摘要
随着生成式人工智能(AIGC)为代表的新一代人工智能的快速发展,加速了各个学科转向人工智能驱动的科学研究,地理空间智能(GeoAI)技术在解决传统制图学任务注定会比传统的方法具有更好的性能,地图学也因此迎来了新的机遇与挑战,产生智能地图制图新领域,形成智能地图制图学。地图学研究有人工智能传统,但是过去受限于人工智能工具的计算能力等原因,并未取得很大的进展。随着进入智能化时代,人与机器都将成为制图与读图的主体。地图内容的产生经历了专家生成内容和用户生成内容的阶段,正在向人工智能生成内容阶段发展。人工智能与地图传输模型的结合衍生出智能地图传输模型,包括制图信息智能获取、智能制图、智能读图、地图信息智能解读4个环节,进而从这4个方面对智能地图的研究进展进行了分析和梳理。研究显示,使用智能化方法解决地图学问题的研究仍然处于起步阶段,人工智能与地图学结合仍存在诸多挑战,包括缺乏训练数据集、模型算法缺乏泛化能力和可解释性等,这些也是未来可以发展的方向。
As Artificial Intelligence Generated Content(AIGC)rapidly advances,various disciplines are shifting toward AI-driven scientific research.GeoAI technology,which focuses on geographic spatial intelligence,has the potential to outperform traditional methods in solving cartographic tasks.This shift presents both new opportunities and challenges for cartography.Despite some progress in integrating AI into cartographic research,limitations in computational power and other factors have hindered significant success in the past.As we enter the era of intelligence,both humans and machines will play critical roles in map creation and interpretation.Through artificial intelligence algorithms,maps can be produced quickly,at low cost,and on a large scale.However,there are also issues such as the instability of the quality of map works.The generation of map content has gone through the stages of expert-generated content and user-generated content and is developing towards the stage of artificial intelligence-generated content.In the traditional map-making phase,professional maps are produced by cartographic experts.While the quality of these maps is assured,the number of experts is limited.Consequently,the production cycle is long,the cost is high,the quantity of map products is limited,and they have not been produced on a large scale.At the current stage,generative artificial intelligence can produce map content in three forms:text-to-map(txt2map),map-to-text explanation(map2txt),and map style transfer(map2map).People can already use ChatGPT to generate maps by entering a piece of text,produce a textual explanation of a map by uploading an image of the map to ChatGPT,and even achieve map style transfer from images using Generative Adversarial Networks(GANs).The integration of artificial intelligence with the map transmission model has derived an intelligent map transmission model.It includes four stages:(1)Intelligent acquisition of mapping information:Sampling and collecting information about the real-world geographical environment through artificial intelligence methods,which is then processed and filtered into structured information for mapping;(2)Intelligent mapping:The process of intelligently generating maps through the use of colors,symbols,grading,and other representational methods based on mapping information;(3)Intelligent map reading:The process by which readers use artificial intelligence methods,combined with map language,domain knowledge,and personal understanding,to recognize the real world;(4)Intelligent interpretation of map information:Using artificial intelligence to interpret maps,thereby gaining cognition and understanding of the real world.Although progress has been made,research on using intelligent methods to address cartographic challenges is still in its early stages.Challenges include the lack of comprehensive training datasets,limited model algorithm generalization,and interpretability.These areas offer promising directions for future development.
作者
张岸
朱俊锴
ZHANG An;ZHU Junkai(State Key Laboratory of Resources and Environmental Information System,Institute of Geographical Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China;University of Chinese Academy of Sciences,Beijing 100049,China)
出处
《地球信息科学学报》
EI
CSCD
北大核心
2024年第1期35-45,共11页
Journal of Geo-information Science
基金
中国科学院战略性先导科技专项项目(XDB0740100)
国家重点研发计划项目(2022YFC3002804)。
