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

arXiv: https://arxiv.org/abs/2411.14560 · HTML: https://mlsanity.com/paper/2411.14560/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 14 references that are in the map.
- First posted on arXiv 21 Nov 2024.

## Builds on (most cited first, 9 of 9)

- [Geography-Aware Self-Supervised Learning](https://mlsanity.com/paper/2011.09980/) (2020)
- [GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization](https://mlsanity.com/paper/2309.16020/) (2023)
- [SatCLIP: Global, General-Purpose Location Embeddings with Satellite Imagery](https://mlsanity.com/paper/2311.17179/) (2023)
- [Presence-Only Geographical Priors for Fine-Grained Image Classification](https://mlsanity.com/paper/1906.05272/) (2019)
- [CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations](https://mlsanity.com/paper/2305.01118/) (2023)
- [A Review of Location Encoding for GeoAI: Methods and Applications](https://mlsanity.com/paper/2111.04006/) (2021)
- [Sphere2Vec: A General-Purpose Location Representation Learning over a Spherical Surface for Large-Scale Geospatial Predictions](https://mlsanity.com/paper/2306.17624/) (2023)
- [Geospatial foundation models for image analysis: evaluating and enhancing NASA-IBM Prithvi's domain adaptability](https://mlsanity.com/paper/2409.00489/) (2024)
- [Local Indicator of Colocation Quotient with a Statistical Significance Test: Examining Spatial Association of Crime and Facilities](https://mlsanity.com/paper/2006.13277/) (2020)

## Abstract (arXiv)

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.