Smart City Energy

Modules

Registered users can create Module Configurations based on processes available on various remote web servers. The Catalog below shows some of the modules that other users of iGUESS are using.

Aggregation service with support for slider tool

Identifier: aggregation
This process aggregates previously calculated data sets according to vector data sets. The result will be available for visualization in the slider tool.
Hosted by: Smart City and Region Energy PyWPS - Luxembourg Institute of Science and Technology
Model Inputs
Parameter NameIdentifierDescription
Aggregation level [aggregate_level] A vector polygon data set which represents the aggregation level, which could be block or district level polygons.
Base layer to aggregate [aggregate_basemap] A data set used for aggregation. to be filled...
Model Outputs
Parameter NameIdentifierDescription
Aggregated data [aggregation_result] Aggregated data. This result can be used as input for the 'slider application'

Building stock energy consumption

Identifier: energy_savings
This process will create energy consumption map for buildings in an urban environment using a statistical model. Energy consumption of the building stock available at an aggregated level will be disaggregated to single buildings/dwellings using multiple linear regression analysis based on several building descriptors (housing type, period of construction, floor surface). Significant variables will be automatically selected by performing stepwise regression analysis (Akaike information criterion, stepwise search in both directions) Results will be produced and shown as maps for single buildings/dwellings and for a target level to be used for decision support (e.g. block level). A report of the regression results will be produced in output and can be downloaded at http://wps.iguess.tudor.lu/wpsoutputs/file_name.html where 'file_name' is the file name indicated for the regression report file. The report includes the list of variables selected by the stepwise regression, the estimated regression coefficients, standard error, t-value, significance, R-squared and diagnostic plots.
Hosted by: Smart City and Region Energy PyWPS - Luxembourg Institute of Science and Technology
Model Inputs
Parameter NameIdentifierDescription
Building data [input_address] A vector point layer containing input data at address / building level used to predict the energy consumption. The following attributes should be attached: ID for post-code area; ID for the target level; number of dwellings; total floor surface in square meters; housing type identified by integer numbers (e.g. '1'); year of construction (e.g. '1972'). Column names should be the following: 'id_pc'; 'id_block', 'n_units','floor','type','year'. Housing types can be freely defined depending on the goal of the analysis and should match with the ones provided in the 'Housing categories' .csv file.
File name for the linear regression report. [input_report_name] Name of the linear regression report file that will be produced in output (e.g. file_name).
Housing categories [input_categories] A table in .csv format containing information about the housing types and periods of construction to be used for the disaggregation of energy consumption. Every row represents the combination of a specific housing type and period of construction. The table should contain 4 columns named as follows: 'type' containing information bout the housing type identified by one integer number (e.g. '1') and matching with the ones defined in the 'Building data' file; 'type.ext' containing the codes for housing types that will be shown in output from linear regression. Each code should be represented by a string of maximum 4 digits (e.g. 'dh' for detached houses) and should univocally match with the relative housing type number in the column "type"; 'period.start' containing the starting year of a certain period of construction (e.g.'1965' for the period of construction 1965-1974); 'period.end' containing the final year of a certain period of construction (e.g.'1974' for the period of construction 1965-1974). Column separator in the file should be ';'. An example file can be found at http://wps.iguess.tudor.lu/pywps/sampleData/energy_savings/housing_categories_example.csv
Measured consumption data [input_post_code] A vector polygon dataset containing measured energy consumption data. Measured consumption will be used as input to run the statistical model and disaggregate energy consumption. Polygons represent the areas where consumption data are available (e.g. post-code areas). The following two attribute columns should be attached: 'id_pc' ID for the area; 'y_gas' average energy consumption per dwelling, e.g. natural gas (m3/dw a) or electricity (kWh/dw a).
Target level [input_block] A vector polygon dataset at the target level where results are wished to be shown (e.g.block level). The following attributes should be attached: 'block_id'ID for the target level (block level)
Model Outputs
Parameter NameIdentifierDescription
Energy consumption predicted at the address level [energy_dwelling] Energy consumption predicted at the address level
Energy consumption predicted at the block level [energy_block] Energy consumption predicted at the block level
Energy consumption predicted at the post-code level [energy_post_code] Energy consumption predicted at the post-code level

