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--- |
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license: cc-by-4.0 |
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tags: |
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- geospatial |
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- landuse, |
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- residential, |
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- non-residential, |
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- POI |
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--- |
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# POI-based land use classification |
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<!-- Provide a quick summary of the dataset. --> |
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## Dataset Details |
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POI-based land use datasets generated and shared by the [Geospatial Science and Human Security Division in Oak Ridge National Laboratory](https://mapspace.ornl.gov/). |
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This dataset classifies land use into three classes: residential, non-residential and open space. |
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The dataset has a spatial resolution of 500 meters and covers all countries and regions of the world except for the US and Greenland. |
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### Dataset Description |
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<!-- Provide a longer summary of what this dataset is. --> |
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- **Curated by:** Geospatial Science and Human Security Division in Oak Ridge National Laboratory |
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- **License:** cc-by-4.0 |
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## Uses |
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<!-- Address questions around how the dataset is intended to be used. --> |
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### Direct Use |
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<!-- This section describes suitable use cases for the dataset. --> |
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urban planning, transportation planning, population modeling, disaster risk assessment |
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## Dataset Structure |
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This dataset has four bands. The pixel values for residential, non-residential and open space bands are probabilities of the area being the land use class. |
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The 'classification' band classifies each pixel into one of the three land use classes with the highest probability. |
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### Source Data |
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Global POI data from PlanetSense Program. |
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## Bias, Risks, and Limitations |
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The POI data are not collected for US and Greenland. As a result, the land use result does not cover these two regions. |
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The training dataset used to train the land use classification model are based on OpenStreetMap land use polygons. |
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Some regions have better training data samples than other regions. |
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As a result, the land use classification model accuracy are not the same across the globe. |
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In the future, we will further improve the both the POI data and training data coverage for regions that have limited coverages. |
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## Citation |
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<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> |
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**APA:** |
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Fan, Junchuan & Thakur, Gautam (2024), Three-class Global POI-based land use map, Dataset, [https://doi.org/10.17605/OSF.IO/395ZF](https://doi.org/10.17605/OSF.IO/395ZF) |
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Fan, J., & Thakur, G. (2023). Towards POI-based large-scale land use modeling: spatial scale, semantic granularity and geographic context. International Journal of Digital Earth, 16(1), 430–445. |
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Thakur, G., & Fan, J. (2021). MapSpace: POI-based Multi-Scale Global Land Use Modeling. GIScience Conference 2021. |
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## Dataset Card Contact |
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[email protected] |
