A number of heat scenarios studying how green infrastructure would affect the future climate of Stockholm. The dataset consist of a number of scenarios whisch simulates how...

extened: 4x4 and 1x11km simulations, various indicators: avg temperatue, avg preciptiation total, summer days, heat days, tropical nights Owner: AIT Notes: 1km urban climate...

Owner: City of Linz/AIT Notes: Merge of footprint and LIDAR data, tree cadastre

Owner: City of Linz Note: Lidar data

Owner. OGD Upper Austria Notes: Based on DKM (Digitale Katastralmappe)

Owner: EEA, European Commission Notes: Derived from remote sensing data updated version available:...

Ensemble climate simulations, based on different GHG emission scenarios Modeling of future climate scenarios Owner: CORDEX Responsible party http://euro-cordex.net/index.php.en...

Owner: EEA, European Commission Nots: Derived from remote sensing data Responsible party https://www.eea.europa.eu/data-and-maps/data/urban-atlas#tab-gis-data Responsible Party...

Nomalized Digital Surface Model (nDSM) Owner: City of Linz, extended Notes: Lidar data , 21/2 D city model

Owner. City of Linz, extended Note: remote sensing data

Owner. OGD Upper Austria Notes: Derived from remote sensing data

Distribution of population for Naples Municipality - census data Owner: ISTAT National Institute of Statistics Italy

Basins of Naples Municpality derived from DSM

Imperviousness layer was derived from remote sensing data (Pleiades images)

Digital Surface Model (DSM) was obtained from Lidara data. Owner: Naples Metropolitan City

Stem height was derived from remote sensing data (Pleiades images) and trees height

The building classification was obtained according to the construction material.

Leaf Area Index (LAI) was derived from remote sensing data (Pleiades images)

Leaf Area Density (LAD) was derived from remote sensing data (Pleiades images), computed from LAI and canopy height

Trees height was derived from remote sensing data (Pleiades images) and DSM and DTM