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

• URL: http://arxiv.org/abs/2411.14560v1
• Date: Thu, 21 Nov 2024 20:17:41 GMT
• Title: Enhancing GeoAI and location encoding with spatial point pattern statistics: A Case Study of Terrain Feature Classification
• Authors: Sizhe Wang, Wenwen Li,
• Abstract summary: This study introduces a novel approach to terrain feature classification incorporating spatial point pattern statistics into deep learning models. We improve the GeoAI model by a knowledge driven approach to integrate both first-order and second-order effects of point patterns.
• Score: 2.724802833397066
• License:
• 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.

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