Tree level change detection over Ahmedabad city using very high resolution satellite images and Deep Learning

TL;DR

This paper demonstrates the use of high-resolution satellite imagery and deep learning, specifically YOLOv7, for accurate tree change detection in Ahmedabad, achieving up to 80% detection accuracy.

Contribution

It introduces a novel application of YOLOv7 for urban tree change detection using very high resolution satellite images in an Indian city.

Findings

Achieved 80% tree detection accuracy after hyperparameter tuning.

Developed a curated dataset of 6500 images for training.

Demonstrated effective change detection with minimal false segmentation.

Abstract

In this study, 0.5m high resolution satellite datasets over Indian urban region was used to demonstrate the applicability of deep learning models over Ahmedabad, India. Here, YOLOv7 instance segmentation model was trained on well curated trees canopy dataset (6500 images) in order to carry out the change detection. During training, evaluation metrics such as bounding box regression and mask regression loss, mean average precision (mAP) and stochastic gradient descent algorithm were used for evaluating and optimizing the performance of model. After the 500 epochs, the mAP of 0.715 and 0.699 for individual tree detection and tree canopy mask segmentation were obtained. However, by further tuning hyper parameters of the model, maximum accuracy of 80 % of trees detection with false segmentation rate of 2% on data was obtained.