{"id":335761,"date":"2020-05-12T12:00:23","date_gmt":"2020-05-12T19:00:23","guid":{"rendered":"https:\/\/www.esri.com\/about\/newsroom\/?post_type=blog&#038;p=335761"},"modified":"2025-04-01T13:11:12","modified_gmt":"2025-04-01T20:11:12","slug":"deep-learning-helps-kuwait-automate-map-updates","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/about\/newsroom\/blog\/deep-learning-helps-kuwait-automate-map-updates","title":{"rendered":"Deep Learning Helps Kuwait Automate Map Updates to Better Serve Citizens"},"author":5122,"featured_media":0,"parent":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":[23422],"tags":[482772,23402,1701,287732,271],"industry":[],"esri-blog-category":[478532],"esri_blog_department":[478172],"class_list":["post-335761","blog","type-blog","status-publish","format-standard","hentry","category-machine-learning-capability","tag-ai-ml","tag-automation","tag-deep-learning","tag-kuwait","tag-mapping","esri-blog-category-analytics","esri_blog_department-mapping"],"acf":{"video_source":"","video_start":"","video_stop":"","short_description":"Deep learning helps keep the Kuwait base map up-to-date and complete to provide accurate information in a rapidly developing country.","pdf":{"host_remotely":false,"file":"","file_url":""},"flexible_content":[{"acf_fc_layout":"sidebar","layout":"standard","image_reference":null,"image_reference_figure":"","spotlight_image":null,"section_title":"","spotlight_name":"","position":"Right","content":"Creating a deep learning model and training dataset with GIS allows PACI to automate much of its mapping, realizing immediate benefits in time and cost savings as well as improving the data accuracy.\r\n\r\nKey Takeaways\r\n<ul>\r\n \t<li>GeoAI Machine learning model allows PACI to automatically update Kuwait\u2019s base maps with the new streets and buildings.<\/li>\r\n \t<li>Time spent developing and training a deep learning model returned ever more accurate results.<\/li>\r\n \t<li>Time savings and accuracy improvements directly benefit the citizens and residents of Kuwait in getting better data and directions for less cost.<\/li>\r\n<\/ul>","snippet":""},{"acf_fc_layout":"content","content":"Kuwait Finder, a mobile location app built by the Kuwaiti government, was a smashing success when it was first released in 2013. Among its many achievements, it was a triumph of data-gathering and data-processing, offering authoritative turn-by-turn directions for Kuwait, a locale whose system of address numbers and street names can be confusing.\r\n\r\nBy the end of last year, Kuwait Finder had amassed 750,000 users. Within the country, that level of saturation makes Kuwait Finder a more popular wayfinding tool than Google Maps.\r\n\r\nThis popularity presented its own challenges including how to keep the application and underlying data up to date. Per capita, Kuwait is the fourth-wealthiest country in the world, and with the Kuwait National Development Plan goal to increase infrastructure expenditures by 11 percent, change is already well underway. Construction projects\u2014including the world\u2019s <a href=\"https:\/\/en.wikipedia.org\/wiki\/Sheikh_Jaber_Al-Ahmad_Al-Sabah_Causeway\">longest causeway<\/a>, a new airport passenger terminal, and a new 500,000-person residential area called <a href=\"https:\/\/en.wikipedia.org\/wiki\/Madinat_al-Hareer\">Silk City<\/a>\u2014amounts to $500 billion in active investments including major changes to transportation networks and other infrastructure.\r\n\r\nTo stay trusted, Kuwait Finder must capture and reflect this dynamism, with an up-to-the-moment authoritative geospatial rendition of the entire country.\r\n<h3><strong>Turning to Automation<\/strong><\/h3>\r\nTo create Kuwait Finder initially back in 2012, a five-person staff at Kuwait\u2019s Public Authority for Civil Information (PACI) tapped into the country\u2019s long-term investment in geographic information system (GIS) technology. The GIS team pulled together data from various ministries, as well as PACI\u2019s internal paper maps and AutoCAD files, to capture the current basemap. With the objective of updating the construction of new buildings and changes to the city\u2019s streets, they studied satellite imagery to extract details for the map. But by the time the crew found the changes and input them back into the Kuwait Finder database, the infrastructure of Kuwait City had changed once again.\r\n\r\n\u201cWhere are the new streets? Where are the building footprints?\u201d Maher Abdel Karim, PACI\u2019s GIS consultant said recently, summarizing the challenges. \u201cOnce we were finished updating them, there would be new satellite imagery we\u2019d missed, and we\u2019d have to repeat the process again.\u201d"},{"acf_fc_layout":"image","image":335841,"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"PACI needed to make necessary changes to Kuwait Finder in a way that was quick, inexpensive, accurate and simple\u2014the more automated, the better. Compared to the vast resources available to a company like Google, PACI\u2019s size and budget were limited.\r\n\r\nPACI researched the viability of <a href=\"https:\/\/www.esri.com\/en-us\/artificial-intelligence\/overview\">artificial intelligence<\/a> to provide a solution.\r\n\r\n\u201cWe thought, how can we use AI to automate the process?\u201d Abdel Karim said. \u201cWe wanted to use machine learning to extract street data and building footprints from the satellite imagery while using the minimum amount of human input.\u201d\r\n<h3><strong>Deep Learning to the Rescue<\/strong><\/h3>\r\nDeep learning, a powerful form of AI, involves teaching a computer to detect patterns in large amounts of data, and to recognize and extract just the information you want. If done right, the algorithm acts quickly and thoroughly and even finds changes that human intuition would miss.\r\n\r\nPACI\u2019s GIS team needed to teach the computer how to recognize building footprints from satellite data, and also note which ones were new since the last batch of satellite images. Elsewhere in the Middle East, an oil-and-gas company used machine learning to alert it if any new structures were being built near its <a href=\"https:\/\/ivt.maps.arcgis.com\/apps\/MapSeries\/index.html?appid=9766dba97c954fcaa175da83b72ccf06\">thousands of miles of pipeline<\/a>. PACI\u2019s task was a bit more complex as it wanted the computer to tell it about anything new across the entire country."