Building stock energy savings potential

Identifier: energy_savings_benchmark
This process will create energy demand and energy savings potential map for buildings in an urban environment. The estimation is based on national benchmark values and assumes the implementation of standard retrofit measures. Input requirements for buildings include floor surface, housing type (detached, terraced house, apartment block, etc.) and year of construction. Benchmark values are provided for Belgium, France, Germany, Netherlands and United Kingdom (England). Results will be produced and shown as maps for single buildings.
Hosted by: Smart City and Region Energy PyWPS - Luxembourg Institute of Science and Technology
Model Inputs
Parameter NameIdentifierDescription
Benchmark values [input_benchmark] URL to a table in .csv format containing national or local benchmark values for the heating energy demand (kWh/m2 a), total primary energy (kWh/m2 a), CO2 emission (kg/m2 a) and heating cost (euro/m2 a) in the present state and renovated state of buildings per housing type and period of construction. A reference table containing national benchmark values is provided at the URL: http://wps.iguess.tudor.lu/pywps/sampleData/energy_savings_benchmark/input_benchmark.csv Data should be residing at a web accesible location e.g. Dropbox or public web server URL. Reference documents for benchmark values provided are the following: The Tabula Project. http://episcope.eu/iee-project/tabula/ (Belgium, France, Germany); N.L. Agentschap, "Voorbeeldwoningen 2011, Bestaande bouw", 2011 (Netherlands); Building Typology Brochure England, BRE output 296 024, 2014 (UK, England). Note: In the reference data not all benchmark values are available for every Country, benchmark values not available will result in "NA" values or missing attributes in output.
Country [idcountry] Country identifier referring to the set of national benchmark values: 1 = Belgium; 2 = France (zone climatique 1); 3 = Germany; 4 = Netherlands; 5 = United Kingdom (England).
Input data for buildings [input_building] A vector polygon dataset containing building information. The following attributes should be attached: total floor surface in square meters (column name: "floor"), housing type (column name: "type"), year of construction (column name: "year"). All values should be provided as "Integer". Housing types are identified by numbers (from 1 to 6) according to the following code: 1 = detached house; 2 = semi-detached house; 3 = terraced house; 4 = small multi-family house; 5 = multi-family house; 6 = apartment block. Notes: benchmark values for the housing types 2 and 4 are currently available only for Belgium and the Netherlands; for the Netherlands the following convention applies: 4 = maisonette; 5="galerij" apartment block; 6="portiek" apartment block. Missing values for floor surface, type and year of construction should be marked with "NA" and will result in Null values in output.
Model Outputs
Parameter NameIdentifierDescription
Energy savings for buildings [energy_building] A vector polygon dataset of buildings with the following attributes: heating energy demand (kWh/a), total primary energy (kWh/a), CO2 emission (kg/a) and heating cost (euro/a) in the present state, renovated state and reduction potential. Note: outputs depend on the availability of benchmark values.

Building stock energy savings potential (Engineering model)

Identifier: daedalus_01_gis
This process calculates geometrical features of buildings at an urban scale, including average height, surface of external walls and volume. Results can be used in input to the module "Building Stock Characterization" and are needed for the calculation of energy savings potential and Life Cycle Assessment of the building stock.
Hosted by: Smart City and Region Energy PyWPS - Luxembourg Institute of Science and Technology
No Inputs
No Outputs

Geothermal cadastre

Identifier: geothermal_cadastre
This process is not working yet. It should generate a sampling scheme for boreholes providing geothermal energy.
Hosted by: Smart City and Region Energy PyWPS - Luxembourg Institute of Science and Technology
Model Inputs
Parameter NameIdentifierDescription
Dummy input, min,mac_occurs = 0 [dummy]
Input building footprint polygons [building_footprints]
Parcels [parcels] A data set containing parcels
Some buffer width [bufferwidth]
Value of Borehole interdistance [interdistance]
Model Outputs
Parameter NameIdentifierDescription
Buffer output as GML 2.1.2 [geothermal_boreholes]

