The Client

A Leading Academic Institution

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.

PROJECT REQUIREMENTS

Enhancing AI/ML Model Accuracy for Geographical Mapping with Accurate Image Annotation

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:

  • Image Annotation Services: Labeling various water bodies precisely in over 1200 historical map images.
  • Digital Restoration: Enhancing faded and low-quality map images to facilitate clear and accurate annotation.
PROJECT CHALLENGES

Handling Low-Quality Maps, Intricate Geometries, and Tight Deadlines for Precise Water Body Annotations

The project presented several significant challenges for our team due to the nature of the source material and the complexity of the task:

  • Variable Map Quality: Many map images were faded, damaged, or of low resolution, with obscuring water body details.
  • Diverse Cartographic Styles: Maps from different eras and regions used varied symbols, colors, and artistic representations for water bodies, complicating consistent annotation.
  • Complex Geometries and Water Systems: Irregular shapes and overlapping features in water bodies required precise boundary delineation. Some maps contained intricate water systems with interconnected rivers, lakes, and coastal features, requiring meticulous attention to detail during annotation.
  • Time Constraints: The project demanded rapid processing of a large dataset to meet research deadlines.
  • Manual Annotation Required for Accuracy: Despite access to advanced annotation tools, manual labeling and verification were essential to ensure accuracy, as unclear images could negatively impact the AI model's training process.
OUR SOLUTION

Leveraging Manual Expertise and Technology to Ensure

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:

Customized Guidelines for Image Labeling

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.

Digital Restoration of Geographical Maps

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.

Polygon Annotation Using CVAT Tool

By utilizing the customizable CVAT tool and polygon annotation technique, we precisely labeled waterbodies and their features in vintage maps.

Custom Automation Scripts

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.

Multi-pass Quality Checks

We implemented a rigorous, multi-stage review process where multiple annotators and quality auditors validated annotations to eliminate errors and maintain consistency.

Project Outcomes

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:

40% Enhanced Object Detection Accuracy

The annotated datasets enhanced the AI model’s ability to detect water bodies more accurately.

Improved Water Body Categorization

The model demonstrated superior performance in recognizing and classifying various types of water bodies across historical maps.

Faster Time-to-Market

High-quality training data enabled faster model development, supporting the client’s research timelines and objectives.

CONTACT US

Need Scalable Data Annotation for Complex Visual Datasets?

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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