},{"acf_fc_layout":"image","image":335821,"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"If properly trained, a machine learning algorithm can move conceptual mountains. At the beginning of the process, however, the computer needs to be taught to \u201cread.\u201d This requires the same patience and skill required to teach a young child to recognize letters, then words and sentences, and finally complex thoughts.\r\n\r\nPACI needed to establish a \u201cground truth,\u201d a common geospatial framework that would encompass the existing database. After much trial-and-error, the staff was able to train using 75 square kilometers of data to provide input for the model to scan 3,000 square kilometers of satellite imagery.\r\n<h3><strong>Knowing the Local Vocabulary Pays Off<\/strong><\/h3>\r\nFor PACI, the main challenge was giving the program enough information to recognize buildings and streets.\r\n\r\n\u201cThe contrast between land, streets, and buildings, was very minimal,\u201d Abdel Karim explained. \u201cIt\u2019s hard even for a person to differentiate among them by eye. And our threshold for saying that our model worked was very high.\u201d\r\n\r\nOften, with shadows cast by the sweltering summer sun, building footprints did not easily align with the images captured by the satellite. The darkened overlapping edges often confused the program.\r\n\r\nStreets posed their own unique problem. They had to be recognizable individually, but also as part of a coherent grid. PACI\u2019s staff had to ensure that all streets in the imagery were connected by common \u201ccenter lines\u201d\u2014and that the lines in the new images fit seamlessly with those in the existing street network.\r\n\r\n\u201cYou really have to take your local experience and use it as a \u2018flavor\u2019 on top of the satellite imagery,\u201d Abdel Karim said. \u201cThat\u2019s what makes it possible for machine learning to perform these calculations.\u201d"},{"acf_fc_layout":"image","image":337101,"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"The teaching process required serious work. But once the machine learning model was ready, the rest was a relative breeze. PACI devoted time to training and fine tuning the model, knowing that this work would pay dividends into the future.\r\n\r\nThe model and training data set can be used today to update the database in about three hours and provide even more accurate maps than before. And this model will live on and can continue to be modified and enhanced by PACI into the future, taking into consideration new architecture styles, building types or other new features.\r\n<h3><strong>Reaching the Payoff<\/strong><\/h3>\r\nThe model could now do, before lunchtime, a task that previously took five humans a year to complete. The time spent to train the machine to extract features from satellite imagery to its GIS repository paid off.\r\n\r\n\u201cYou need to have a lot of patience,\u201d Abdel Karim said. \u201cIt can be \u00a0a fairly long, iterative process to train, predict, evaluate, and then go in and do it all over again. It\u2019s a knowledge investment. You\u2019re investing in your staff, who are working, studying, and learning how to do this, to get the desired output.\u201d\r\n\r\nHowever, the return is worth the investment. The organization now has the confidence that it can keep the Kuwait base map and Kuwait Finder updated in a timely manner and have it continue to be the authoritative source for information in Kuwait. The time and cost savings from this automation will allow the small team at PACI to keep innovating to stay ahead of the competition. The lessons learned from using deep learning and <a href=\"https:\/\/www.esri.com\/en-us\/arcgis\/products\/imagery-remote-sensing\/overview\">remotely sensed data<\/a> will also feed new ideas for PACI staff and will foster more innovation.\r\n\r\n\u201cWe will continue to use machine learning to create new features and new layers for our GIS repository that people really need and are looking for,\u201d Abdel Karim said.\r\n\r\n&nbsp;\r\n\r\nExplore more examples of where <a href=\"https:\/\/medium.com\/geoai\">artificial intelligence and GIS intersect<\/a>."},{"acf_fc_layout":"image","image":335831,"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"sidebar","layout":"standard","image_reference":null,"image_reference_figure":"","spotlight_image":null,"section_title":"","spotlight_name":"","position":"Center","content":"<h2><strong>Turning AI Eyes to the Streets<\/strong><\/h2>\r\nSimilar to Google Street View, PACI invested in their own mobile mapping vehicle to capture street-level images to help Kuwaitis in their wayfinding. Seeing an address in the same context as looking through car window helps pinpoint the final destination, and better visualize the location.\r\n\r\n\u201cNow, the idea came to us to use machine learning to extract features from this street-level imagery,\u201d said Maher Abdel Karim, PACI\u2019s GIS consultant. \u201cTo add things missing in our database, such as traffic lights and signs.\u201d\r\n\r\nPACI also envisions using the street level imagery to help monitor the progress of current projects, and also to guide the city\u2019s maintenance operations.\r\n\r\n\u201cWe could find the streets that have bad conditions,\u201d Abdel Karim said. \u201cInstead of doing mass street rehabilitation, we could target only the part that has a problem.\u201d\r\n\r\nPACI is now adapting the same machine learning training tools it used for satellite imagery to this new street-level task.\r\n\r\n\u201cWe are trying to build this kind of machine learning-supported data approach so that we can feed our models with data and specify the output that we are looking for,\u201d Abdel Karim said.","snippet":""}],"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 Helps Kuwait Automate Map Updates<\/title>\n<meta name=\"description\" content=\"Deep learning helps keep the Kuwait base map up-to-date and complete to provide accurate information in a rapidly developing country.\" \/>\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\/blog\/deep-learning-helps-kuwait-automate-map-updates\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Deep Learning Helps Kuwait Automate Map Updates to Better Serve Citizens\" 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