Green roofs

Identifier: green_roofs
This module classifies roof patches that are suitable for different types of green roof installations.
Hosted by: Smart City and Region Energy PyWPS - Luxembourg Institute of Science and Technology
Model Inputs
Parameter NameIdentifierDescription
Maximum roof slope where green can be built [max_roof_slope] Maximum slope angle of the roof where a green roof can be installed. The default is set to 10 degrees which is usually a threshold where green roofs are not too expensive to be built. Above 10 degrees measures to prevent growing medium movement and erosion must be taken.
Roof patches with suitable area for green roof installations [roof_patches] A vector data set which delinates the suitable roof patches for a green roof installation. This input is an output of the solar irradiation module of iGUESS.
Model Outputs
Parameter NameIdentifierDescription
Classification of possible green roof installations [green_roofs_locations] Possible locations of green roof installations. All patches with higher than the given max_slope are skipped (usually 10 degrees). According to different sources cultivation is then very expensive because of soil erosion and water issues. If a flat roof top is found a kind of horticulture should be placed where people could grow food. Intensive or extensive green roofs are classified if the third quartile of slope of the roof patch is not higher than max_slope.

GTUI Isochrones

Identifier: gtui_isochrones
This process calculates isochrones. to be filled with more text...
Hosted by: Smart City and Region Energy PyWPS - Luxembourg Institute of Science and Technology
Model Inputs
Parameter NameIdentifierDescription
BBOX East coordinate in EPSG:3857 [bbox_e] Coordinate value of East boundary for the resulting image.
BBOX North coordinate in EPSG:3857 [bbox_n] Coordinate value of North boundary for the resulting image.
BBOX South coordinate in EPSG:3857 [bbox_s] Coordinate value of South boundary for the resulting image.
BBOX West coordinate in EPSG:3857 [bbox_w] Coordinate value of West boundary for the resulting image.
East coordinate in EPSG:3857 [x_coord] Coordinate value of X (East) of starting point for isochrone calculation.
Height in Pixels of resulting PNG [dim_height]
Means of transport [network_id] This textual input will identify by which mean of transport you will travel. Should be either "pedestrian", "bicycle" or "car".
North coordinate in EPSG:3857 [y_coord] Coordinate value of Y (North) of starting point for isochrone calculation.
Should modified network, according to future infrastructure, be used in calculation? [network_modifier] 0 or 1. Only avalibale for bicycles!
Width in Pixels of resulting PNG [dim_width]
Model Outputs
Parameter NameIdentifierDescription
Isochrones PNG [isochrones_png] PNG showing segments of isochrones for 5, 10 and 20 minutes of traveltime for the chosen means of transport. It reassembles the given bounding box

Land Cover Change Model

Identifier: estimum_lcc_model
Land Cover Change Model of the ESTIMUM project
Hosted by: Smart City and Region Energy PyWPS - Luxembourg Institute of Science and Technology
Model Inputs
Parameter NameIdentifierDescription
Land Cover [land_cover] Select Land Cover Map
Land Cover Category [choose_land_cover] Choose Land Cover Category
Land Cover Expansion Rate [choose_lc_expansion] Choose Land Cover expansion rate [ha]
Mapping Grid [mapping_grid] Select Mapping Grid Map
Protected Areas [protected_areas] Select Protected Areas Map
Sampling Grid [sampling_grid] Select Sampling Grid Map
Model Outputs
Parameter NameIdentifierDescription
Land Cover expansion map [lc_expansion_map] Define Land Cover expansion map

org.n52.wps.server.algorithm.SimpleBufferAlgorithm

Identifier: org.n52.wps.server.algorithm.SimpleBufferAlgorithm
Hosted by: 52°North WPS 4.0.0-beta.4-SNAPSHOT
Model Inputs
Parameter NameIdentifierDescription
data [data]
width [width]
Model Outputs
Parameter NameIdentifierDescription
result [result]

