Image tagging of aerial imagery is a powerful tool used in many professional fields for data collection and analysis. It refers to the process of adding metadata (labels) to satellite or drone imagery that can be used for object identification, shapes, dimensions, and other characteristics. This information can then be analyzed and mined to generate meaningful insights. This technique allows users to easily search large datasets with improved accuracy and quicker turnaround time than manual methods. It is becoming increasingly popular in a variety of applications such as military resource management, urban planning, infrastructure management, natural resource monitoring, and even medical research. As technology continues to advance, it is expected that image tagging of aerial imagery will become an indispensable tool in identifying patterns, predicting outcomes and informing decision making processes across all sectors.
One another area with a lot of potential to grow is the Image Tagging of Aerial Imagery where the image annotation done on aerial imagery of all kinds. This helps the clients to analysis of aerial images in diverse industries like agriculture, real estate, aerospace etc. The future world will be more capable of analysing sensory intelligence to helps humans to identify the content of the images like What the image is comprised of? Where are the objects located from an aerial/drone images? resemblance of images and differences. These questions are answered by data annotation, machines have the ability to read and understand the images to provide solutions. Artificial intelligence and computer vision technology are built by creating algorithms that can easily identify objects in the images and train them.
For example, annotation of an aerial street image can be done by marking various images with various colors and shapes. Each vehicle, pedestrian, traffic signal is given a color and annotated. This processed image is then fed to the program for comparing the similarities and differences. The type of visual features are recognized respective images are picked. The computer vision and AI program autonomously identifies relevant and irrelevant objects depending on the purpose of the program.