# Olive Tree Satellite Image Segmentation Based On SAM and Multi-Phase Refinement

## TL;DR

This paper introduces a novel satellite image segmentation method for olive trees using SAM and multi-phase refinement, achieving high accuracy to aid biodiversity conservation amid climate change.

## Contribution

It combines SAM with shape and size constraints for improved olive tree segmentation, surpassing initial model performance.

## Key findings

- Achieved 98% accuracy in olive tree segmentation
- Significantly improved over initial SAM performance of 82%
- Effective for early anomaly detection in climate change context

## Abstract

In the context of proven climate change, maintaining olive biodiversity through early anomaly detection and treatment using remote sensing technology is crucial, offering effective management solutions. This paper presents an innovative approach to olive tree segmentation from satellite images. By leveraging foundational models and advanced segmentation techniques, the study integrates the Segment Anything Model (SAM) to accurately identify and segment olive trees in agricultural plots. The methodology includes SAM segmentation and corrections based on trees alignement in the field and a learanble constraint about the shape and the size. Our approach achieved a 98\% accuracy rate, significantly surpassing the initial SAM performance of 82\%.