{"id":627722,"date":"2023-10-31T19:59:34","date_gmt":"2023-11-01T02:59:34","guid":{"rendered":"https:\/\/www.esri.com\/about\/newsroom\/?post_type=arcnews&#038;p=627722"},"modified":"2023-10-25T07:43:02","modified_gmt":"2023-10-25T14:43:02","slug":"deep-learning-model-unlocks-potential-of-solar-energy-development","status":"publish","type":"arcnews","link":"https:\/\/www.esri.com\/about\/newsroom\/arcnews\/deep-learning-model-unlocks-potential-of-solar-energy-development","title":{"rendered":"Deep Learning Model Unlocks Potential of Solar Energy Development"},"author":5752,"featured_media":0,"menu_order":0,"template":"","format":"standard","meta":{"_acf_changed":false,"sync_status":"","episode_type":"","audio_file":"","podmotor_file_id":"","podmotor_episode_id":"","castos_file_data":"","cover_image":"","cover_image_id":"","duration":"","filesize":"","filesize_raw":"","date_recorded":"","explicit":"","block":"","itunes_episode_number":"","itunes_title":"","itunes_season_number":"","itunes_episode_type":"","_links_to":"","_links_to_target":""},"categories":[275822,369852,381322],"tags":[23402,1701,10332,238001,237991],"arcnews_issues":[486972],"class_list":["post-627722","arcnews","type-arcnews","status-publish","format-standard","hentry","category-arcgis-living-atlas-of-the-world","category-artificial-intelligence--ai","category-sustainability","tag-automation","tag-deep-learning","tag-imagery","tag-site-selection","tag-solar-energy","arcnews_issues-fall-2023","arcnews_sections-your-work"],"acf":{"short_description":"A deep learning model in ArcGIS Living Atlas of the World automatically detects parking lots that are ideal for solar panel installation.","pdf":{"host_remotely":false,"file":"","file_url":""},"flexible_content":[{"acf_fc_layout":"content","content":"Solar power presents an immense opportunity for generating sustainable and green energy. Realizing its full potential requires identifying suitable locations for solar panel installations.\r\n\r\n<a href=\"https:\/\/www.pivotenergy.net\/\">Pivot Energy<\/a>, a national renewable energy provider headquartered in Colorado, needed assistance locating parking lots across various areas of interest that are appropriate for potential solar panel implementation. Using advanced GIS technology, a team from Esri partner Platte River Analytics helped Pivot Energy do this accurately and efficiently.\r\n\r\nBy leveraging a deep learning model from Esri, the team at Platte River Analytics extracted parking lot surfaces from high-resolution imagery. The team then used geoprocessing tools in ArcGIS Pro to conduct more precise measurements and calculations of potential sites.\r\n\r\nThe results of this analysis provided Pivot Energy with invaluable information, empowering staff to make data-driven decisions and plan out solar power adoption efforts more effectively."},{"acf_fc_layout":"image","image":627752,"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<h2>The Benefits of Parking-Lot-Based Solar Development<\/h2>\r\nParking lots offer significant yet underutilized space for solar power development. They possess key characteristics that make them ideal for generating solar energy, such as large surface areas, unobstructed exposure to sunlight, and proximity to electrical infrastructure connections. In addition, paved parking lots typically have very low slopes, are designed to drain, don\u2019t compete with other land uses, and aren\u2019t in full use all the time.\r\n\r\nSolar-powered parking lots can provide numerous environmental and economic advantages to owners and communities as well. They can be quickly equipped with electric vehicle (EV) charging stations. This not only enables EVs to be powered directly by solar-generated energy, promoting clean transportation, but it also reduces electricity costs for owners and operators and opens avenues for potential revenue generation through energy sales. In addition, offering EV charging to customers attracts car owners who will spend money in the area while charging their vehicles.\r\n\r\nAll these are reasons why Pivot Energy saw the need to scour parking lots across the East Coast and, eventually, the nation to see which ones are ideal for solar energy development."},{"acf_fc_layout":"content","content":"<h2>A Quick, Automated Way to Detect Suitable Parking Lots<\/h2>\r\nTo automate the detection of parking lots in Pivot Energy\u2019s areas of interest, the team at Platte River Analytics relied on Esri\u2019s Parking Lots Classification \u2013 USA deep learning model, available in ArcGIS Living Atlas of the World.