mKGR

The official code of Remote Sensing of Environment paper Learning to Reason over Multi-Granularity Knowledge Graph for Zero-shot Urban Land-Use Mapping.

Abstract: This paper introduces a multi-granularity knowledge graph reasoning (mKGR) framework. Only with indirect supervision from other tasks, mKGR can automatically integrate multimodal geospatial data as varying granularity entities and rich spatial-semantic interaction relationships. Subsequently, mKGR incorporates a novel fault-tolerant knowledge graph embedding method to establish relationships between geographic units and land-use categories, thereby reasoning land-use mapping outcomes. Extensive experiments demonstrate that mKGR not only outperforms existing zero-shot approaches but also exceeds those with direct supervision. Furthermore, this paper reveals the superiority of mKGR in large-scale holistic reasoning, an essential aspect of land-use mapping. Benefiting from mKGR's zero-shot classification and large-scale holistic reasoning capabilities, a comprehensive urban land-use map of China is generated with low-cost.

• [x] Products: Publicly accessible on ArcGIS Online, download the products on Zenodo.
• [x] Code: Publicly available in this repository.
• [x] Dataset: Publicly available on Zenodo.

Ubuntu 20.04 (or other Linux distribution), one GPU (video memory greater than 12GB and support cuda)

• python>=3.11.5
• numpy>=1.26.2
• pytorch>=2.2.1
• pandas>=2.2.2
• geopandas>=0.14.0

MGKG Construction

Option 1: Directly download the constructed graph.

Option 2：Construct the graph in the KG_construction folder.

MGKG Reasoning for Land-use Mapping

Train the graph embedding in the KG_embedding folder and infer to obtain the land-use mapping result.

Other Code

figure_script: The code for generating the figures in the paper.

landuse_app: The code for 15-minute city application of land-use mapping results.

We have published the land-use mapping and 15-minute walkability results of China on ArcGIS Online.

Contact

If you have any questions about it, please let me know. (Create an 🐛 issue or 📧 email: wangfaye@whu.edu.cn)