Soil sinkholes significantly influence soil degradation, but their irregular shapes, along with interference from shadow and vegetation, make it challenging to accurately quantify their properties using remotely sensed data. We present a novel framework for sinkhole segmentation that combines traditional topographic computations of closed depressions with the newly developed prompt-based Segment Anything Model (SAM). Within this framework, termed SinkSAM, we highlight four key improvements: (1) The integration of topographic computations with SAM enables pixel-level refinement of sinkhole boundaries segmentation; (2) A coherent mathematical prompting strategy, based on closed depressions, addresses the limitations of purely learning-based models (CNNs) in detecting and segmenting undefined sinkhole features, while improving generalization to new, unseen regions; (3) Using Depth Anything V2 monocular depth for automatic prompts eliminates photogrammetric biases, enabling sinkhole mapping without the dependence on LiDAR data; and (4) An established sinkhole database facilitates fine-tuning of SAM, improving its zero-shot performance in sinkhole segmentation. These advancements allow the deployment of SinkSAM, in an unseen test area, in the highly variable semiarid region, achieving an intersection-over-union (IoU) of 40.27\% and surpassing previous results. This paper also presents the first SAM implementation for sinkhole segmentation and demonstrates the robustness of SinkSAM in extracting sinkhole maps using a single RGB image.

翻译：土壤天坑显著影响土壤退化，但其不规则形状以及阴影和植被的干扰，使得利用遥感数据精确量化其特性具有挑战性。我们提出了一种新颖的天坑分割框架，该框架将传统的闭合洼地地形计算与新开发的基于提示的Segment Anything Model（SAM）相结合。在这个称为SinkSAM的框架中，我们重点阐述了四项关键改进：（1）地形计算与SAM的集成实现了天坑边界分割的像素级精细化；（2）基于闭合洼地的连贯数学提示策略，解决了纯学习模型（CNN）在检测和分割未定义天坑特征方面的局限性，同时提高了对新、未见区域的泛化能力；（3）使用Depth Anything V2单目深度进行自动提示消除了摄影测量偏差，使得无需依赖LiDAR数据即可进行天坑制图；（4）一个已建立的天坑数据库促进了SAM的微调，提升了其在零样本天坑分割任务中的性能。这些进展使得SinkSAM能够在高度变化的半干旱地区的一个未见测试区域进行部署，并取得了40.27%的交并比（IoU），超越了先前的结果。本文还首次展示了用于天坑分割的SAM实现，并证明了SinkSAM仅使用单张RGB图像提取天坑地图的鲁棒性。