Research on the Spatial Data Intelligent Foundation Model

• URL: http://arxiv.org/abs/2405.19730v5
• Date: Wed, 28 Aug 2024 13:05:41 GMT
• Title: Research on the Spatial Data Intelligent Foundation Model
• Authors: Shaohua Wang, Xing Xie, Yong Li, Danhuai Guo, Zhi Cai, Yu Liu, Yang Yue, Xiao Pan, Feng Lu, Huayi Wu, Zhipeng Gui, Zhiming Ding, Bolong Zheng, Fuzheng Zhang, Jingyuan Wang, Zhengchao Chen, Hao Lu, Jiayi Li, Peng Yue, Wenhao Yu, Yao Yao, Leilei Sun, Yong Zhang, Longbiao Chen, Xiaoping Du, Xiang Li, Xueying Zhang, Kun Qin, Zhaoya Gong, Weihua Dong, Xiaofeng Meng,
• Abstract summary: This report focuses on spatial data intelligent large models, delving into the principles, methods, and cutting-edge applications of these models. It provides an in-depth discussion on the definition, development history, current status, and trends of spatial data intelligent large models. The report systematically elucidates the key technologies of spatial data intelligent large models and their applications in urban environments, aerospace remote sensing, geography, transportation, and other scenarios.
• Score: 70.47828328840912
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This report focuses on spatial data intelligent large models, delving into the principles, methods, and cutting-edge applications of these models. It provides an in-depth discussion on the definition, development history, current status, and trends of spatial data intelligent large models, as well as the challenges they face. The report systematically elucidates the key technologies of spatial data intelligent large models and their applications in urban environments, aerospace remote sensing, geography, transportation, and other scenarios. Additionally, it summarizes the latest application cases of spatial data intelligent large models in themes such as urban development, multimodal systems, remote sensing, smart transportation, and resource environments. Finally, the report concludes with an overview and outlook on the development prospects of spatial data intelligent large models.

Related papers

• Foundation Models for Remote Sensing and Earth Observation: A Survey [101.77425018347557]
  This survey systematically reviews the emerging field of Remote Sensing Foundation Models (RSFMs) It begins with an outline of their motivation and background, followed by an introduction of their foundational concepts. We benchmark these models against publicly available datasets, discuss existing challenges, and propose future research directions.
  arXiv  Detail & Related papers  (2024-10-22T01:08:21Z)
• Enabling High Data Throughput Reinforcement Learning on GPUs: A Domain Agnostic Framework for Data-Driven Scientific Research [90.91438597133211]
  We introduce WarpSci, a framework designed to overcome crucial system bottlenecks in the application of reinforcement learning. We eliminate the need for data transfer between the CPU and GPU, enabling the concurrent execution of thousands of simulations.
  arXiv  Detail & Related papers  (2024-08-01T21:38:09Z)
• Deep Learning for Spatiotemporal Big Data: A Vision on Opportunities and Challenges [4.497634148674422]
  Intemporal big data can foster new opportunities to solve problems that have not been possible before. The distinctive characteristics of big data pose new challenges for deep learning technologies.
  arXiv  Detail & Related papers  (2023-10-30T19:12:51Z)
• Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook [95.32949323258251]
  Temporal data, notably time series andtemporal-temporal data, are prevalent in real-world applications. Recent advances in large language and other foundational models have spurred increased use in time series andtemporal data mining.
  arXiv  Detail & Related papers  (2023-10-16T09:06:00Z)
• Unified Data Management and Comprehensive Performance Evaluation for Urban Spatial-Temporal Prediction [Experiment, Analysis & Benchmark] [78.05103666987655]
  This work addresses challenges in accessing and utilizing diverse urban spatial-temporal datasets. We introduceatomic files, a unified storage format designed for urban spatial-temporal big data, and validate its effectiveness on 40 diverse datasets. We conduct extensive experiments using diverse models and datasets, establishing a performance leaderboard and identifying promising research directions.
  arXiv  Detail & Related papers  (2023-08-24T16:20:00Z)
• LibCity: A Unified Library Towards Efficient and Comprehensive Urban Spatial-Temporal Prediction [74.08181247675095]
  There are limitations in the existing field, including open-source data being in various formats and difficult to use. We propose LibCity, an open-source library that offers researchers a credible experimental tool and a convenient development framework.
  arXiv  Detail & Related papers  (2023-04-27T17:19:26Z)
• Statistical Deep Learning for Spatial and Spatio-Temporal Data [0.0]
  We present an overview of traditional statistical and machine learning perspectives for modeling spatial andtemporal data. We then focus on a variety of hybrid models that have recently been developed for latent process, data, and parameter specifications. These hybrid models integrate modeling ideas with deep neural network models in order to take advantage of the strengths of each modeling paradigm.
  arXiv  Detail & Related papers  (2022-06-05T16:49:10Z)
• From Data to Actions in Intelligent Transportation Systems: a Prescription of Functional Requirements for Model Actionability [10.27718355111707]
  This work aims to describe how data, coming from diverse ITS sources, can be used to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes. Grounded in this described data modeling pipeline for ITS, wedefine the characteristics, engineering requisites and intrinsic challenges to its three compounding stages, namely, data fusion, adaptive learning and model evaluation.
  arXiv  Detail & Related papers  (2020-02-06T12:02:30Z)

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.