Update README.md

README.md

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