Enhancing GeoAI and location encoding with spatial point pattern statistics: A Case Study of Terrain Feature Classification

TL;DR

This paper presents a novel method that integrates spatial point pattern statistics into deep learning models to improve terrain feature classification accuracy in GeoAI applications.

Contribution

It introduces a knowledge-driven approach to incorporate first- and second-order spatial effects into location encoding for GeoAI.

Findings

Enhanced model accuracy with spatial point pattern statistics

Effective integration of spatial relationships improves terrain classification

Demonstrated benefits of spatial context in GeoAI decision-making

Abstract

This study introduces a novel approach to terrain feature classification by incorporating spatial point pattern statistics into deep learning models. Inspired by the concept of location encoding, which aims to capture location characteristics to enhance GeoAI decision-making capabilities, we improve the GeoAI model by a knowledge driven approach to integrate both first-order and second-order effects of point patterns. This paper investigates how these spatial contexts impact the accuracy of terrain feature predictions. The results show that incorporating spatial point pattern statistics notably enhances model performance by leveraging different representations of spatial relationships.