PV Potential for Esch city

Identifier: pv_potential_mod_esch
This process calculates the electrical potential on roof tops based on different inputs of economical parameters of PV panel technologies. Inputs are spatial data sets from solar irradiation module and economic parameters given by the user.
Hosted by: Smart City and Region Energy PyWPS - Luxembourg Institute of Science and Technology
Model Inputs
Parameter NameIdentifierDescription
Building footprints [building_footprints] A vector polygon data set which represents the building footprints. Except of feature geometries no other additional information is needed. If attribute data is attached it will be ingnored during import process.
Economic panel life time in years [econ_lifetime] Envisioned economic panel life time in years [a]. Decimal separator must be . (period)
Panel cost in Euro [panel_cost] Panel costs in Euro per square meter of installation. Usually in a range from 1500 to 2000 Euro/m^2. Typical values for amorph, multi- or mono-crystalline panels would be 1500, 1800 or 2000 Euro/m^2 respectively. Decimal separator must be . (period)
Panel efficiency [panel_efficiency] Panel efficiency in percent defined by the user. Usually in a range of 8 to 20 percent. Typical values for amorph, multi- or mono-crystalline panels would be 8.0, 14.0 or 16.0 percent respectively. Decimal separator must be . (period)
Payback price [payback_price] Guaranteed payback price over the economic panel lifetime in Euro per kWh [Euro/kWh]. Decimal separator must be . (period).
Roof patches with suitable area for PV installations [roof_patches] A vector data set which delinates the suitable roof patches for a solar PV installation. This input is an output of the solar irradiation process of iGUESS.
Solar Irradiation [solar_irradiation] A raster data set representing solar irradiation on rooftops. This input can be an output of the solar irradiation module of iGUESS, but can also be delivered by the cities themselves as preprocessed data raster set. Pixel value must be yearly sum of irradiation in kWh per square meter.
Model Outputs
Parameter NameIdentifierDescription
PV potential [pv_potential] PV potential generated for roof patches given as input. Several new columns are attached to the generated data set: user_sum = Yearly sum of solar irradiation in kWh available for roof patch, user_generated_energy = potential energy harvested in kWh by roof patch per year with given parameters, user_investment = total investment over envisioned panel life time taking into account maintenance of panels, user_cost = cost/energy ratio seen over envisioned panel life time, csum_area, csum_investment, csum_generated_energy = cumulative sums of named columns used for visualisation in the slider tool. This result can be used as input for the 'slider application'.

PV Potential with user based input

Identifier: pv_potential_user
This process calculates the electrical potential on roof tops based on different inputs of economical parameters of PV panel technologies. Inputs are spatial data sets from solar irradiation module and economic parameters given by the user.
Hosted by: Smart City and Region Energy PyWPS - Luxembourg Institute of Science and Technology
Model Inputs
Parameter NameIdentifierDescription
Building footprints [building_footprints] A vector polygon data set which represents the building footprints. Except of feature geometries no other additional information is needed. If attribute data is attached it will be ingnored during import process.
Economic panel life time in years [econ_lifetime] Envisioned economic panel life time in years [a]. Decimal separator must be . (period)
Panel cost in Euro [panel_cost] Panel costs in Euro per square meter of installation. Usually in a range from 1500 to 2000 Euro/m^2. Typical values for amorph, multi- or mono-crystalline panels would be 1500, 1800 or 2000 Euro/m^2 respectively. Decimal separator must be . (period)
Panel efficiency [panel_efficiency] Panel efficiency in percent defined by the user. Usually in a range of 8 to 20 percent. Typical values for amorph, multi- or mono-crystalline panels would be 8.0, 14.0 or 16.0 percent respectively. Decimal separator must be . (period)
Payback price [payback_price] Guaranteed payback price over the economic panel lifetime in Euro per kWh [Euro/kWh]. Decimal separator must be . (period).
Roof patches with suitable area for PV installations [roof_patches] A vector data set which delinates the suitable roof patches for a solar PV installation. This input is an output of the solar irradiation process of iGUESS.
Solar Irradiation [solar_irradiation] A raster data set representing solar irradiation on rooftops. This input can be an output of the solar irradiation module of iGUESS, but can also be delivered by the cities themselves as preprocessed data raster set. Pixel value must be yearly sum of irradiation in kWh per square meter.
Model Outputs
Parameter NameIdentifierDescription
PV potential [pv_potential] PV potential generated for roof patches given as input. Several new columns are attached to the generated data set: user_sum = Yearly sum of solar irradiation in kWh available for roof patch, user_generated_energy = potential energy harvested in kWh by roof patch per year with given parameters, user_investment = total investment over envisioned panel life time taking into account maintenance of panels, user_cost = cost/energy ratio seen over envisioned panel life time, csum_area, csum_investment, csum_generated_energy = cumulative sums of named columns used for visualisation in the slider tool. This result can be used as input for the 'slider application'.

