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3<aside class=search-modal id=search><div class=container><section class=search-header><div class="row no-gutters justify-content-between mb-3"><div class=col-6><h1>Search</h1></div><div class="col-6 col-search-close"><a class=js-search href=# aria-label=Close><i class="fas fa-times-circle text-muted" aria-hidden=true></i></a></div></div><div id=search-box><input name=q id=search-query placeholder=Search... autocapitalize=off autocomplete=off autocorrect=off spellcheck=false type=search class=form-control aria-label=Search...></div></section><section class=section-search-results><div id=search-hits></div></section></div></aside><div class="page-header header--fixed"><header><nav class="navbar navbar-expand-lg navbar-light compensate-for-scrollbar" id=navbar-main><div class=container-xl><div class="d-none d-lg-inline-flex"><a class=navbar-brand href=/>GEOAI group</a></div><button type=button class=navbar-toggler data-toggle=collapse data-target=#navbar-content aria-controls=navbar-content aria-expanded=false aria-label="Toggle navigation">
4<span><i class="fas fa-bars"></i></span></button><div class="navbar-brand-mobile-wrapper d-inline-flex d-lg-none"><a class=navbar-brand href=/>GEOAI group</a></div><div class="navbar-collapse main-menu-item collapse justify-content-center" id=navbar-content><ul class="navbar-nav d-md-inline-flex"><li class=nav-item><a class=nav-link href=/#top><span>Home</span></a></li><li class=nav-item><a class=nav-link href=/#project><span>Research</span></a></li><li class=nav-item><a class=nav-link href=/#publication><span>Publications</span></a></li><li class=nav-item><a class=nav-link href=/#people><span>Team</span></a></li><li class=nav-item><a class=nav-link href=https://geoai-cnrs.notion.site/ target=_blank rel=noopener><span>Interns</span></a></li><li class=nav-item><a class=nav-link href=/medias><span>Media</span></a></li><li class=nav-item><a class=nav-link href=/#contact><span>Contact Us</span></a></li></ul></div><ul class="nav-icons navbar-nav flex-row ml-auto d-flex pl-md-2"></ul></div></nav></header></div><div class=page-body><article class="article article-project"><div class="article-container pt-3"><h1>GeoUrban-AI Mapping</h1><div class=article-metadata><span class=article-date>Last updated on
5Jul 19, 2024</span></div></div><div class="article-header article-container featured-image-wrapper mt-4 mb-4" style=max-width:512px;max-height:512px><div style=position:relative><img src=/project/geourbanai-mapping/featured_hu8d3c02823ad7d427e98b3c6c9bd338f9_583521_6aaac3e304c44fad36c38e9179d425e4.webp width=512 height=512 alt class=featured-image></div></div><div class=article-container><div class=article-style><div class=article-style itemprop=articleBody><p><b><h2 id=geourban-ai-tool>GeoUrban-AI Tool</h2></b></p><p>GeoUrban-AI powered <a href=http://geoai.cnrs.edu.lb/urbanmodels/ target=_blank>[Tool]</a> allows to autonomously extract buildings' footprints from satellite/aerial imagery. The aim is to make our applied research findings accessible to a larger community. Though it is not common to develop such a web-based tool for a team that is mainly involved with research rather than product development; We believe this demo would help us target a larger audience and would open up new horizons for the GEOAI group.</p><img src=./lebanonmap.png><p><b><h2 id=national-urban-map>Automated National Urban Map Extraction</h2></b></p><p>Developing countries usually lack the proper governance means to generate and regularly update a national rooftop map. Using traditional photogrammetry and surveying methods to produce buildings map at the federal level is costly and time-consuming. Relying on earth observation and deep learning methods, we aim in this project to bridge this gap and propose a pipeline to autonomously produce national urban maps. We detail all engineering steps to replicate this work and ensure highly accurate results in dense and slum areas witnessed in regions that lack proper urban planning in the Global South. We applied a case study of the proposed pipeline to Lebanon and successfully produced the first comprehensive national building footprint map with approximately 1 Million units with an 84% accuracy. The proposed architecture relies on advanced augmentation techniques to overcome dataset scarcity, which is often the case in developing countries.