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Solar Potential Analysis of Rooftops Using Satellite Imagery
Solar energy is one of the most important sources of renewable energy an...
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SolarNet: A Deep Learning Framework to Map Solar Power Plants In China From Satellite Imagery
Renewable energy such as solar power is critical to fight the ever more ...
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Observational daily and regional photovoltaic solar energy production for the Netherlands
This paper presents a new method for deriving the energy yield generated...
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Origami Inspired Solar Panel Design
The goal of this paper was to take a flat solar panel and make cuts on t...
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GIS-AHP Multi-Decision-Criteria-Analysis for the Optimal Location of Solar Energy Plants at Indonesia
A reliable tool for site-suitability assessment of solar power plants ca...
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Construcción de un Mapa de Vulnerabilidad Sanitaria en Argentina a partir de datos públicos
This document is intended to present in detail the processing criteria a...
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What do adoption patterns of solar panels observed so far tell about governments' incentive? insight from diffusion models
The paper uses diffusion models to understand the main determinants of d...
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Monitoring spatial sustainable development: Semi-automated analysis of satellite and aerial images for energy transition and sustainability indicators
Solar panels are installed by a large and growing number of households due to the convenience of having cheap and renewable energy to power house appliances. In contrast to other energy sources solar installations are distributed very decentralized and spread over hundred-thousands of locations. On a global level more than 25 effect of the quick energy transition from a carbon based economy to a green economy is though still very difficult to quantify. As a matter of fact the quick adoption of solar panels by households is difficult to track, with local registries that miss a large number of the newly built solar panels. This makes the task of assessing the impact of renewable energies an impossible task. Although models of the output of a region exist, they are often black box estimations. This project's aim is twofold: First automate the process to extract the location of solar panels from aerial or satellite images and second, produce a map of solar panels along with statistics on the number of solar panels. Further, this project takes place in a wider framework which investigates how official statistics can benefit from new digital data sources. At project completion, a method for detecting solar panels from aerial images via machine learning will be developed and the methodology initially developed for BE, DE and NL will be standardized for application to other EU countries. In practice, machine learning techniques are used to identify solar panels in satellite and aerial images for the province of Limburg (NL), Flanders (BE) and North Rhine-Westphalia (DE).
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