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Application of a semantic segmentation convolutional neural network for accurate automatic detection and mapping of solar photovoltaic arrays in aerial imagery
We consider the problem of automatically detecting small-scale solar pho...
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GridTracer: Automatic Mapping of Power Grids using Deep Learning and Overhead Imagery
Energy system information valuable for electricity access planning such ...
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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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A proposed method to extract maximum possible power in the shortest time on solar PV arrays under partial shadings using metaheuristic algorithms
The increasing use of fossil fuels to produce energy is leading to envir...
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Virtual Prototyping and Distributed Control for Solar Array with Distributed Multilevel Inverter
In this paper, we present the virtual prototyping of a solar array with ...
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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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Map Generation from Large Scale Incomplete and Inaccurate Data Labels
Accurately and globally mapping human infrastructure is an important and...
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Mapping solar array location, size, and capacity using deep learning and overhead imagery
The effective integration of distributed solar photovoltaic (PV) arrays into existing power grids will require access to high quality data; the location, power capacity, and energy generation of individual solar PV installations. Unfortunately, existing methods for obtaining this data are limited in their spatial resolution and completeness. We propose a general framework for accurately and cheaply mapping individual PV arrays, and their capacities, over large geographic areas. At the core of this approach is a deep learning algorithm called SolarMapper - which we make publicly available - that can automatically map PV arrays in high resolution overhead imagery. We estimate the performance of SolarMapper on a large dataset of overhead imagery across three US cities in California. We also describe a procedure for deploying SolarMapper to new geographic regions, so that it can be utilized by others. We demonstrate the effectiveness of the proposed deployment procedure by using it to map solar arrays across the entire US state of Connecticut (CT). Using these results, we demonstrate that we achieve highly accurate estimates of total installed PV capacity within each of CT's 168 municipal regions.
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