PEACE: Empowering Geologic Map Holistic Understanding with MLLMs

• URL: http://arxiv.org/abs/2501.06184v1
• Date: Fri, 10 Jan 2025 18:59:42 GMT
• Title: PEACE: Empowering Geologic Map Holistic Understanding with MLLMs
• Authors: Yangyu Huang, Tianyi Gao, Haoran Xu, Qihao Zhao, Yang Song, Zhipeng Gui, Tengchao Lv, Hao Chen, Lei Cui, Scarlett Li, Furu Wei,
• Abstract summary: Geologic map, as a fundamental diagram in geology science, provides critical insights into the structure and composition of Earth's subsurface and surface. Despite their significance, current Multimodal Large Language Models (MLLMs) often fall short in geologic map understanding. To quantify this gap, we construct GeoMap-Bench, the first-ever benchmark for evaluating MLLMs in geologic map understanding.
• Score: 64.58959634712215
• License:
• Abstract: Geologic map, as a fundamental diagram in geology science, provides critical insights into the structure and composition of Earth's subsurface and surface. These maps are indispensable in various fields, including disaster detection, resource exploration, and civil engineering. Despite their significance, current Multimodal Large Language Models (MLLMs) often fall short in geologic map understanding. This gap is primarily due to the challenging nature of cartographic generalization, which involves handling high-resolution map, managing multiple associated components, and requiring domain-specific knowledge. To quantify this gap, we construct GeoMap-Bench, the first-ever benchmark for evaluating MLLMs in geologic map understanding, which assesses the full-scale abilities in extracting, referring, grounding, reasoning, and analyzing. To bridge this gap, we introduce GeoMap-Agent, the inaugural agent designed for geologic map understanding, which features three modules: Hierarchical Information Extraction (HIE), Domain Knowledge Injection (DKI), and Prompt-enhanced Question Answering (PEQA). Inspired by the interdisciplinary collaboration among human scientists, an AI expert group acts as consultants, utilizing a diverse tool pool to comprehensively analyze questions. Through comprehensive experiments, GeoMap-Agent achieves an overall score of 0.811 on GeoMap-Bench, significantly outperforming 0.369 of GPT-4o. Our work, emPowering gEologic mAp holistiC undErstanding (PEACE) with MLLMs, paves the way for advanced AI applications in geology, enhancing the efficiency and accuracy of geological investigations.

Related papers

• Geolocation with Real Human Gameplay Data: A Large-Scale Dataset and Human-Like Reasoning Framework [59.42946541163632]
  We introduce a comprehensive geolocation framework with three key components. GeoComp, a large-scale dataset; GeoCoT, a novel reasoning method; and GeoEval, an evaluation metric. We demonstrate that GeoCoT significantly boosts geolocation accuracy by up to 25% while enhancing interpretability.
  arXiv  Detail & Related papers  (2025-02-19T14:21:25Z)
• MapEval: A Map-Based Evaluation of Geo-Spatial Reasoning in Foundation Models [7.422346909538787]
  We introduce MapEval, a benchmark designed to assess diverse and complex map-based user queries with geo-spatial reasoning. MapEval consists of 700 unique multiple-choice questions about locations across 180 cities and 54 countries. Our detailed analyses provide insights into the strengths and weaknesses of current models, though all models still fall short of human performance by more than 20% on average. This gap highlights MapEval's critical role in advancing general-purpose foundation models with stronger geo-spatial understanding.
  arXiv  Detail & Related papers  (2024-12-31T07:20:32Z)
• A comprehensive GeoAI review: Progress, Challenges and Outlooks [0.0]
  Geospatial Artificial Intelligence (GeoAI) has gained traction in the most relevant research works and industrial applications. This paper offers a comprehensive review of GeoAI as a synergistic concept applying Artificial Intelligence (AI) methods and models to geospatial data.
  arXiv  Detail & Related papers  (2024-12-16T10:41:02Z)
• Artificial Intelligence Studies in Cartography: A Review and Synthesis of Methods, Applications, and Ethics [4.665390376528911]
  We conduct a systematic content analysis and narrative synthesis of research studies integrating GeoAI and cartography. We identify dimensions of GeoAI methods for cartography such as data sources, data formats, map evaluations, and six contemporary GeoAI models. We raise five potential ethical challenges that need to be addressed in the integration of GeoAI for cartography.
  arXiv  Detail & Related papers  (2023-12-13T05:15:57Z)
• 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)
• Are Large Language Models Geospatially Knowledgeable? [21.401931052512595]
  This paper investigates the extent of geospatial knowledge, awareness, and reasoning abilities encoded within Large Language Models (LLM) With a focus on autoregressive language models, we devise experimental approaches related to (i) probing LLMs for geo-coordinates to assess geospatial knowledge, (ii) using geospatial and non-geospatial prepositions to gauge their geospatial awareness, and (iii) utilizing a multidimensional scaling (MDS) experiment to assess the models' geospatial reasoning capabilities.
  arXiv  Detail & Related papers  (2023-10-09T17:20:11Z)
• K2: A Foundation Language Model for Geoscience Knowledge Understanding and Utilization [105.89544876731942]
  Large language models (LLMs) have achieved great success in general domains of natural language processing. We present the first-ever LLM in geoscience, K2, alongside a suite of resources developed to further promote LLM research within geoscience.
  arXiv  Detail & Related papers  (2023-06-08T09:29:05Z)
• GeoGLUE: A GeoGraphic Language Understanding Evaluation Benchmark [56.08664336835741]
  We propose a GeoGraphic Language Understanding Evaluation benchmark, named GeoGLUE. We collect data from open-released geographic resources and introduce six natural language understanding tasks. We pro vide evaluation experiments and analysis of general baselines, indicating the effectiveness and significance of the GeoGLUE benchmark.
  arXiv  Detail & Related papers  (2023-05-11T03:21:56Z)
• GeoFault: A well-founded fault ontology for interoperability in geological modeling [0.0]
  This paper presents a domain ontology: GeoFault, resting on the Basic Ontology BFO (Arp et al., 2015) and the GeoCore (Garcia et al., 2020) It models the knowledge related to geological faults. Faults are essential to various industries but to model. The reference to the BFO and GeoCore allows assigning these various elements to define classes.
  arXiv  Detail & Related papers  (2023-02-14T14:20:13Z)
• 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)

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.