Our client is a top research institution dedicated to studying and protecting the world’s natural water resources. Their research focuses on tracking changes in water bodies over time, providing a deeper understanding of environmental shifts. The insights of this study are intended to support the client’s water conservation efforts. To improve these efforts, the client is developing an AI-powered geospatial mapping solution to analyze water bodies more accurately and efficiently.
To train the AI/ML solution for automated map analysis, the client needed accurately annotated historical maps depicting water features such as rivers, lakes, and reservoirs. The specific requirements included:
The project presented several significant challenges for our team due to the nature of the source material and the complexity of the task:
To address the client’s needs and overcome project challenges, we delegated a team of Accuracy in Geographic Data Annotationthree image annotators and employed a strategic combination of technical solutions, manual expertise, and digital enhancement techniques. We took care of:
We developed a detailed set of annotation guidelines specific to geographic data mapping. These guidelines included instructions on handling different cartographic styles, overlapping objects, and unclear map images, ensuring consistency across all annotations.
Our photo editing experts used advanced digital restoration techniques to enhance the clarity of faded or degraded maps. This process involved adjusting contrast and sharpening edges to bring out subtle details for accurate GIS annotations.
By utilizing the customizable CVAT tool and polygon annotation technique, we precisely labeled waterbodies and their features in vintage maps.
For automating common annotation tasks, such as detection of potential water features based on color and pattern recognition, we ran custom scripts within the CVAT tool.
We implemented a rigorous, multi-stage review process where multiple annotators and quality auditors validated annotations to eliminate errors and maintain consistency.
We successfully delivered a precisely annotated dataset of 1,200 historical maps, providing a robust foundation for AI/ML model development and research. Our GIS annotation services helped client achieve remarkable results, such as:
The annotated datasets enhanced the AI model’s ability to detect water bodies more accurately.
The model demonstrated superior performance in recognizing and classifying various types of water bodies across historical maps.
High-quality training data enabled faster model development, supporting the client’s research timelines and objectives.
We specialize in handling annotation project challenges, like low-quality images, intricate geometries, dense scenes, or hard-to-label objects, while meeting tight production deadlines. To get a free quote or discuss your requirements for our data labeling services, contact us at info@suntec.ai.
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