Our client is a well-known academic institution involved in studying and preserving natural water bodies. A key aspect of their research is analyzing historical data to understand changes in natural water bodies over time to uncover insights into environmental changes and help develop strategies for water conservation. To achieve this, the client was developing an AI/ML solution aimed at geographic data mapping. The solution required precise data input from annotated historical maps detailing water features such as rivers, lakes, and reservoirs.
As the client was training an AI/ML solution for automated map analysis, The primary objective of the project was to provide high-quality, accurate annotations of water bodies in a large collection of historical map images. The client required-
While the project had clear guidelines and requirements, our team faced several challenges due to the nature of the source material and the complexity of the project including:
Our team of three dedicated image annotators and a photo enhancement expert used a strategic combination of technical solutions, manual expertise, and digital enhancement techniques to overcome these challenges and meet the client's objectives:
We developed a detailed set of annotation guidelines specific to historical water feature mapping. These guidelines included instructions on handling different cartographic styles and unclear map images.
Our photo editing expert 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 annotation.
We used a locally hosted CVAT (Computer Vision Annotation Tool) that was secure, customizable, and allowed precise polygonal masking. This tool enabled our team to carefully outline the contours of water features like rivers, lakes, and streams ensuring accuracy in each annotation.
We implemented custom scripts within the CVAT environment to assist with common annotation tasks specific to water body identification. These enhancements included automatic detection of potential water features based on color and pattern recognition, which annotators could then verify and refine.
A precisely annotated dataset of 1200 historical maps provided a strong foundation for AI/ML model development and research.
The client's model became far more proficient at recognizing and categorizing different water bodies across the historical maps
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