<p><a href=https://geogroup.ai/catalogue/ target=_blank>[Map]</a> encompases ~1 MM urban units with an 84% accuracy. When you ZOOM IN, the dots on the map refer to the centroids of each building at a specific geographical location <a href=https://geogroup.ai/publication/2024igarss_nationalurbanmap/2
5024IGARSS_NationalUrbanMap.pdf>[Paper]</a></p>.</p><img src=./solar.png><p><b><h2 id=solar-potential>Solar Potential Map for Lebanon</h2></b></p><p>Estimating the solar potential of buildings' rooftops at a large scale is a fundamental step for every country to utilize its solar power efficiently. However, such estimation becomes time-consuming and costly if done through on-site measurements. This project uses deep learning-based multi-class instance segmentation to extract buildings' footprints from satellite images. We propose a photovoltaic panels placement algorithm to estimate the solar potential of every rooftop, which results in Lebanon's first buildings' solar potential map. We report average and total solar potential per district and localize regions corresponding to the highest solar potential yield <a href=https://geogroup.ai/publication/2022SolarMapLebanon/>[Paper]</a>.</p><p><img src=./scinet.jpg title="Sci-Net architecture"></p><p><b><h2 id=sci-net>Sci-Net: a Scale Invariant Model for Building Detection from Aerial Images</h2><p></b>Buildings&rsquo; segmentation is a fundamental task in the field of earth observation and aerial imagery analysis. Most existing deep learning based algorithms in the literature can be applied to fixed or narrow-ranged spatial resolution imagery. In practical scenarios, users deal with a wide spectrum of images resolution and thus, often need to resample a given aerial image to match the spatial resolution of the dataset used to train the deep learning model. This however, would result in a severe degradation in the quality of the output segmentation masks. To deal with this issue, we propose in this research a Scale-invariant neural network (Sci-Net) that is able to segment buildings present in aerial images at different spatial resolutions. Specifically, we modified the U-Net architecture and fused it with dense Atrous Spatial Pyramid Pooling (ASPP) to extract fine-grained multi-scale representations. We compared the performance of our proposed model against several state-of-the-art models on the Open Cities AI dataset, and showed that Sci-Net provides a steady improvement margin in performance across all resolutions available in the dataset <a href=https://geogroup.ai/publication/2022SciNet/>[Paper]</a>.</p></p><p><img src=./bdabtc.jpg title="Buildings Classification using Very High Resolution Satellite Imagery"></p><p><b><h2 id=bda-btc>Buildings Classification using Very High Resolution Satellite Imagery</h2><p></b>Buildings classification using satellite images is becoming more important for several applications such as damage assessment, resource allocation, and population estimation. This project focuses on two specific classification tasks: building-type classification (residential or non-residential) and building-damage assessment (damaged or not-damaged). We propose to rely solely on RGB satellite images and follow a 2-stage deep learning-based approach. Due to the lack of an appropriate dataset for the residential/non-residential building classification, we introduce a new dataset of high-resolution satellite images. We conducted extensive experiments to select the best hyper-parameters, model architecture, and training paradigm, and we propose a new transfer learning-based approach that outperforms classical methods <a href=https://geogroup.ai/publication/2023ECRS_BDABTC/>[Paper]</a>.