Click, Segment, Learn! Using SAM to Explore Remote Sensing Imagery
Muhammad Azzam A.W. (1*), Az-Azira A.A.(1), Syalini M.S.(1), Siti Nor Afzan A.H.(1), Norhayati C.M.(1), Siti Masayu Y. (1) and Mohd Aizat Hisyam I.(1)

(1) Researcher, ICT Development & Geoinformatics Division, Malaysian Space Agency, Malaysia

Abstract

Remote sensing images with high resolution are increasingly essential around the world for tracking changes in land surfaces, monitoring urban expansion, and studying environmental aspects. However, extracting useful information from these images remains difficult because landscapes are complex and varied, image qualities are differed, and manual labeling data is time-consuming and labor intensive. Moreover, existing segmentation methods often lack flexibility across different environments, making it hard for both practical applications and newcomers learning AI-driven remote sensing to scale these techniques effectively. In response to these challenges, this study explores integrating the Segment Anything Model (SAM), a recent vision foundation model, into remote sensing workflows. We explore how SAM could improve segmentation accuracy of complex landscapes over Malaysia while simplifying the process so that users, even beginners, could interactively engage in what we call a ^Click, Segment, Learn^ workflow. This intuitive approach allows users to simply click on areas of interest, watch SAM automatically segment features, and learn from the outputs to better understand geospatial patterns. By applying SAM to urban, agricultural, and coastal datasets from Peninsular Malaysia, this study demonstrates how prompt-driven segmentation using zero-shot and interactive modes would reduce dependency on large, annotated datasets and extensive technical expertise. Preliminary results show that SAM outperforms conventional deep learning-based methods in segmenting key features such as built-up areas, road networks, coastal areas, and vegetation. Furthermore, this approach holds promise as a foundational platform bridging complex AI methods with practical geospatial applications, supporting national planning, environmental monitoring, and disaster response efforts, while simultaneously serving as a valuable educational resource for beginners engaging with AI in remote sensing.

Keywords: computer vision, SAM, remote sensing, Malaysia

Topic: Topic C: Emerging Technologies in Remote Sensing