Peilong Ma is a PhD candidate in Cartography and Geographic Information Systems at Nanjing Normal University, advised by Prof. Min Chen. His research centers on intelligent geographic modeling, geospatial information services, AI for Science, and open science infrastructures.
He studies how domain knowledge, model resources, and computational services can be organized into reusable workflows for geographic analysis, simulation, and decision support. He contributes to OpenGMS and OpenGMP and develops research software such as PyGeoModel and OpenGeoLab to support model discovery, sensitivity analysis, and reproducible workflows.
His recent work includes knowledge-driven hydrological sensitivity analysis, open knowledge frameworks for geoscientific models, and interactive tools for geographic modeling. He has authored 10 journal papers, received nearly 200 citations, and holds five Chinese invention patents together with two software copyrights.
Preprint, under review
An under-review paper on PyGeoModel for integrating intelligent geographic model services into Jupyter-based urban analysis.
SSRN preprint, under review
An under-review paper on AI-driven runtime estimation and scheduling for distributed geo-simulation tasks.
ISPRS 2026 full paper accepted for oral presentation; to appear in the ISPRS Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
An accepted ISPRS 2026 conference paper on ReAct-based agent workflows for automated web map generation from natural language instructions.
Preprint, under review
A preprint on integrating service-oriented resources and reproducible workflows for geographic modeling.
Journal of Hydrology, 660, 133406
A knowledge-driven workflow for hydrological model sensitivity analysis using distributed online model services.
Transactions in GIS, 28(2), 154-175
An open knowledge framework for discovering, linking, and reusing geoscientific models.
Remote Sensing, 14(3), 599
Fine-grained PM2.5 retrieval using multi-source data and multiple machine learning models.
An intelligent Python package for urban and geographic modeling within Jupyter environments, integrating model recommendation, interactive configuration, question answering, and service-based execution.
An MCP server for geospatial data processing tools, exposing WhiteBox and SAGA GIS style capabilities through a standardized interface for language-model-based agents and desktop assistants.
An intelligent segmentation system for remote sensing and GIS imagery, supporting text-prompt segmentation, point-based refinement, GeoTIFF overlay browsing, Shapefile export, and optional voice interaction.