</p></p></div></div><div class=article-tags><a class="badge badge-light" href=/tag/deep-learning/>Deep Learning</a></div><div class=share-box><ul class=share><li><a href="https://twitter.com/intent/tweet?url=https%3A%2F%2Fgeogroup.ai%2Fproject%2Fgeourbanai-mapping%2F&amp;text=GeoUrban-AI+Mapping" target=_blank rel=noopener class=share-btn-twitter aria-label=twitter><i class="fab fa-twitter"></i></a></li><li><a href="https://www.facebook.com/sharer.php?u=https%3A%2F%2Fgeogroup.ai%2Fproject%2Fgeourbanai-mapping%2F&amp;t=GeoUrban-AI+Mapping" target=_blank rel=noopener class=share-btn-facebook aria-label=facebook><i class="fab fa-facebook"></i></a></li><li><a href="mailto:?subject=GeoUrban-AI%20Mapping&amp;body=https%3A%2F%2Fgeogroup.ai%2Fproject%2Fgeourbanai-mapping%2F" target=_blank rel=noopener class=share-btn-email aria-label=envelope><i class="fas fa-envelope"></i></a></li><li><a href="https://www.linkedin.com/shareArticle?url=https%3A%2F%2Fgeogroup.ai%2Fproject%2Fgeourbanai-mapping%2F&amp;title=GeoUrban-AI+Mapping" target=_blank rel=noopener class=share-btn-l
5inkedin aria-label=linkedin-in><i class="fab fa-linkedin-in"></i></a></li><li><a href="whatsapp://send?text=GeoUrban-AI+Mapping%20https%3A%2F%2Fgeogroup.ai%2Fproject%2Fgeourbanai-mapping%2F" target=_blank rel=noopener class=share-btn-whatsapp aria-label=whatsapp><i class="fab fa-whatsapp"></i></a></li><li><a href="https://service.weibo.com/share/share.php?url=https%3A%2F%2Fgeogroup.ai%2Fproject%2Fgeourbanai-mapping%2F&amp;title=GeoUrban-AI+Mapping" target=_blank rel=noopener class=share-btn-weibo aria-label=weibo><i class="fab fa-weibo"></i></a></li></ul></div><div class="project-related-pages content-widget-hr"><h2>Publications</h2><div class="media stream-item view-compac"><div class=media-body><div class="section-subheading article-title mb-0 mt-0"><a href=/publication/2024igarss_nationalurbanmap/>Automated National Urban Map Extraction</a></div><a href=/publication/2024igarss_nationalurbanmap/ class=summary-link><div class=article-style>Developing countries usually lack the proper governance means to generate and regularly update a national rooftop map. Using …</div></a><div class="stream-meta article-metadata"><div><span><a href=/author/hasan-nasrallah/>Hasan Nasrallah</a></span>, <span><a href=/author/abed-ellatif-samhat/>Abed Ellatif Samhat</a></span>, <span><a href=/author/cristiano-nattero/>Cristiano Nattero</a></span>, <span><a href=/author/ali-j.-ghandour/>Ali J. GHANDOUR</a></span></div></div><div class=btn-links><a class="btn btn-outline-primary btn-page-header btn-sm" href=/publication/2024igarss_nationalurbanmap/2024IGARSS_NationalUrbanMap.pdf target=_blank rel=noopener>PDF</a>
6<a href=# class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal" data-filename=/publication/2024igarss_nationalurbanmap/cite.bib>Cite</a>
7<a class="btn btn-outline-primary btn-page-header btn-sm" href=/project/geourbanai-mapping/>Project</a>
8<a class="btn btn-outline-primary btn-page-header btn-sm" href=https://doi.org/10.1109/IGARSS53475.2024.10642549 target=_blank rel=noopener>DOI</a></div></div><div class=ml-3><a href=/publication/2024igarss_nationalurbanmap/><img src=/publication/2024igarss_nationalurbanmap/featured_hu04d2e8070d983a9994887c0ac3ac82ff_224537_150x0_resize_q75_h2_lanczos_3.webp height=176 width=150 alt="Automated National Urban Map Extraction" loading=lazy></a></div><div class=ml-3><div data-badge-popover=left data-badge-type=donut data-doi=10.1109/IGARSS53475.2024.10642549 data-condensed=true class=altmetric-embed data-hide-no-mentions=true></div><div class=__dimensions_badge_embed__ data-doi=10.1109/IGARSS53475.2024.10642549 data-legend=hover-left