Update README.md

README.md

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 ### Curation Rationale
 
 <!-- Motivation for the creation of this dataset. -->
+The RDD2020 dataset was curated with the objective of facilitating the development, testing, and benchmarking of machine learning models for road damage detection, catering specifically to the needs of municipalities and road agencies. A significant aspect of the dataset's curation process was the conversion of images into the Python Imaging Library (PIL) format and the meticulous parsing of XML annotations to ensure a seamless integration between the image data and the associated labels. This conversion process was driven by the need to simplify the handling of image data for machine learning applications, as the PIL format is widely supported by data processing and model training frameworks commonly used in the field. 
 
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+Additionally, the parsing of XML files to extract detailed annotations about the type and coordinates of road damages allows for precise labeling of the data. This approach ensures that each image is directly associated with its corresponding damage type and location. The dataset's diversity, with images sourced from three different countries, aims to enable the creation of robust models that are effective across various environmental conditions and road infrastructures, thereby broadening the applicability and relevance of the trained models.
 
 
 #### Data Collection and Processing
 
 <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
 
-[More Information Needed]
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 #### Who are the source data producers?
 
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 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-[More Information Needed]
+The dataset primarily includes images from three countries (India, Japan, and the Czech Republic), which may not fully represent road conditions worldwide. Users should be cautious when generalizing models trained on this dataset to other regions.
 
 ### Recommendations