Tobler’s First Law in GeoAI: A Spatially Explicit Deep Learning Model for Terrain Feature Detection under Weak Supervision

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. Major challenges, however, such as a lack of training data and ignorance of spatial principles and spatial effects in AI model design remain, significantly hindering the in-depth integration of AI with geospatial research. This article reports our work in developing a cutting-edge deep learning model that enables object detection, especially of natural features, in a weakly supervised manner. Our work has made three innovative contributions: First, we present a novel method of object detection using only weak labels. This is achieved by developing a spatially explicit model according to Tobler’s first law of geography to enable weakly supervised object detection. Second, we integrate the idea of an attention map into the deep learning–based object detection pipeline and develop a multistage training strategy to further boost detection performance. Third, we have successfully applied this model for the automated detection of Mars impact craters, the inspection of which often involved tremendous manual work prior to our solution. Our model is generalizable for detecting both natural and man-made features on the surface of the Earth and other planets. This research has made a major contribution to the enrichment of the theoretical and methodological body of knowledge of GeoAI.