Enhancing GeoAI and location encoding with spatial point pattern statistics: A Case Study of Terrain Feature Classification
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866915030789783552 |
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| author | Wang, Sizhe Li, Wenwen |
| author_facet | Wang, Sizhe Li, Wenwen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_14560 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing GeoAI and location encoding with spatial point pattern statistics: A Case Study of Terrain Feature Classification Wang, Sizhe Li, Wenwen Computer Vision and Pattern Recognition Machine Learning 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. |
| title | Enhancing GeoAI and location encoding with spatial point pattern statistics: A Case Study of Terrain Feature Classification |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2411.14560 |