| --- |
| license: other |
| language: |
| - en |
| tags: |
| - GeoFM |
| - PlacePulse |
| - SpatialRepresentationLearning |
| - OpenStreetMap |
| - StreetView |
| - Multimodal |
| - Geospatial |
| pretty_name: Place Pulse 2.0 Multimodal |
| size_categories: |
| - 100K<n<1M |
| --- |
| |
| # 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_in_region** – interpolation evaluation. |
| - **validation_out_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 |
| @inproceedings{muehlematter2026urbanfusion, |
| title = {UrbanFusion: Stochastic Multimodal Fusion for Contrastive Learning of Robust Spatial Representations}, |
| author = {M{\"u}hlematter, Dominik J. and Che, Lin and Hong, Ye and Raubal, Martin and Wiedemann, Nina}, |
| booktitle = {International Conference on Machine Learning (ICML)}, |
| year = {2026}, |
| } |
| ``` |
| --- |
|
|
| ## 📊 References |
|
|
| [1] Dubey, A., Naik, N., Parikh, D., Raskar, R., and Hidalgo, C. A. (2016). Deep learning the city: Quantifying urban perception at a global scale. In ECCV, pp. 196–212.<br> |
| [2] Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., ... Bargellini, P. (2012). Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sensing of Environment, 120:25–36.<br> |
| [3] OpenStreetMap contributors (2017). Planet dump retrieved from https://planet.osm.org<br> |
