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This study aims to develop a model for detecting dynamic changes in land by analyzing aerial images over time. In scenarios where collected labels, annotations, and change maps for a sequence of images are expensive and not easily accessible, unsupervised change detection methods are valuable. The goal is to leverage an agnostic segmentation model to gain insights into performing change detection without annotations, detect small objects in large images, and pinpoint changes within specified areas relative to historical images. This research aims to contribute to environmental protection and land management advancements. To demonstrate the effectiveness of our model, we utilize two public datasets and a case study from the northern region of Portugal.

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Landscape Change Detection Based on Agnostic Aerial Image Segmentation

  • 会议论文

  • 发表日期：

    2026年

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  • Narjes Davari,
  • Matías Molina,
  • Rita P. Ribeiro,
  • Carlos Ferreira,
  • João Gama

摘要

This study aims to develop a model for detecting dynamic changes in land by analyzing aerial images over time. In scenarios where collected labels, annotations, and change maps for a sequence of images are expensive and not easily accessible, unsupervised change detection methods are valuable. The goal is to leverage an agnostic segmentation model to gain insights into performing change detection without annotations, detect small objects in large images, and pinpoint changes within specified areas relative to historical images. This research aims to contribute to environmental protection and land management advancements. To demonstrate the effectiveness of our model, we utilize two public datasets and a case study from the northern region of Portugal.