Abstract
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.
Abstract (translated)
最近对地理空间人工智能（GeoAI）的兴趣推动了广泛的应用，这些应用利用人工智能（AI），尤其是深度学习技术来解决地理空间问题。然而，诸如训练数据不足以及在AI模型设计中忽视空间原理和空间效应等重大挑战仍然存在，这严重阻碍了AI与地理研究的深度融合。本文报告了我们开发的一种深度学习模型的工作成果，该模型能够在弱监督条件下实现目标检测，特别是自然特征的目标检测。我们的工作主要有三个贡献： 首先，我们提出了一种仅使用弱标签进行对象检测的方法。这一方法通过基于托布勒（Tobler）地理学第一定律的空间显式模型得以实现。 其次，我们将注意图引入到物体检测管道中，并开发出一种多阶段训练策略以提高性能。 第三，我们将该模型应用于火星撞击坑的检测任务上，这一任务之前需要大量的手动工作。我们的模型可以推广到地球及其他行星表面自然和人造特征的目标检测任务中。 这项研究推进了GeoAI在理论和方法论基础方面的进展。
URL
https://arxiv.org/abs/2508.03745
