Vadodara Catch Trees

Project overview

Tree images were annotated for a computer vision dataset focused on identifying and annotating catch trees in Vadodara. Visible trees in agricultural and outdoor environments were detected and accurate annotations created to generate high-quality training data for a computer vision model.

Work performed

• Reviewed and organised the tree image dataset before annotation.
• Identified catch trees within the provided images.
• Created accurate polygon segmentation masks around visible tree areas.
• Followed the visible boundaries of tree canopies as closely as possible.
• Maintained consistent labelling using the Catch_Tree class.
• Partially visible trees were annotated wherever applicable.
• Distinguished target trees from surrounding vegetation and background objects.
• Carried out quality checks and corrected inaccurate or incomplete segmentation masks.
• Kept annotations consistent and suitable for computer vision model training.

What made it hard

• Identifying target trees in complex outdoor environments.
• Differentiating catch trees from surrounding trees and vegetation.
• Overlapping tree canopies were hard to separate cleanly.
• Partially visible trees required judgement about mask extent.
• Dense branches and foliage complicated boundary tracing.
• Complex backgrounds contained buildings, roads, crops and other objects.
• Camera angles, lighting conditions and tree sizes varied throughout the dataset.
• Maintaining precise segmentation boundaries around irregular tree canopies.

Result

A high-quality Vadodara catch trees dataset was completed with accurate polygon segmentation annotations. The dataset was prepared for training a computer vision model capable of identifying and segmenting catch trees in real-world outdoor environments.

Final deliverable

Delivered accurate and consistent segmentation annotations for catch trees throughout the dataset. The final output matched the project requirements and was ready for AI model training and tree detection and segmentation applications.