\r\n\r\nDeveloped by the Esri analytics team, this prebuilt model is trained to identify parking lots within sourced areal imagery. Like the more than 65 other deep learning models that the Esri analytics team has developed to detect objects ranging from Arctic seals to power lines, the Parking Lots Classification \u2013 USA model automatically extracts the assets from imagery without users having to invest time or money in training data or personnel.\r\n\r\nFor this project, the team at Platte River Analytics needed to use high-quality, submeter data that allowed the model to identify and analyze land features as detailed as parking lots. The team acquired one-meter resolution National Agriculture Imagery Program (NAIP) imagery from the United States Geological Survey\u2019s EarthExplorer web app. The imagery in this app has been acquired by the US Department of Agriculture during agricultural growing seasons from 2003 to the present.\r\n\r\nAfter downloading the NAIP imagery, the Platte River Analytics team seamlessly integrated the deep learning model into its ArcGIS Pro workflow. The team processed the imagery with the model, which automatically identified parking lots across dozens of Pivot Energy\u2019s areas of interest.\r\n\r\nThe model was easy to use. The initial area of interest that the team looked into was the size of a large US city, and it took less than 12 hours to both download the imagery and process it in ArcGIS Pro.\r\n\r\nTo further analyze the identified parking lots, the team at Platte River Analytics used the Raster to Polygon geoprocessing tool in ArcGIS Pro to convert the raster outputs into polygons. This enabled the team to get more precise measurements and calculate the size of each lot, providing valuable information to Pivot Energy so staff could begin conducting feasibility assessments and get started with project planning.\r\n\r\nFrom there, the GIS team at Pivot Energy was able to study regulatory factors\u2014such as floodplains, tree cover, wetlands, and wildlife migration routes\u2014around parking lots that were initially deemed acceptable for solar development."},{"acf_fc_layout":"content","content":"<h2>Saving Dozens of Hours of Manual Work per Week<\/h2>\r\nTaking a machine learning-based approach to finding suitable parking lots for solar panel installation enabled staff at Pivot Energy to make informed decisions quickly regarding which areas and specific parking lots could work for this endeavor. By using advanced GIS to assess parking lot locations and sizes, the developer can optimize project planning, ensure maximum energy generation capacity, and speed up the installation of solar infrastructure.\r\n\r\nAccording to Rachel Mead, GIS manager at Pivot Energy, the process that the team at Platte River Analytics used to extract parking lots from imagery saved her own team more than 20 hours per week of manually searching aerial imagery for\u2014and digitizing parking lots throughout\u2014the company\u2019s areas of interest, which stretch across the United States.\r\n\r\n\u201cIt has been a huge time-saver having access to the deep learning models provided by Esri,\u201d she said. \u201cBy automating this\u2026we can save dozens of hours per week and realign that time to other projects.\u201d"}],"references":null},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.9 (Yoast SEO v25.9) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Deep Learning Model Unlocks Potential of Solar Energy Development | Fall 2023 | ArcNews<\/title>\n<meta name=\"description\" content=\"A deep learning model in ArcGIS Living Atlas of the World automatically detects parking lots that are ideal for solar panel installation.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.esri.com\/about\/newsroom\/arcnews\/deep-learning-model-unlocks-potential-of-solar-energy-development\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Deep Learning Model Unlocks Potential of Solar Energy Development\" \/>\n<meta property=\"og:description\" content=\"A deep learning model in ArcGIS Living Atlas of the World automatically detects parking lots that are ideal for solar panel installation.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.esri.com\/about\/newsroom\/arcnews\/deep-learning-model-unlocks-potential-of-solar-energy-development\" \/>\n<meta property=\"og:site_name\" content=\"Esri\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/esrigis\/\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.esri.com\/about\/newsroom\/app\/uploads\/2023\/10\/arcnews-banner-deeplearningmodel-wide.jpg\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:description\" content=\"A deep learning model in ArcGIS Living Atlas of the World automatically detects parking lots that are ideal for solar panel installation.