用深度学习从卫星图像自动识别地球土丘，助力气候变化研究。
Mapping earth mounds from space
- 基于深度学习框架分析卫星遥感图像，自动检测土丘分布。
- 在多个区域验证了方法可行性，但整体精度仍有提升空间。
- 适合从事生态监测与遥感分析的研究者参考。
规则的植被图案被认为是广泛存在的地貌，但其全球范围尚未被估算。其中，斑状景观在气候变化背景下尤为值得关注：半干旱灌木林中呈规律分布的植被斑块是资源极度匮乏的体现，预示着生态系统可能崩溃为均质荒漠；而白蚁巢穴形成的斑状景观则被证明能增强生态系统对气候变化的韧性。然而，大规模识别此类土丘需依赖自动化方法，例如利用流行的深度学习框架处理海量遥感数据（如光学卫星影像）。本文针对该问题展开研究，并在多个景观类型和地理区域上对比了当前最先进的深度网络模型。尽管取得了令人鼓舞的结果，但我们发现仍需更多研究才能实现从太空自动测绘这些土丘。
原文摘要 · Abstract (English)
Regular patterns of vegetation are considered widespread landscapes, although their global extent has never been estimated. Among them, spotted landscapes are of particular interest in the context of climate change. Indeed, regularly spaced vegetation spots in semi-arid shrublands result from extreme resource depletion and prefigure catastrophic shift of the ecosystem to a homogeneous desert, while termite mounds also producing spotted landscapes were shown to increase robustness to climate change. Yet, their identification at large scale calls for automatic methods, for instance using the popular deep learning framework, able to cope with a vast amount of remote sensing data, e.g., optical satellite imagery. In this paper, we tackle this problem and benchmark some state-of-the-art deep networks on several landscapes and geographical areas. Despite the promising results we obtained, we found that more research is needed to be able to map automatically these earth mounds from space.
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