用深度学习检测亚马逊森林变化并自动标注关键词。
Annotating Satellite Images of Forests with Keywords from a Specialized Corpus in the Context of Change Detection
- 通过对比不同时期卫星图像，识别森林覆盖变化。
- 从科学文献提取关键词，自动标注变化区域。
- 适用于环境监测，也可推广至其他领域。
亚马逊雨林是调节地球气候、维系物种多样性的关键生态系统，其砍伐对全球碳排放和生物多样性影响重大。本文提出一种基于深度学习的方法，利用地球观测卫星的图像对检测亚马逊雨林的砍伐情况，通过比较同一地区不同时间的影像，识别森林覆盖的变化。同时，构建视觉语义模型，从与亚马逊相关的科学文献中提取候选关键词，自动为检测到的变化生成语义标注。在亚马逊图像对数据集上评估了该方法，验证了其在检测砍伐和生成相关标注方面的有效性。本方法不仅为监测和研究亚马逊砍伐影响提供有力工具，且具有通用性，可扩展至其他应用领域。
原文摘要 · Abstract (English)
The Amazon rain forest is a vital ecosystem that plays a crucial role in regulating the Earth's climate and providing habitat for countless species. Deforestation in the Amazon is a major concern as it has a significant impact on global carbon emissions and biodiversity. In this paper, we present a method for detecting deforestation in the Amazon using image pairs from Earth observation satellites. Our method leverages deep learning techniques to compare the images of the same area at different dates and identify changes in the forest cover. We also propose a visual semantic model that automatically annotates the detected changes with relevant keywords. The candidate annotation for images are extracted from scientific documents related to the Amazon region. We evaluate our approach on a dataset of Amazon image pairs and demonstrate its effectiveness in detecting deforestation and generating relevant annotations. Our method provides a useful tool for monitoring and studying the impact of deforestation in the Amazon. While we focus on environment applications of our work by using images of deforestation in the Amazon rain forest to demonstrate the effectiveness of our proposed approach, it is generic enough to be applied to other domains.
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