\" \/>\n<meta name=\"twitter:image\" content=\"https:\/\/www.esri.com\/about\/newsroom\/app\/uploads\/2023\/10\/arcnews-banner-deeplearningmodel-wide.jpg\" \/>\n<meta name=\"twitter:site\" content=\"@Esri\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\n\t    \"@context\": \"https:\/\/schema.org\",\n\t    \"@graph\": [\n\t        {\n\t            \"@type\": \"WebPage\",\n\t            \"@id\": \"https:\/\/www.esri.com\/about\/newsroom\/arcnews\/deep-learning-model-unlocks-potential-of-solar-energy-development\",\n\t            \"url\": \"https:\/\/www.esri.com\/about\/newsroom\/arcnews\/deep-learning-model-unlocks-potential-of-solar-energy-development\",\n\t            \"name\": \"Deep Learning Model Unlocks Potential of Solar Energy Development | Fall 2023 | ArcNews\",\n\t            \"isPartOf\": {\n\t                \"@id\": \"https:\/\/www.esri.com\/about\/newsroom\/#website\"\n\t            },\n\t            \"datePublished\": \"2023-11-01T02:59:34+00:00\",\n\t            \"description\": \"A deep learning model in ArcGIS Living Atlas of the World automatically detects parking lots that are ideal for solar panel installation.\",\n\t            \"breadcrumb\": {\n\t                \"@id\": \"https:\/\/www.esri.com\/about\/newsroom\/arcnews\/deep-learning-model-unlocks-potential-of-solar-energy-development#breadcrumb\"\n\t            },\n\t            \"inLanguage\": \"en-US\",\n\t            \"potentialAction\": [\n\t                {\n\t                    \"@type\": \"ReadAction\",\n\t                    \"target\": [\n\t                        \"https:\/\/www.esri.com\/about\/newsroom\/arcnews\/deep-learning-model-unlocks-potential-of-solar-energy-development\"\n\t                    ]\n\t                }\n\t            ]\n\t        },\n\t        {\n\t            \"@type\": \"BreadcrumbList\",\n\t            \"@id\": \"https:\/\/www.esri.com\/about\/newsroom\/arcnews\/deep-learning-model-unlocks-potential-of-solar-energy-development#breadcrumb\",\n\t            \"itemListElement\": [\n\t                {\n\t                    \"@type\": \"ListItem\",\n\t                    \"position\": 1,\n\t                    \"name\": \"Home\",\n\t                    \"item\": \"https:\/\/www.esri.com\/about\/newsroom\"\n\t                },\n\t                {\n\t                    \"@type\": \"ListItem\",\n\t                    \"position\": 2,\n\t                    \"name\": \"ArcNews Articles\",\n\t                    \"item\": \"https:\/\/www.esri.com\/about\/newsroom\/arcnews\"\n\t                },\n\t                {\n\t                    \"@type\": \"ListItem\",\n\t                    \"position\": 3,\n\t                    \"name\": \"Deep Learning Model Unlocks Potential of Solar Energy Development\"\n\t                }\n\t            ]\n\t        },\n\t        {\n\t            \"@type\": \"WebSite\",\n\t            \"@id\": \"https:\/\/www.esri.com\/about\/newsroom\/#website\",\n\t            \"url\": \"https:\/\/www.esri.com\/about\/newsroom\/\",\n\t            \"name\": \"Esri\",\n\t            \"description\": \"Esri Newsroom\",\n\t            \"potentialAction\": [\n\t                {\n\t                    \"@type\": \"SearchAction\",\n\t                    \"target\": {\n\t                        \"@type\": \"EntryPoint\",\n\t                        \"urlTemplate\": \"https:\/\/www.esri.com\/about\/newsroom\/?s={search_term_string}\"\n\t                    },\n\t                    \"query-input\": {\n\t                        \"@type\": \"PropertyValueSpecification\",\n\t                        \"valueRequired\": true,\n\t                        \"valueName\": \"search_term_string\"\n\t                    }\n\t                }\n\t            ],\n\t            \"inLanguage\": \"en-US\"\n\t        },\n\t        {\n\t            \"@type\": \"Person\",\n\t            \"@id\": \"https:\/\/www.esri.com\/about\/newsroom\/#\/schema\/person\/41c803b2ea8734c36f9c4e9586d1449d\",\n\t            \"name\": \"Amy Ambard\",\n\t            \"image\": {\n\t                \"@type\": \"ImageObject\",\n\t                \"inLanguage\": \"en-US\",\n\t                \"@id\": \"https:\/\/www.esri.com\/about\/newsroom\/#\/schema\/person\/image\/\",\n\t                \"url\": \"https:\/\/secure.gravatar.com\/avatar\/f356480172f8ad0bc8d72b855e84171c52f1944c7c7779f3e425d73bf3efa3c7?s=96&d=blank&r=g\",\n\t                \"contentUrl\": \"https:\/\/secure.gravatar.com\/avatar\/f356480172f8ad0bc8d72b855e84171c52f1944c7c7779f3e425d73bf3efa3c7?s=96&d=blank&r=g\",\n\t                \"caption\": \"Amy Ambard\"\n\t            },\n\t            \"url\": \"\"\n\t        }\n\t    ]\n\t}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"Deep Learning Model Unlocks Potential of Solar Energy Development | Fall 2023 | ArcNews","description":"A deep learning model in ArcGIS Living Atlas of the World automatically detects parking lots that are ideal for solar panel installation.