A New Framework for Integrating DNN-Based Geographic Simulation Models within GISystems
文献类型：期刊论文
| 作者 | Zhang, Peng1,4; Wu, Wenzhou1,4; Xue, Cunjin3; Shi, Shaochen2,4; Su, Fenzhen1,4 |
| 刊名 | ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
 |
| 出版日期 | 2024-10-01 |
| 卷号 | 13期号:10页码:361 |
| 关键词 | tight integration geo-simulation models deep neural network GISystem island morphology |
| DOI | 10.3390/ijgi13100361 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | As a crucial spatial decision support tool, Geographic Information Systems (GISystems) are widely used in fields such as digital watersheds, resource management, environmental assessment, and regional governance, with their core strength lying in the integration of geographic simulation models from various disciplines, enabling the analysis of complex geographical phenomena and the resolution of comprehensive spatial problems. With the rapid advancement of artificial intelligence, deep neural network-based geographic simulation models (DNN-GSMs) have increasingly replaced traditional models, offering significant advantages in simulation accuracy and inference speed, and have become indispensable components in GISystems. However, existing integration methods do not adequately account for the specific characteristics of DNN-GSMs, such as their formats and input/output data types. To address this gap, we propose a novel tight integration framework for DNN-GSMs, comprising four key interfaces: the data representation interface, the model representation interface, the data conversion interface, and the model application interface. These interfaces are designed to describe spatial data, the simulation model, the adaptation between spatial data and the model, and the model's application process within the GISystem, respectively. To validate the proposed method, we construct a spatial morphology simulation model based on CNN-LSTM, integrate it into a GISystem using the proposed interfaces, and conduct a series of predictive experiments on island morphology evolution. The results demonstrate the effectiveness of the proposed integration framework for DNN-GSMs. |
| WOS关键词 | DEEP NEURAL-NETWORKS ; SYSTEM ; GIS ; EARTH |
| WOS研究方向 | Computer Science ; Physical Geography ; Remote Sensing |
| WOS记录号 | WOS:001343150400001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/209560]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Wu, Wenzhou |
| 作者单位 | 1.Univ Chinese Acad Sci, Coll Resource & Environm, Beijing 100049, Peoples R China 2.Lanzhou Jiaotong Univ, Fac Geomat, Lanzhou 730070, Peoples R China 3.Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Peng,Wu, Wenzhou,Xue, Cunjin,et al. A New Framework for Integrating DNN-Based Geographic Simulation Models within GISystems[J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,2024,13(10):361. |
| APA | Zhang, Peng,Wu, Wenzhou,Xue, Cunjin,Shi, Shaochen,&Su, Fenzhen.(2024).A New Framework for Integrating DNN-Based Geographic Simulation Models within GISystems.ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,13(10),361. |
| MLA | Zhang, Peng,et al."A New Framework for Integrating DNN-Based Geographic Simulation Models within GISystems".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 13.10(2024):361. |
入库方式： OAI收割
来源：地理科学与资源研究所
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