Datasets:

DominikM198
/

PP2-M

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.

📑 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/
│
├── LICENSES/ → Licenses for all modalities
├── Tables_statistics/ → Statistics & tables (based on Place Pulse 2.0)
├── SVI/ → Street View Images
├── sentinel2/ → Sentinel-2 images
├── OSM_basemaps/ → OSM basemaps (zoom 15, 16, 17)
├── OSM_pois/ → Raw POIs + generated text prompts
└── Precomputed_features/ → Pre-extracted modality-specific features

🔀 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.

📖 Citation

If you use PP2-M, please cite our work:

@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.
[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.
[3] OpenStreetMap contributors (2017). Planet dump retrieved from https://planet.osm.org