Geolocation Representation from Large Language Models are Generic Enhancers for Spatio-Temporal Learning

• URL: http://arxiv.org/abs/2408.12116v1
• Date: Thu, 22 Aug 2024 04:05:02 GMT
• Title: Geolocation Representation from Large Language Models are Generic Enhancers for Spatio-Temporal Learning
• Authors: Junlin He, Tong Nie, Wei Ma,
• Abstract summary: Universal representation models are less prevalent than their extensive use in natural language processing and computer vision. This discrepancy arises primarily from the high costs associated with the input existing representation models. We develop a training-free method that leverages large language models to derive geolocation representations.
• Score: 10.438284728725842
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the geospatial domain, universal representation models are significantly less prevalent than their extensive use in natural language processing and computer vision. This discrepancy arises primarily from the high costs associated with the input of existing representation models, which often require street views and mobility data. To address this, we develop a novel, training-free method that leverages large language models (LLMs) and auxiliary map data from OpenStreetMap to derive geolocation representations (LLMGeovec). LLMGeovec can represent the geographic semantics of city, country, and global scales, which acts as a generic enhancer for spatio-temporal learning. Specifically, by direct feature concatenation, we introduce a simple yet effective paradigm for enhancing multiple spatio-temporal tasks including geographic prediction (GP), long-term time series forecasting (LTSF), and graph-based spatio-temporal forecasting (GSTF). LLMGeovec can seamlessly integrate into a wide spectrum of spatio-temporal learning models, providing immediate enhancements. Experimental results demonstrate that LLMGeovec achieves global coverage and significantly boosts the performance of leading GP, LTSF, and GSTF models.

