EarthScape: A Multimodal Dataset for Surficial Geologic Mapping and Earth Surface Analysis
- URL: http://arxiv.org/abs/2503.15625v1
- Date: Wed, 19 Mar 2025 18:23:48 GMT
- Title: EarthScape: A Multimodal Dataset for Surficial Geologic Mapping and Earth Surface Analysis
- Authors: Matthew Massey, Abdullah-Al-Zubaer Imran,
- Abstract summary: We introduce EarthScape, a novel, AI-ready multimodal dataset for surficial geologic mapping and Earth surface analysis.<n>EarthScape integrates high-resolution aerial RGB and near-infrared (NIR) imagery, digital elevation models (DEM), multi-scale DEM-derived terrain features, and hydrologic and infrastructure vector data.<n>As a living dataset with a vision for expansion, EarthScape bridges the gap between computer vision and Earth sciences.
- Score: 0.31077024712075796
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Surficial geologic mapping is essential for understanding Earth surface processes, addressing modern challenges such as climate change and national security, and supporting common applications in engineering and resource management. However, traditional mapping methods are labor-intensive, limiting spatial coverage and introducing potential biases. To address these limitations, we introduce EarthScape, a novel, AI-ready multimodal dataset specifically designed for surficial geologic mapping and Earth surface analysis. EarthScape integrates high-resolution aerial RGB and near-infrared (NIR) imagery, digital elevation models (DEM), multi-scale DEM-derived terrain features, and hydrologic and infrastructure vector data. The dataset provides detailed annotations for seven distinct surficial geologic classes encompassing various geological processes. We present a comprehensive data processing pipeline using open-sourced raw data and establish baseline benchmarks using different spatial modalities to demonstrate the utility of EarthScape. As a living dataset with a vision for expansion, EarthScape bridges the gap between computer vision and Earth sciences, offering a valuable resource for advancing research in multimodal learning, geospatial analysis, and geological mapping. Our code is available at https://github.com/masseygeo/earthscape.
Related papers
- Geographical Context Matters: Bridging Fine and Coarse Spatial Information to Enhance Continental Land Cover Mapping [2.9212099078191756]
BRIDGE-LC is a novel deep learning framework that integrates multi-scale geospatial information into the land cover classification process.
Our results demonstrate that integrating geospatial information improves land cover mapping performance.
arXiv Detail & Related papers (2025-04-16T17:42:46Z) - 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.<n>We propose a MLLM (OmniGeo) tailored to geospatial applications.<n>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) - 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.<n>GeoComp, a large-scale dataset; GeoCoT, a novel reasoning method; and GeoEval, an evaluation metric.<n>We demonstrate that GeoCoT significantly boosts geolocation accuracy by up to 25% while enhancing interpretability.
arXiv Detail & Related papers (2025-02-19T14:21:25Z) - EarthView: A Large Scale Remote Sensing Dataset for Self-Supervision [72.84868704100595]
This paper presents a dataset specifically designed for self-supervision on remote sensing data, intended to enhance deep learning applications on Earth monitoring tasks.<n>The dataset spans 15 tera pixels of global remote-sensing data, combining imagery from a diverse range of sources, including NEON, Sentinel, and a novel release of 1m spatial resolution data from Satellogic.<n>Accompanying the dataset is EarthMAE, a tailored Masked Autoencoder developed to tackle the distinct challenges of remote sensing data.
arXiv Detail & Related papers (2025-01-14T13:42:22Z) - PEACE: Empowering Geologic Map Holistic Understanding with MLLMs [64.58959634712215]
Geologic map, as a fundamental diagram in geology science, provides critical insights into the structure and composition of Earth's subsurface and surface.<n>Despite their significance, current Multimodal Large Language Models (MLLMs) often fall short in geologic map understanding.<n>To quantify this gap, we construct GeoMap-Bench, the first-ever benchmark for evaluating MLLMs in geologic map understanding.
arXiv Detail & Related papers (2025-01-10T18:59:42Z) - ImplicitTerrain: a Continuous Surface Model for Terrain Data Analysis [14.013976303831313]
ImplicitTerrain is an implicit neural representation (INR) approach for modeling high-resolution terrain continuously and differentiably.
Our experiments demonstrate superior surface fitting accuracy, effective topological feature retrieval, and various topographical feature extraction.
arXiv Detail & Related papers (2024-05-31T23:05:34Z) - Semi-Automated Segmentation of Geoscientific Data Using Superpixels [4.035753155957697]
Geological processes determine the distribution of resources such as critical minerals, water, and geothermal energy.
Inspired by the concept of superpixels, we propose a deep-learning based approach to segmentized survey data into regions with similar characteristics.
arXiv Detail & Related papers (2023-03-20T19:21:46Z) - 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) - Semantic Segmentation of Vegetation in Remote Sensing Imagery Using Deep
Learning [77.34726150561087]
We propose an approach for creating a multi-modal and large-temporal dataset comprised of publicly available Remote Sensing data.
We use Convolutional Neural Networks (CNN) models that are capable of separating different classes of vegetation.
arXiv Detail & Related papers (2022-09-28T18:51:59Z) - Leveraging Domain Adaptation for Low-Resource Geospatial Machine
Learning [0.0]
Many labeled geospatial datasets are specific to certain regions, instruments, or extreme weather events.
We investigate the application of modern domain-adaptation to multiple proposed geospatial benchmarks.
arXiv Detail & Related papers (2021-07-11T06:47:20Z)
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.