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

TL;DR

This paper introduces a spatially explicit deep learning model based on Tobler's first law for weakly supervised terrain feature detection, demonstrating its effectiveness on planetary and terrestrial datasets.

Contribution

It develops a novel weakly supervised object detection method incorporating spatial principles and attention mechanisms, advancing GeoAI modeling techniques.

Findings

Successfully detects impact craters on Mars with minimal supervision

Generalizes to natural and human-made features on Earth and other planets

Improves detection performance with multistage training and attention maps

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.