Knowledge-Driven GeoAI: Integrating Spatial Knowledge into Multi-Scale Deep Learning for Mars Crater Detection
Knowledge-Driven GeoAI: Integrating Spatial Knowledge into Multi-Scale Deep Learning for Mars Crater Detection
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DOI：
10.3390/rs13112116
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Chia-Yu Hsu;Wenwen Li;Sizhe Wang
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文献类型：
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作者：
Chia-Yu Hsu;Wenwen Li;Sizhe Wang
本文介绍了一种新的GeoAI解决方案，以支持自动绘制火星表面的全球陨石坑。传统的陨石坑检测算法受到只能以半自动或多阶段方式工作的限制，并且大多数都是为了处理火星表面一小部分区域的特定数据集而开发的，这阻碍了它们对全球陨石坑检测的可移植性。作为替代方案，我们提出了一种基于深度学习的GeoAI解决方案，以有效地解决这个问题。我们的目标检测流程中集成了三个创新功能：（1）利用特征金字塔网络来生成跨多个目标尺度的具有丰富语义的特征地图;（2）集成了基于Hough变换的先验地理空间知识，以实现对潜在陨石坑的更准确定位;（3）采用尺度感知分类器，提高了对大、小陨石坑实例的预测精度。结果表明，与流行的Faster R-CNN模型相比，所提出的策略在火山口检测性能上有显着提高。将地理空间领域知识整合到数据驱动的分析中，将GeoAI研究提升到一个新的水平，以实现知识驱动的GeoAI。这项研究可以应用于各种各样的目标检测和图像分析任务。
This paper introduces a new GeoAI solution to support automated mapping of global craters on the Mars surface. Traditional crater detection algorithms suffer from the limitation of working only in a semiautomated or multi-stage manner, and most were developed to handle a specific dataset in a small subarea of Mars’ surface, hindering their transferability for global crater detection. As an alternative, we propose a GeoAI solution based on deep learning to tackle this problem effectively. Three innovative features are integrated into our object detection pipeline: (1) a feature pyramid network is leveraged to generate feature maps with rich semantics across multiple object scales; (2) prior geospatial knowledge based on the Hough transform is integrated to enable more accurate localization of potential craters; and (3) a scale-aware classifier is adopted to increase the prediction accuracy of both large and small crater instances. The results show that the proposed strategies bring a significant increase in crater detection performance than the popular Faster R-CNN model. The integration of geospatial domain knowledge into the data-driven analytics moves GeoAI research up to the next level to enable knowledge-driven GeoAI. This research can be applied to a wide variety of object detection and image analysis tasks.