基于地理学第一定律，弱监督下实现地形特征自动检测
Tobler's First Law in GeoAI: A Spatially Explicit Deep Learning Model for Terrain Feature Detection Under Weak Supervision
- 引入托布勒地理学第一定律，构建空间显式深度学习模型
- 通过注意力图与多阶段训练，提升弱监督下的检测性能
- 可泛化至地球及火星等地表自然与人工特征识别
近年来，地理空间人工智能（GeoAI）在利用人工智能特别是深度学习解决地理空间问题方面展现出广泛应用前景。然而，训练数据匮乏以及模型设计中忽视空间规律和空间效应等重大挑战，严重阻碍了人工智能与地理空间研究的深度融合。本文提出一种仅使用弱标签即可实现对象检测的深度学习方法，特别针对自然特征检测。首先，基于托布勒地理学第一定律，构建空间显式的模型；其次，将注意力图引入检测流程，并设计多阶段训练策略以提升性能；最后，将该模型应用于火星撞击坑检测任务，此前该任务需大量人工标注。模型具备在地球及其他行星地表上检测自然与人为特征的泛化能力。本研究推动了GeoAI的理论与方法基础发展。
原文摘要 · Abstract (English)
Recent interest in geospatial artificial intelligence (GeoAI) has fostered a wide range of applications using artificial intelligence (AI), especially deep learning, for geospatial problem solving. However, major challenges such as a lack of training data and the neglect of spatial principles and spatial effects in AI model design remain, significantly hindering the in-depth integration of AI with geospatial research. This paper reports our work in developing a deep learning model that enables object detection, particularly of natural features, in a weakly supervised manner. Our work makes three contributions: First, we present a method of object detection using only weak labels. This is achieved by developing a spatially explicit model based on Tobler's first law of geography. Second, we incorporate attention maps into the object detection pipeline and develop a multistage training strategy to improve performance. Third, we apply this model to detect impact craters on Mars, a task that previously required extensive manual effort. The model generalizes to both natural and human-made features on the surfaces of Earth and other planets. This research advances the theoretical and methodological foundations of GeoAI.
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