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.esri.com\/about\/newsroom\/arcnews\/deep-learning-model-unlocks-potential-of-solar-energy-development","og_locale":"en_US","og_type":"article","og_title":"Deep Learning Model Unlocks Potential of Solar Energy Development","og_description":"A deep learning model in ArcGIS Living Atlas of the World automatically detects parking lots that are ideal for solar panel installation.","og_url":"https:\/\/www.esri.com\/about\/newsroom\/arcnews\/deep-learning-model-unlocks-potential-of-solar-energy-development","og_site_name":"Esri","article_publisher":"https:\/\/www.facebook.com\/esrigis\/","og_image":[{"url":"https:\/\/www.esri.com\/about\/newsroom\/app\/uploads\/2023\/10\/arcnews-banner-deeplearningmodel-wide.jpg","type":"","width":"","height":""}],"twitter_card":"summary_large_image","twitter_description":"A deep learning model in ArcGIS Living Atlas of the World automatically detects parking lots that are ideal for solar panel installation.","twitter_image":"https:\/\/www.esri.com\/about\/newsroom\/app\/uploads\/2023\/10\/arcnews-banner-deeplearningmodel-wide.jpg","twitter_site":"@Esri","schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/www.esri.com\/about\/newsroom\/arcnews\/deep-learning-model-unlocks-potential-of-solar-energy-development","url":"https:\/\/www.esri.com\/about\/newsroom\/arcnews\/deep-learning-model-unlocks-potential-of-solar-energy-development","name":"Deep Learning Model Unlocks Potential of Solar Energy Development | Fall 2023 | ArcNews","isPartOf":{"@id":"https:\/\/www.esri.com\/about\/newsroom\/#website"},"datePublished":"2023-11-01T02:59:34+00:00","description":"A deep learning model in ArcGIS Living Atlas of the World automatically detects parking lots that are ideal for solar panel installation.","breadcrumb":{"@id":"https:\/\/www.esri.com\/about\/newsroom\/arcnews\/deep-learning-model-unlocks-potential-of-solar-energy-development#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.esri.com\/about\/newsroom\/arcnews\/deep-learning-model-unlocks-potential-of-solar-energy-development"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/www.esri.com\/about\/newsroom\/arcnews\/deep-learning-model-unlocks-potential-of-solar-energy-development#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.esri.com\/about\/newsroom"},{"@type":"ListItem","position":2,"name":"ArcNews Articles","item":"https:\/\/www.esri.com\/about\/newsroom\/arcnews"},{"@type":"ListItem","position":3,"name":"Deep Learning Model Unlocks Potential of Solar Energy Development"}]},{"@type":"WebSite","@id":"https:\/\/www.esri.com\/about\/newsroom\/#website","url":"https:\/\/www.esri.com\/about\/newsroom\/","name":"Esri","description":"Esri Newsroom","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.esri.com\/about\/newsroom\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Person","@id":"https:\/\/www.esri.com\/about\/newsroom\/#\/schema\/person\/41c803b2ea8734c36f9c4e9586d1449d","name":"Amy Ambard","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.esri.com\/about\/newsroom\/#\/schema\/person\/image\/","url":"https:\/\/secure.gravatar.com\/avatar\/f356480172f8ad0bc8d72b855e84171c52f1944c7c7779f3e425d73bf3efa3c7?s=96&d=blank&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/f356480172f8ad0bc8d72b855e84171c52f1944c7c7779f3e425d73bf3efa3c7?s=96&d=blank&r=g","caption":"Amy Ambard"},"url":""}]}},"sort_order":"10","_links":{"self":[{"href":"https:\/\/www.esri.com\/about\/newsroom\/wp-json\/wp\/v2\/arcnews\/627722","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.esri.com\/about\/newsroom\/wp-json\/wp\/v2\/arcnews"}],"about":[{"href":"https:\/\/www.esri.com\/about\/newsroom\/wp-json\/wp\/v2\/types\/arcnews"}],"author":[{"embeddable":true,"href":"https:\/\/www.esri.com\/about\/newsroom\/wp-json\/wp\/v2\/users\/5752"}],"version-history":[{"count":0,"href":"https:\/\/www.esri.com\/about\/newsroom\/wp-json\/wp\/v2\/arcnews\/627722\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.esri.com\/about\/newsroom\/wp-json\/wp\/v2\/media?parent=627722"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.esri.com\/about\/newsroom\/wp-json\/wp\/v2\/categories?post=627722"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.esri.com\/about\/newsroom\/wp-json\/wp\/v2\/tags?post=627722"},{"taxonomy":"arcnews_issues","embeddable":true,"href":"https:\/\/www.esri.com\/about\/newsroom\/wp-json\/wp\/v2\/arcnews_issues?post=627722"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}