Update README.md

README.md

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-### Dataset Summary
+# PP2-M: Place Pulse 2.0 - Multimodal
+
+**PP2-M** (Place Pulse 2.0 - Multimodal) is a dataset based on the original Place Pulse 2.0 dataset [1], enriched with additional geospatial modalities for training **multimodal Geo-Foundation Models (GeoFM)**.  
+
+The dataset includes aligned pairs of the following modalities:
+
+- 🌍 **Geographical coordinates** (lat, lon) from Place Pulse 2.0 [1]  
+- 🏙 **Street view images** from Place Pulse 2.0 [1]  
+- 🛰 **Remote sensing images** from Sentinel-2 [2]  
+- 🗺 **Cartographic basemaps** from OpenStreetMap [3]  
+- 📍 **Points of interest (POIs)** from OpenStreetMap [3]  
+
+---
+
+## 📜 License
+Due to its multimodality, PP2-M comes with **different licenses per modality**, as described in the folder [`LICENSES`](./LICENSES).
+
+---
+
+## 📑 Modalities Description
+
+### 📌 Coordinates
+- **110,988 locations**, each with associated geographic coordinates.
+
+### 🏙 Street View Images (SVI)
+- Obtained from **Google Street View.  
+- Resolution: **400 × 300 pixels**.
+
+### 🛰 Remote Sensing Images (Sentinel-2)
+- Sentinel-2 **Level-2A** images.  
+- Acquisition period: **Jan 1 – Dec 31, 2024**.  
+- Filtered for minimal cloud coverage.  
+- Each patch includes spectral bands:  
+  `B01, B02, B03, B04, B05, B06, B07, B08, B08A, B09, B11, B12`  
+- Resolution: **256 × 256 pixels**.
+
+### 🗺 Cartographic Basemaps (OSM_basemaps)
+- Tiles from **OpenStreetMap tile server**.  
+- Zoom levels: **15, 16, 17** → resolutions of **1200 m, 600 m, 300 m**.  
+- Downloaded: **May 2025**.  
+- Rendered at **256 × 256 pixels**.
+
+### 📍 Points of Interest (OSM_pois)
+- Extracted from **OpenStreetMap**.  
+- For each location: up to **15 nearest POIs within 200 m**.  
+- Adaptive search radius ensures coverage in sparse areas.  
+- Retained POIs with tags:  
+  `amenity, shop, leisure, tourism, healthcare, theatre, cinema, building=religious, building=transportation, public_transport=station`  
+- **Excluded**: `parking, parking_space, bench, bicycle_parking, motorcycle_parking, post_box, toilets`  
+- Each POI is assigned a **representative category** (priority order: `amenity → leisure → religion → public_transport → shop → tourism`).  
+- Special cases:  
+  - `healthcare` if substring matches  
+  - `museum` if name contains "museum"  
+- Final POIs are used to construct **textual prompts** describing each POI’s name, category, and distance.  
+
+---
+
+## 📂 Folder Structure
+
+PP2-M/ <br>
+│<br>
+├── LICENSES/             → Licenses for all modalities<br>
+├── Tables_statistics/    → Statistics & tables (based on Place Pulse 2.0)<br>
+├── SVI/                  → Street View Images<br>
+├── sentinel2/            → Sentinel-2 images<br>
+├── OSM_basemaps/         → OSM basemaps (zoom 15, 16, 17)<br>
+├── OSM_pois/             → Raw POIs + generated text prompts<br>
+└── Precomputed_features/ → Pre-extracted modality-specific features<br>
+
+
+## 🔀 Dataset Splits
+- **Training** – samples used for training.  
+- **validation_out_region** – interpolation evaluation.  
+- **validation_in_region** – extrapolation evaluation (unseen cities).  
+
+---
+
+## 📊 Precomputed Features
+In addition to raw data, we provide **pre-extracted features** from each modality using modality-specific models.  
+See details in our paper: [UrbanFusion](https://github.com/DominikM198/UrbanFusion/).
+
+---
+
+## 📖 Citation
+If you use PP2-M, please cite our work:
+
+```bibtex
+@article{muehlematter2025urbanfusion,
+  title   = {UrbanFusion: Stochastic Multimodal Fusion for Contrastive Learning of Robust Spatial Representations},
+  author  = {Dominik J. Mühlematter and Nina Wiedemann and Lin Che and Ye Hong and Martin Raubal},
+  year    = {2025},
+  journal = {arXiv preprint arXiv:xxxx.xxxxx}
+}
+