Solar irradiation

Identifier: solar_irradiation
This process calculates the sum of yearly irradiation (in kWh/m^2) on a roof surface, taking into account cloud cover and other local conditions. This process uses the r.sun GRASS module and is described in detail in the CRTE internal report "Development of a Solar Energy Cadastre using a GIS-based Solar Radiation Model".
Hosted by: Smart City and Region Energy PyWPS - Luxembourg Institute of Science and Technology
Model Inputs
Parameter NameIdentifierDescription
Building footprints [building_footprints] A vector polygon data set which represents the building footprints. Except of feature geometries no other additional information is needed. If attribute data is attached it will be ignored during import process.
Cloud cover measured in octa averaged over the last 30 years [meteo_octa] A vector point data set which represents the daily cloud cover measured in octa.
Column name used for classification of features used in training area dataset [training_roof_classification_column] Field of type INTEGER which specifies the classification attribute of the four existing training classes (e.g. 1,2,3,4)
Digital Surface Model [dsm] A raster data set representing a Digital Surface Model of the city. The resolution should be as small as possible (~1m) to have roof objects detected. The data set should not have wholes (NULL values). They should be filled by preprocessing. Units should be meters.
Linke turbidity [meteo_linke] A vector point data set which represents the monthly values of Linke's turbidity factor.
Ratio of diffuse to global irradiation [meteo_ratio] A vector point data set which represents the monthly values of Ratio of diffuse to global irradiation factor.
Training areas for roof top classification [training_roof_classification] A vector data set that contains four classes of roof training areas. First class should indicate flat roofs, second class pitched roofs, third class totally useless areas like current construction sites with a lot of pixel noise, fourth class border areas of buildings. Data type of column must be of value INTEGER (e.g. 1,2,3,4).
Model Outputs
Parameter NameIdentifierDescription
Solar irradiation [solar_irradiation] A raster data set which contains the solar irradiation based on the input data sets. Unit is kWh per year and square meter. Data can be used to feed the PV potential module.
Suitable roof patches [roof_patches] A vector data set which delinates the suitable roof patches for a PV or green roof installation. Additionally, univariate statistics about slope of the patch are given. This is further used in the green roofs module. Column "type" characterises the type of the roof: 1 = flat roof, 2 = pitched roof. Furthermore a smmothing algorithm is applied to reduce the number of vertices of the patches. This will speed up visualisation in the slider tool. Data can be used to feed the PV potential or green roof module.

