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<!DOCTYPE html>
<html>
<head>
<meta charset='utf-8' />
<title>SolarMapper - mapping solar arrays in Connecticut, USA using deep learning and overhead imagery</title>
<meta name='viewport' content='initial-scale=1,maximum-scale=1,user-scalable=no' />
<script src='https://api.mapbox.com/mapbox-gl-js/v1.4.1/mapbox-gl.js'></script>
<link href='https://api.mapbox.com/mapbox-gl-js/v1.4.1/mapbox-gl.css' rel='stylesheet' />
<script src='https://api.tiles.mapbox.com/mapbox-gl-js/v1.7.0/mapbox-gl.js'></script>
<link href='https://api.tiles.mapbox.com/mapbox-gl-js/v1.7.0/mapbox-gl.css' rel='stylesheet' />
<link href='./style.css' rel='stylesheet' />
</head>
<body>
<div id='map'></div>
<div id='add-on-content'>
<div class='banner'>
<div style='width: 150%; cursor: pointer; cursor: hand;' id='banner-left' onclick='openModal()'>
<h2 style='margin: 5px 0px;'>SolarMapper</h2>
<p style='margin: 5px 0px;' id='subtitle'>Mapping solar arrays in Connecticut, USA using deep learning
and overhead imagery</p>
</div>
<div id='banner-right'>
<div style='float:right'>
<h4 style='text-align:right; margin: 0px 10px; cursor: pointer; cursor: hand;' onclick='openModal()'>
ABOUT
</h4>
</div>
<div style='float:right'>
<h4 style='text-align:right; margin: 0px 10px'>
<a target='_blank' href='https://github.com/energydatalab/solarMapper'>DATA</a>
</h4>
</div>
</div>
</div>
<div>
<div class='jump-to' id='jump-to-coords'>
<div>
<h4 class='jump-to-text'>Jump to LngLatLike coordinates:</h4>
</div>
<div>
<input type='text' id='dest-coords' name='dest-coords' placeholder='Format: -72.985, 41.258'>
</div>
<div>
<button class='jump-buttons' id='submit-coords'>Go!</button>
</div>
</div>
<div class='jump-to' id='jump-to-location'>
<div>
<h4 class='jump-to-text'>Jump to location:</h4>
</div>
<div>
<button class='jump-buttons' id='groton'>Groton (residential)</button>
<button class='jump-buttons' id='somers'>Somers (utility)</button>
<button class='jump-buttons' id='west-haven'>West Haven (commercial)</button>
<button class='jump-buttons' id='full-ct'>Full CT view</button>
</div>
</div>
</div>
<div id='modal'>
<div class='modal-content'>
<h3>What you get is not always what you see—pitfalls in solar array assessment using overhead imagery</h3>
<h4>Wei Hu, Kyle Bradbury, Jordan M. Malof, Boning Li, Bohao Huang, Artem Streltsov, K. Sydny Fujita, and Ben Hoen</h4>
<p>
Effective integration planning for small, distributed solar photovoltaic (PV) arrays into electric power grids requires access to high quality data: the location and power capacity of individual solar PV arrays. Unfortunately, national databases of small-scale solar PV do not exist; those that do are limited in their spatial resolution, typically aggregated up to state or national levels. While several promising approaches for solar PV detection have been published, strategies for evaluating the performance of these models are often highly heterogeneous from study to study. The resulting comparison of these methods for practical applications for energy assessments becomes challenging and may imply that the reported performance evaluations overly optimistic. The heterogeneity comes in many forms, each of which we explore in this work: the degree of diversity of the locations and sensors (e.g. different satellites, aerial photography ) from which the training and validation data originate, the validation of ground truth (manual annotation of imagery vs known solar PV locations), the level of spatial aggregation (e.g. array-level vs regional estimates), and inconsistencies in the training and validation datasets (e.g. different datasets are used for each study and those data are not always made accessible). For each, we discuss emerging practices from the literature to address them or suggest directions of future research. As part of our investigation, we evaluate solar PV identification performance in two large regions: the entire state of Connecticut and the city of San Diego, CA. In Connecticut, we also use 33,114 known parcel-level solar PV installations from Berkeley Lab’s Tracking the Sun dataset to evaluate parcel-level performance and evaluate capacity estimates using 169 municipalities. We also make our code (which we call SolarMapper), pre-trained models, training data, and predictions publicly available and provide a web portal for interactively inspecting each prediction that was made. Our findings suggest that traditional performance evaluation of the automated identification of solar PV from satellite imagery may be optimistic due to common limitations in the validation process. The takeaways from this work are intended to inform and catalyze the large-scale practical application of automated solar PV assessment techniques by energy researchers and professionals.
</p>
<p>
<a target='_blank' href='https://arxiv.org/abs/1902.10895'>[Full text on arXiv]</a>
</p>
<p>Website created by Wayne Hu. Built with Mapbox.</p>
</div>
</div>
</div>
<script src='./main.js'></script>
</body>
</html>