backgrounds, and quickly assign remote crews to fix them.

The use of this technology enables automated inspection of power lines on a large scale, allowing for rapid inspections without manual intervention. This not only reduces inspection time and costs, but also enhances safety measures in the maintenance of electrical infrastructure.