Published May 13, 2026
| Version v1
Dataset
Open
Trained deep learning models for street-level land surface temperature analysis using Google Street View imagery
Authors/Creators
-
1.
Katholieke Universiteit Leuven
-
2.
Lund University
Description
This dataset contains the trained machine learning models used in the study “Differentiated street greenery cooling of road surfaces: Spatially varying nonlinear effects from street view and geographically weighted machine learning.” It includes (i) a global Random Forest model for land surface temperature prediction, (ii) a GeoShapley‑based Random Forest model for spatially explicit model interpretation, and (iii) a fine‑tuned Mask2Former semantic segmentation model for extracting street‑level vegetation and built‑environment features from Google Street View imagery.
The paper was published in Sustainable Cities and Society: https://doi.org/10.1016/j.scs.2026.107501
All code and model pipelines are made open source on: https://github.com/toolambr/street_greenery
Files
fine_tuned_mask2former.zip
Files
(1.1 GB)
| Name | Size | Download all |
|---|---|---|
|
md5:43dd87fea3189cc3e33e06624be4a755
|
800.3 MB | Preview Download |
|
md5:9877ab023cd10d96c5c2a7aa168a7dde
|
279.2 MB | Download |
|
md5:a85b2675e4b608944d6cc5513522daf6
|
25.3 MB | Download |
Additional details
Related works
- Is supplement to
- Journal: 10.1016/j.scs.2026.107501 (DOI)
- Workflow: https://github.com/toolambr/street_greenery (URL)
Software
- Repository URL
- https://github.com/toolambr/street_greenery