GeoGPT: Understanding and Processing Geospatial Tasks through An Autonomous GPT
School of Geography and Information Engineering, China University of Geosciences（中国地质大学地理与信息工程学院）；National Engineering Research Center for Geographic Information System, China University of Geosciences（中国地质大学地理信息系统国家工程研究中心）
Decision-makers in GIS need to combine a series of spatial algorithms and operations to solve geospatial tasks. For example, in the task of facility siting, the Buffer tool is usually first used to locate areas close or away from some specific entities; then, the Intersect or Erase tool is used to select candidate areas satisfied multiple requirements. Though professionals can easily understand and solve these geospatial tasks by sequentially utilizing relevant tools, it is difficult for non-professionals to handle these problems. Recently, Generative Pre-trained Transformer (e.g., ChatGPT) presents strong performance in semantic understanding and reasoning. Especially, AutoGPT can further extend the capabilities of large language models (LLMs) by automatically reasoning and calling externally defined tools. Inspired by these studies, we attempt to lower the threshold of non-professional users to solve geospatial tasks by integrating the semantic understanding ability inherent in LLMs with mature tools within the GIS community. Specifically, we develop a new framework called GeoGPT that can conduct geospatial data collection, processing, and analysis in an autonomous manner with the instruction of only natural language. In other words, GeoGPT is used to understand the demands of non-professional users merely based on input natural language descriptions, and then think, plan, and execute defined GIS tools to output final effective results. Several cases including geospatial data crawling, spatial query, facility siting, and mapping validate the effectiveness of our framework. Though limited cases are presented in this paper, GeoGPT can be further extended to various tasks by equipping with more GIS tools, and we think the paradigm of "foundational plus professional" implied in GeoGPT provides an effective way to develop next-generation GIS in this era of large foundation models.
Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations
GeoDS Lab, Department of Geography, University of Wisconsin-Madison, USA（威斯康星大学麦迪逊分校地理系GeoDS实验室）；Department of Geography and Regional Research, University of Vienna, Austria（维也纳大学地理与区域研究系）；Department of Geography, University of California-Santa Barbara, USA（加州大学圣塔芭芭拉分校地理系）
CURIE: Evaluating LLMs On Multitask Scientific Long Context Understanding and Reasoning
Google（谷歌）；Harvard（哈佛大学）；University of Zurich（苏黎世大学）；NIST（国家标准与技术研究院）；UMD College Park（大学公园大学分校）；Rutgers（罗格斯大学）；FU Berlin（柏林自由大学）；Modelyst；Cornell（康奈尔大学）
IC-EO: Interpretable Code-based assistant for Earth Observation
Université Paris Cité, LIPADE, F-75006 Paris, France askEarth AG, Zurich, Switzerland ESA -lab, Frascati, Italy ESA, Advanced Concepts；Studies Office, Noordwijk, the Netherlands
ShapefileGPT: A Multi-Agent Large Language Model Framework for Automated Shapefile Processing
School of Mathematics and Computer Science, Zhejiang Agriculture and Forestry University（浙江农林大学数学与计算机科学学院）；School of Earth Sciences, Zhejiang University（浙江大学地球科学学院）；Xiaohongshu（小红书）；Baichuan Inc.（百川智能）