Solar irradiation geomorphon

Identifier: solar_irradiation_geomorphon
This process calculates the sum of yearly irradiation (in kWh/m^2) on a roof surface, taking into account cloud cover and other local conditions. This process uses the r.sun GRASS module and is described in detail in the CRTE internal report "Development of a Solar Energy Cadastre using a GIS-based Solar Radiation Model". It uses geomorphon algroithm to detect roof structures
Hosted by: Smart City and Region Energy PyWPS - Luxembourg Institute of Science and Technology
Model Inputs
Parameter NameIdentifierDescription
Building footprints [building_footprints] A vector polygon data set which represents the building footprints. Except of feature geometries no other additional information is needed. If attribute data is attached it will be ignored during import process.
Cloud cover measured in octa averaged over the last 30 years [meteo_octa] A vector point data set which represents the daily cloud cover measured in octa.
Digital Surface Model [dsm] A raster data set representing a Digital Surface Model of the city. The resolution should be as small as possible (~1m) to have roof objects detected. The data set should not have wholes (NULL values). They should be filled by preprocessing. Units should be meters.
Linke turbidity [meteo_linke] A vector point data set which represents the monthly values of Linke's turbidity factor.
Ratio of diffuse to global irradiation [meteo_ratio] A vector point data set which represents the monthly values of Ratio of diffuse to global irradiation factor.
Model Outputs
Parameter NameIdentifierDescription
Solar irradiation [solar_irradiation] A raster data set which contains the solar irradiation based on the input data sets. Unit is kWh per year and square meter. Data can be used to feed the PV potential module.
Suitable roof patches [roof_patches] A vector data set which delinates the suitable roof patches for a PV or green roof installation. Additionally, univariate statistics about slope of the patch are given. This is further used in the green roofs module. Column "type" characterises the type of the roof: 1 = flat roof, 2 = pitched roof. Furthermore a smmothing algorithm is applied to reduce the number of vertices of the patches. This will speed up visualisation in the slider tool. Data can be used to feed the PV potential or green roof module.

Solar irradiation imaxlik

Identifier: solar_irradiation_imaxlik
This process calculates the sum of yearly irradiation (in kWh/m^2) on a roof surface, taking into account cloud cover and other local conditions. This process uses the r.sun GRASS module and is described in detail in the CRTE internal report "Development of a Solar Energy Cadastre using a GIS-based Solar Radiation Model". It uses i.maxlik algroithm to detect roof structures
Hosted by: Smart City and Region Energy PyWPS - Luxembourg Institute of Science and Technology
Model Inputs
Parameter NameIdentifierDescription
Building footprints [building_footprints] A vector polygon data set which represents the building footprints. Except of feature geometries no other additional information is needed. If attribute data is attached it will be ignored during import process.
Cloud cover measured in octa averaged over the last 30 years [meteo_octa] A vector point data set which represents the daily cloud cover measured in octa.
Digital Surface Model [dsm] A raster data set representing a Digital Surface Model of the city. The resolution should be as small as possible (~1m) to have roof objects detected. The data set should not have wholes (NULL values). They should be filled by preprocessing. Units should be meters.
Linke turbidity [meteo_linke] A vector point data set which represents the monthly values of Linke's turbidity factor.
Ratio of diffuse to global irradiation [meteo_ratio] A vector point data set which represents the monthly values of Ratio of diffuse to global irradiation factor.
Model Outputs
Parameter NameIdentifierDescription
Solar irradiation [solar_irradiation] A raster data set which contains the solar irradiation based on the input data sets. Unit is kWh per year and square meter. Data can be used to feed the PV potential module.
Suitable roof patches [roof_patches] A vector data set which delinates the suitable roof patches for a PV or green roof installation. Additionally, univariate statistics about slope of the patch are given. This is further used in the green roofs module. Column "type" characterises the type of the roof: 1 = flat roof, 2 = pitched roof. Furthermore a smmothing algorithm is applied to reduce the number of vertices of the patches. This will speed up visualisation in the slider tool. Data can be used to feed the PV potential or green roof module.

Urban Heat Island characterisation

Identifier: uhi
This process calculates Urban Heat Island effects in an urban area. It is using a Landsat5 TM scene to charaterize the Urban Heat Island phenomenon by the retrival of land surface temperature of the thermal band of Landsat 5TM. The output is a classified image which shows the level of UHI.
Hosted by: Smart City and Region Energy PyWPS - Luxembourg Institute of Science and Technology
Model Inputs
Parameter NameIdentifierDescription
Landsat 5 TM image stack [landsat5] A scene of Landsat5 TM which is containing all seven bands. Possible data sets can be expolored by the USGS web tool (http://earthexplorer.usgs.gov).
URL to meta data file of Landsat image [landsat5_meta] Text file which comes with the scene of Landsat TM5. Data should be residing at a web accesible location e.g. Dropbox or public web server URL.
Model Outputs
Parameter NameIdentifierDescription
Urban heat island [uhi] A raster image which shows classes of UHI effects. 0 - No, 1 - weak, 2 - middle, 3 - strong, 4 - stronger, 5 - strongest effects of the UHI phenomenon.

