Geospatial Data Annotation
for Geographic Models
We can help you build AI models that provide critical and actionable data for your business.
Make sure your AI product is trained with the most accurate geospatial data annotation
Geospatial data annotation involves preparing maps, satellite photos and other images with a wide variety of data annotation techniques. This usually includes things like information about the location, characteristics of objects, and many other attributes to allow AI systems to understand real-world phenomena to a specific geographical area.
This process usually involves annotating large datasets obtained from many diverse sources in varying formats. All of this data adds more context and makes various events easier to understand. The AI systems can identify visual patterns and images give insights that could be missed in a large spreadsheet. Predictions may become more precise, quicker, and easier as a result.
World-class expertise in Geospatial Annotation
This method involves drawing a set of coordinates around an image. It is best to use this technique to annotate objects that do not neatly fit into a square or rectangular box.
If you are looking for a very detailed type of annotation, then semantic segmentation would be a good choice. It is a deep learning technique that gives each pixel in a picture a label or category. It is employed to identify a group of pixels that make up different categories.
Data annotators use predetermined criteria to classify and identify groups of pixels or vectors inside a picture. It is possible to create the classification law using one or more textural or spectral properties.
Object tracking facilitates real-time or on-film object location determination. Algorithms for object tracking monitor an object's motion and give particular information about it. Each object is labeled from one frame to another to determine its geographic location.
Annotating LiDAR images involves semantic segmentation, object recognition and other types of annotation. To accurately name every point of an item with the same class, the annotator must perform a great deal of scene navigation and angle change observation.
Data annotators place points on certain areas inside the image to help locate an image's component pieces. It is very useful for small objects and shape variations by creating dots across the image.
Geospatial data annotation enables AI systems to gain a comprehensive grasp of particular themes, patterns, and trends on the surface of the Earth. It sheds light on spatial environments and how they affect both people and the physical world.
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We have a minimum threshold for starting any new project, which is 735 productive man-hours a month (equivalent to 5 graphic annotators working on the task monthly).