Related papers

• OmniGeo: Towards a Multimodal Large Language Models for Geospatial Artificial Intelligence [51.0456395687016]
  multimodal large language models (LLMs) have opened new frontiers in artificial intelligence. We propose a MLLM (OmniGeo) tailored to geospatial applications. By combining the strengths of natural language understanding and spatial reasoning, our model enhances the ability of instruction following and the accuracy of GeoAI systems.
  arXiv  Detail & Related papers  (2025-03-20T16:45:48Z)
• GeoJEPA: Towards Eliminating Augmentation- and Sampling Bias in Multimodal Geospatial Learning [0.0]
  We present GeoJEPA, a versatile multimodal fusion model for geospatial data built on the self-supervised Joint-Embedding Predictive Architecture. We aim to eliminate the widely accepted augmentation- and sampling biases found in self-supervised geospatial representation learning. The results are multimodal semantic representations of urban regions and map entities that we evaluate both quantitatively and qualitatively.
  arXiv  Detail & Related papers  (2025-02-25T22:03:28Z)
• GeoAggregator: An Efficient Transformer Model for Geo-Spatial Tabular Data [5.40483645224129]
  This paper introduces GeoAggregator, an efficient and lightweight algorithm for geospatial data modeling. We benchmark it against spatial statistical models, XGBoost, and several state-of-the-art geospatial deep learning methods. Results demonstrate that GeoAggregators achieve the best or second-best performance compared to their competitors on nearly all datasets.
  arXiv  Detail & Related papers  (2025-02-20T20:39:15Z)
• Swarm Intelligence in Geo-Localization: A Multi-Agent Large Vision-Language Model Collaborative Framework [51.26566634946208]
  We introduce smileGeo, a novel visual geo-localization framework. By inter-agent communication, smileGeo integrates the inherent knowledge of these agents with additional retrieved information. Results show that our approach significantly outperforms current state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-08-21T03:31:30Z)
• Image-Based Geolocation Using Large Vision-Language Models [19.071551941682063]
  We introduce tool, an innovative framework that significantly enhances image-based geolocation accuracy. tool employs a systematic chain-of-thought (CoT) approach, mimicking human geoguessing strategies. It achieves an impressive average score of 4550.5 in the GeoGuessr game, with an 85.37% win rate, and delivers highly precise geolocation predictions.
  arXiv  Detail & Related papers  (2024-08-18T13:39:43Z)
• Geode: A Zero-shot Geospatial Question-Answering Agent with Explicit Reasoning and Precise Spatio-Temporal Retrieval [0.0]
  We introduce a pioneering system designed to tackle zero-shot geospatial question-answering tasks with high precision. Our approach represents a significant improvement in addressing the limitations of current large language models.
  arXiv  Detail & Related papers  (2024-06-26T21:59:54Z)
• WorldGPT: Empowering LLM as Multimodal World Model [51.243464216500975]
  We introduce WorldGPT, a generalist world model built upon Multimodal Large Language Model (MLLM) WorldGPT acquires an understanding of world dynamics through analyzing millions of videos across various domains. We conduct evaluations on WorldNet, a multimodal state transition prediction benchmark.
  arXiv  Detail & Related papers  (2024-04-28T14:42:02Z)
• GeoLLM: Extracting Geospatial Knowledge from Large Language Models [49.20315582673223]
  We present GeoLLM, a novel method that can effectively extract geospatial knowledge from large language models. We demonstrate the utility of our approach across multiple tasks of central interest to the international community, including the measurement of population density and economic livelihoods. Our experiments reveal that LLMs are remarkably sample-efficient, rich in geospatial information, and robust across the globe.
  arXiv  Detail & Related papers  (2023-10-10T00:03:23Z)
• GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization [61.10806364001535]
  Worldwide Geo-localization aims to pinpoint the precise location of images taken anywhere on Earth. Existing approaches divide the globe into discrete geographic cells, transforming the problem into a classification task. We propose GeoCLIP, a novel CLIP-inspired Image-to-GPS retrieval approach that enforces alignment between the image and its corresponding GPS locations.
  arXiv  Detail & Related papers  (2023-09-27T20:54:56Z)
• Geo-Encoder: A Chunk-Argument Bi-Encoder Framework for Chinese Geographic Re-Ranking [61.60169764507917]
  Chinese geographic re-ranking task aims to find the most relevant addresses among retrieved candidates. We propose an innovative framework, namely Geo-Encoder, to more effectively integrate Chinese geographical semantics into re-ranking pipelines.
  arXiv  Detail & Related papers  (2023-09-04T13:44:50Z)
• GeoNet: Benchmarking Unsupervised Adaptation across Geographies [71.23141626803287]
  We study the problem of geographic robustness and make three main contributions. First, we introduce a large-scale dataset GeoNet for geographic adaptation. Second, we hypothesize that the major source of domain shifts arise from significant variations in scene context. Third, we conduct an extensive evaluation of several state-of-the-art unsupervised domain adaptation algorithms and architectures.
  arXiv  Detail & Related papers  (2023-03-27T17:59:34Z)
• MGeo: Multi-Modal Geographic Pre-Training Method [49.78466122982627]
  We propose a novel query-POI matching method Multi-modal Geographic language model (MGeo) MGeo represents GC as a new modality and is able to fully extract multi-modal correlations for accurate query-POI matching. Our proposed multi-modal pre-training method can significantly improve the query-POI matching capability of generic PTMs.
  arXiv  Detail & Related papers  (2023-01-11T03:05:12Z)
• A General Purpose Neural Architecture for Geospatial Systems [142.43454584836812]
  We present a roadmap towards the construction of a general-purpose neural architecture (GPNA) with a geospatial inductive bias. We envision how such a model may facilitate cooperation between members of the community.
  arXiv  Detail & Related papers  (2022-11-04T09:58:57Z)
• Geographic Adaptation of Pretrained Language Models [29.81557992080902]
  We introduce geoadaptation, an intermediate training step that couples language modeling with geolocation prediction in a multi-task learning setup. We show that the effectiveness of geoadaptation stems from its ability to geographically retrofit the representation space of the pretrained language models.
  arXiv  Detail & Related papers  (2022-03-16T11:55:00Z)
• Multi-Level Graph Convolutional Network with Automatic Graph Learning for Hyperspectral Image Classification [63.56018768401328]
  We propose a Multi-level Graph Convolutional Network (GCN) with Automatic Graph Learning method (MGCN-AGL) for HSI classification. By employing attention mechanism to characterize the importance among spatially neighboring regions, the most relevant information can be adaptively incorporated to make decisions. Our MGCN-AGL encodes the long range dependencies among image regions based on the expressive representations that have been produced at local level.
  arXiv  Detail & Related papers  (2020-09-19T09:26:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.