Smart City and Region Energy PyWPS - Luxembourg Institute of Science and Technology

This is the WPS Server of Smart City and Region Energy (MUSIC). It is powered by PyWPS, see http://pywps.wald.intevation.org and http://www.opengeospatial.org/standards/wps.
Provider:
Luxembourg Institute of Science and Technology (LIST) - Environmental Research and Innovation Department - eScience Research Unit (http://www.list.lu/erin)
Contact:
Server URL:
http://wps.iguess.tudor.lu/cgi-bin/pywps.cgi
GetCapabilities URL:

Smart City and Region Energy PyWPS - Luxembourg Institute of Science and Technology

This is the WPS Server of Smart City and Region Energy (MUSIC). It is powered by PyWPS, see http://pywps.wald.intevation.org and http://www.opengeospatial.org/standards/wps.
Provider:
Luxembourg Institute of Science and Technology (LIST) - Environmental Research and Innovation Department - eScience Research Unit (http://www.list.lu/erin)
Contact:
Server URL:
http://wps.iguess.tudor.lu/cgi-bin/pywps.cgi
GetCapabilities URL:

Smart City and Region Energy PyWPS - Luxembourg Institute of Science and Technology

"It is powered by PyWPS, see http://pywps.wald.intevation.org and http://www.opengeospatial.org/standards/wps"
Provider:
LIST
Contact:
Server URL:
http://wps.iguess.tudor.lu/cgi-bin/pywps-g7.cgi
GetCapabilities URL:

Smart City and Region Energy PyWPS - Luxembourg Institute of Science and Technology

This is the WPS Server of Smart City and Region Energy (MUSIC). It is powered by PyWPS, see http://pywps.wald.intevation.org and http://www.opengeospatial.org/standards/wps.
Provider:
Luxembourg Institute of Science and Technology (LIST) - Environmental Research and Innovation Department - eScience Research Unit (http://www.list.lu/erin)
Contact:
Server URL:
http://wps.iguess.tudor.lu/cgi-bin/pywps.cgi
GetCapabilities URL:

52°North WPS 4.0.0-beta.4-SNAPSHOT

Service based on the 52°North implementation of WPS 1.0.0 and 2.0.0
Provider:
52°North GmbH
Contact:
Server URL:
http://geoprocessing.demo.52north.org:8080/wps/WebProcessingService
GetCapabilities URL:

Smart City and Region Energy PyWPS - Luxembourg Institute of Science and Technology

This is the WPS Server of Smart City and Region Energy (MUSIC). It is powered by PyWPS, see http://pywps.wald.intevation.org and http://www.opengeospatial.org/standards/wps.
Provider:
Luxembourg Institute of Science and Technology (LIST) - Environmental Research and Innovation Department - eScience Research Unit (http://www.list.lu/erin)
Contact:
Server URL:
http://wps.iguess.tudor.lu/cgi-bin/pywps.cgi
GetCapabilities URL:

Smart City and Region Energy PyWPS - Luxembourg Institute of Science and Technology

This is the WPS Server of Smart City and Region Energy (MUSIC). It is powered by PyWPS, see http://pywps.wald.intevation.org and http://www.opengeospatial.org/standards/wps.
Provider:
Luxembourg Institute of Science and Technology (LIST) - Environmental Research and Innovation Department - eScience Research Unit (http://www.list.lu/erin)
Contact:
Server URL:
http://wps.iguess.tudor.lu/cgi-bin/pywps.cgi
GetCapabilities URL:

Smart City and Region Energy PyWPS - Luxembourg Institute of Science and Technology

"It is powered by PyWPS, see http://pywps.wald.intevation.org and http://www.opengeospatial.org/standards/wps"
Provider:
LIST
Contact:
Server URL:
http://wps.iguess.tudor.lu/cgi-bin/pywps-g7.cgi
GetCapabilities URL: