The official code of 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.
- Products: Publicly accessible on ArcGIS Online.
- Code: Publicly available in this repository.
- Dataset: Restricted access on Zenodo (to be released upon paper publication; email us for early access).
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
Option 1: Directly download the constructed graph.
Option 2：Construct the graph in the KG_construction folder.
Train the graph embedding in the KG_embedding folder and infer to obtain the land-use mapping result.
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.
If you have any questions about it, please let me know. (Create an 🐛 issue or 📧 email: [email protected])
