Table 1 The overview of GIS-based suitability assessment of solar energy in national and global scale

Lisbon, Portugal

A digital surface model (DSM) built from Light Detection and Ranging (LiDAR) data and a solar

Direct and diffuse solar radiation was obtained from the ground, roof, and facades to calculate urban solar potential

Facades increase the solar potential by 10 to 15% although the average annual irradiance on a facade is one-third of the rooftops

[6]

European Union

Multivariate sampling, correlates the roof area and statistical data(e.g., population density, number of floors) European Settlement Map

CORINE Land Cover and the European Urban Atlas data sets were processed to adjust information on EU built-up areas later Photovoltaic GIS was used for the PV energy yield calculation

EU rooftops could potentially fulfill 24.4% (680TWh) of electricity needs annually by solar PV energy

[2]

Slovenia

LiDAR data and mathematical equations

Estimating the rooftop PV potential in terms of its physical, geographic, technical, and economic potential

The annual physical, geographic, technical and economic potentials were 1273.7 MWh, 1253.8 MWh, 14.2 MWh, and 279.1 Wh, respectively

[50]

Germany

Open Geospatial Consortium(OGC) Standard CityGML, LiDAR

Technical and economic potential (considering roof area and insolation thresholds) are investigated to determine the fraction of the electricity demand of the municipalities and the region

The available roof space (technical The available roof space (technical potential) can cover 77% of the region’s electricity consumption and considering economic potential high irradiance roofs can cover 56% of it

[69]

United States

LiDAR data and statistical models

Combines lidar data, GIS tools with a validated analytical method for rooftop PV suitability employing

The roof area of 81.3 km² could host 1118 GW PV capacity, generating 1432 TWh of electricity per year

[16]

Victoria, Australia

LiDAR Data processed in MATLAB

Pixel-based approach to the estimation of solar energy potentials over pitched roofs in using a specific time interval by image visualization and processing in MATLAB

Coloured 3D map, reveal the roof’s radiation distribution, due to objects the roofs and identifying roof areas with high solar potential for installing solar collectors

[41]

Philadelphia in PA, U.S. A

LiDAR data, building footprints and ArcGIS

Rooftop area analysis was based on slope and aspect, using LIDAR data, ArcGIS tools, and building footprint data

33.7% of building footprint data and 48.6% rooftop areas are suitable for PV systems

[4]

Switzerland

Combination of support vector machines (SVMs) and geographic information systems (GIS)

A combination of machine learning and GIS was used to calculate physical geographic and technical rooftop solar potential

The annual PV potential of Switzerland is 17.86 TW h which corresponds to 28% of its electricity consumption in 2015

[31, 32]

Mumbai, India

The satellite image from Google Earth with a spatial resolution of 0.5 m

Land use data and GIS-based analysis of satellite images to estimate Building Footprint Area (BFA) Ratio

With median efficiency panels, Mumbai city has a potential of 2190 MW which can fulfill 12.8–20% of average daily and 31–60% of peak morning demand

[80]

Odisha, India

Artificial neural network (ANN) and Generic algorithm (GA)

Off-grid systems such as photovoltaic lighting systems and water pumps have been designed and implemented

Mini solar street light, a 20 Wp polycrystalline solar panel has been used to charge a 12 V 10 Ah Li-ion battery in 6–7 sunny hours and can run a 12 V 9 W LED light up to 10 h per a day

[19, 20]