data-style=small_circle></div><a href="https://plu.mx/plum/a/?doi=10.1109/IGARSS53475.2024.10642549" data-popup=bottom data-size=medium class="plumx-plum-print-popup plum-bigben-theme" data-site=plum data-hide-when-empty=true data-pass-hidden-categories=true></a></div></div><div class="media stream-item view-compac"><div class=media-body><div class="section-subheading article-title mb-0 mt-0"><a href=/publication/2023ecrs_zeroshotsam/>Zero-Shot Refinement of Buildings' Segmentation Models using SAM</a></div><a href=/publication/2023ecrs_zeroshotsam/ class=summary-link><div class=article-style>Foundation models have demonstrated unparalleled performance across diverse tasks involving vision, language, and multimodal domains. …</div></a><div class="stream-meta article-metadata"><div><span><a href=/author/ali-mayladan/>Ali Mayladan</a></span>, <span><a href=/author/hasan-nasrallah/>Hasan Nasrallah</a></span>, <span><a href=/author/hasan-moughnieh/>Hasan Moughnieh</a></span>, <span><a href=/author/mustafa-shukor/>Mustafa Shukor</a></span>, <span><a href=/author/ali-j.-ghandour/>Ali J. GHANDOUR</a></span></div></div><div class=btn-links><a class="btn btn-outline-primary btn-page-header btn-sm" href=/publication/2023ecrs_zeroshotsam/2023ECRS_ZeroShotSAM.pdf target=_blank rel=noopener>PDF</a>
9<a class="btn btn-outline-primary btn-page-header btn-sm" href=https://github.com/geoaigroup/GEOAI-ECRS2023/tree/main/Zero-Shot%20Refinement%20of%20Buildings%20Segmentation%20Models%20using%20SAM target=_blank rel=noopener>
9Code</a>
10<a class="btn btn-outline-primary btn-page-header btn-sm" href=/project/geourbanai-mapping/>Project</a></div></div><div class=ml-3><a href=/publication/2023ecrs_zeroshotsam/><img src=/publication/2023ecrs_zeroshotsam/featured_hub6aa90ec86a00915f864c3a94de020c3_639194_150x0_resize_q75_h2_lanczos_3.webp height=172 width=150 alt="Zero-Shot Refinement of Buildings' Segmentation Models using SAM" loading=lazy></a></div><div class=ml-3></div></div><div class="media stream-item view-compac"><div class=media-body><div class="section-subheading article-title mb-0 mt-0"><a href=/publication/2022solarmaplebanon/>Lebanon Solar Rooftop Potential Assessment Using Buildings Segmentation From Aerial Images</a></div><a href=/publication/2022solarmaplebanon/ class=summary-link><div class=article-style>Estimating solar rooftop potential at a national level is a fundamental building block for every country to utilize solar power …</div></a><div class="stream-meta article-metadata"><div><span><a href=/author/hasan-nasrallah/>Hasan Nasrallah</a></span>, <span><a href=/author/abed-ellatif-samhat/>Abed Ellatif Samhat</a></span>, <span><a href=/author/yilei-shi/>Yilei Shi</a></span>, <span><a href=/author/xiaoxiang-zhu/>Xiaoxiang Zhu</a></span>, <span><a href=/author/ghaleb-faour/>Ghaleb Faour</a></span>, <span><a href=/author/ali-j.-ghandour/>Ali J. GHANDOUR</a></span></div></div><div class=btn-links><a class="btn btn-outline-primary btn-page-header btn-sm" href=/publication/2022solarmaplebanon/2022SolarMapLebanon.pdf target=_blank rel=noopener>PDF</a>
11<a href=# class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal" data-filename=/publication/2022solarmaplebanon/cite.bib>Cite</a>
12<a class="btn btn-outline-primary btn-page-header btn-sm" href=/project/geourbanai-mapping/>Project</a>
13<a class="btn btn-outline-primary btn-page-header btn-sm" href=https://doi.org/10.1109/JSTARS.2022.3181446 target=_blank rel=noopener>DOI</a></div></div><div class=ml-3><a href=/publication/2022solarmaplebanon/><img src=/publication/2022solarmaplebanon/featured_hua03109d3a57914e91e12dc72b34e54a4_41771_150x0_resize_q75_h2_lanczos.webp height=74 width=150 alt="Lebanon Solar Rooftop Potential Assessment Using Buildings Segmentation From Aerial Images" loading=lazy></a></div><div class=ml-3><div data-badge-popover=left data-badge-type=donut data-doi=10.1109/JSTARS.2022.3181446 data-condensed=true class=altmetric-embed data-hide-no-mentions=true></div><div class=__dimensions_badge_embed__ data-doi=10.1109/JSTARS.2022.3181446 data-legend=hover-left data-style=small_circle></div><a href="https://plu.mx/plum/a/?doi=10.1109/JSTARS.2022.3181446" data-popup=bottom data-size=medium class="plumx-plum-print-popup plum-bigben-theme" data-site=plum data-hide-when-empty=true data-pass-hidden-categories=true></a></div></div><div class="media stream-item view-compac"><div class=media-body><div class="section-subheading article-title mb-0 mt-0"><a href=/publication/2022scinet/>Sci-Net: Scale-Invariant Model for Buildings Segmentation from Aerial Imagery</a></div><a href=/publication/2022scinet/ class=summary-link><div class=article-style>Buildings’ segmentation is a fundamental task in the field of earth observation and aerial imagery analysis. Most existing deep …</div></a><div class="stream-meta article-metadata"><div><span><a href=/author/hasan-nasrallah/>Hasan Nasrallah</a></span>, <span><a href=/author/mustafa-shukor/>Mustafa Shukor</a></span>, <span><a href=/author/ali-j.-ghandour/>Ali J. GHANDOUR</a></span></div></div><div class=btn-links><a class="btn btn-outline-primary btn-page-header btn-sm" href=/publication/2022scinet/2022SciNet.pdf target=_blank rel=noopener>PDF</a>
14<a href=# class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal" data-filename=/publication/2022scinet/cite.bib>Cite</a>
15<a class="btn btn-outline-primary btn-page-header btn-sm" href=/project/geourbanai-mapping/>Project</a>
16<a class="btn btn-outline-primary btn-page-header btn-sm" href=https://doi.org/10.1007/s11760-023-02520-3 target=_blank rel=noopener>DOI</a></div></div><div class=ml-3><a href=/publication/2022scinet/><img src=/publication/2022scinet/featured_hue5d76a50bdbe22be269754bc3190f0fc_55599_150x0_resize_q75_h2_lanczos.webp height=79 width=150 alt="Sci-Net: Scale-Invariant Model for Buildings Segmentation from Aerial Imagery" loading=lazy></a></div><div class=ml-3><div data-badge-popover=left data-badge-type=donut data-doi=10.1007/s11760-023-02520-3 data-condensed=true class=altmetric-embed data-hide-no-mentions=true></div><div class=__dimensions_badge_embed__ data-doi=10.1007/s11760-023-02520-3 data-legend=hover-left data-style=small_circle></div><a href="https://plu.mx/plum/a/?doi=10.1007/s11760-023-02520-3" data-popup=bottom data-size=medium class="plumx-plum-print-popup plum-bigben-theme" data-site=plum data-hide-when-empty=true data-pass-hidden-categories=true></a></div></div><div class="media stream-item view-compac"><div class=media-body><div class="section-subheading article-title mb-0 mt-0"><a href=/publication/2023ecrs_bdabtc/>Buildings Classification using Very High Resolution Satellite Imagery</a></div><a href=/publication/2023ecrs_bdabtc/ class=summary-link><div class=article-style>Buildings classification using satellite images is becoming more important for several applications such as damage assessment, resource …</div></a><div class="stream-meta article-metadata"><div><span><a href=/author/mohamad-dimassi/>Mohamad Dimassi</a></span>, <span><a href=/author/abed-ellatif-samhat/>Abed Ellatif Samhat</a></span>, <span><a href=/author/mohamad-zaraket/>Mohamad Zaraket</a></span>, <span><a href=/author/jamal-haydar/>Jamal Haydar</a></span>, <span><a href=/author/ali-j.-ghandour/>Ali J. GHANDOUR</a></span></div></div><div class=btn-links><a class="btn btn-outline-primary btn-page-header btn-sm" href=/publication/2023ecrs_bdabtc/2023ECRS_BDABTC.pdf target=_blank rel=noopener>PDF</a>
17<a href=# class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal" data-filename=/publication/2023ecrs_bdabtc/cite.bib>Cite</a>
18<a class="btn btn-outline-primary btn-page-header btn-sm" href=https://storage.googleapis.com/bbtc/bbtc_dataset.tar.gz target=_blank rel=noopener>Dataset</a>
19<a class="btn btn-outline-primary btn-page-header btn-sm" href=/project/geourbanai-mapping/>Project</a></div></div><div class=ml-3><a href=/publication/2023ecrs_bdabtc/><img src=/publication/2023ecrs_bdabtc/featured_hueff1a1e5b08a63632b676ca8b6cb9f82_22055_150x0_resize_q75_h2_lanczos.webp height=64 width=150 alt="Buildings Classification using Very High Resolution Satellite Imagery" loading=lazy></a></div><div class=ml-3></div></div><div class="media stream-item view-compac"><div class=media-body><div class="section-subheading article-title mb-0 mt-0"><a href=/publication/2019buildingshadow/>Building shadow detection based on multi-thresholding segmentation</a></div><a href=/publication/2019buildingshadow/ class=summary-link><div class=article-style>The human eye can easily identify shadows of illuminated objects. However, automatically detecting such shadows with the use of …</div></a><div class="stream-meta article-metadata"><div><span><a href=/author/ali-j.-ghandour/>Ali J. GHANDOUR</a></span>, <span><a href=/author/abdul-karim-gizzini/>Abdul Karim Gizzini</a></span></div></div><div class=btn-links><a class="btn btn-outline-primary btn-page-header btn-sm" href=/publication/2019buildingshadow/2019BuildingShadow.pdf target=_blank rel=noopener>PDF</a>
20<a href=# class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal" data-filename=/publication/2019buildingshadow/cite.bib>Cite</a>
21<a class="btn btn-outline-primary btn-page-header btn-sm" href=/project/geourbanai-mapping/>Project</a>
22<a class="btn btn-outline-primary btn-page-header btn-sm" href=https://doi.org/10.1007/s11760-018-1363-0 target=_blank rel=noopener>DOI</a></div></div><div class=ml-3><a href=/publication/2019buildingshadow/><img src=/publication/2019buildingshadow/featured_hu63ee84fed4182b138c67fc0884226f7c_481858_150x0_resize_q75_h2_lanczos_3.webp height=124 width=150 alt="Building shadow detection based on multi-thresholding segmentation" loading=lazy></a></div><div class=ml-3><div data-badge-popover=left data-badge-type=donut data-doi=10.1007/s11760-018-1363-0 data-condensed=true class=altmetric-embed data-hide-no-mentions=true></div><div class=__dimensions_badge_embed__ data-doi=10.1007/s11760-018-1363-0 data-legend=hover-left data-style=small_circle></div><a href="https://plu.mx/plum/a/?doi=10.1007/s11760-018-1363-0" data-popup=bottom data-size=medium class="plumx-plum-print-popup plum-bigben-theme" data-site=plum data-hide-when-empty=true data-pass-hidden-categories=true></a></div></div><div class="media stream-item view-compac"><div class=media-body><div class="section-subheading article-title mb-0 mt-0"><a href=/publication/2018vehicledetection/>Autonomous Vehicle Detection and Classification in High Resolution Satellite Imagery</a></div><a href=/publication/2018vehicledetection/ class=summary-link><div class=article-style>High resolution remote sensing data can provide worldwide images rapidly contrasted with conventional strategies for information …</div></a><div class="stream-meta article-metadata"><div><span><a href=/author/ali-j.-ghandour/>Ali J. GHANDOUR</a></span>, <span><a href=/author/houssam-krayem/>Houssam Krayem</a></span>, <span><a href=/author/abdul-karim-gizzini/>Abdul Karim Gizzini</a></span></div></div><div class=btn-links><a class="btn btn-outline-primary btn-page-header btn-sm" href=/publication/2018vehicledetection/2018VehicleDetection.pdf target=_blank rel=noopener>PDF</a>
23<a href=# class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal" data-filename=/publication/2018vehicledetection/cite.bib>Cite</a>
24<a class="btn btn-outline-primary btn-page-header btn-sm" href=/project/geourbanai-mapping/>Project</a>
25<a class="btn btn-outline-primary btn-page-header btn-sm" href=https://doi.org/10.1109/ACIT.2018.8672712 target=_blank rel=noopener>DOI</a></div></div><div class=ml-3><a href=/publication/2018vehicledetection/><img src=/publication/2018vehicledetection/featured_hu2ee55d157a79b36504543087a16b97d9_35209_150x0_resize_q75_h2_lanczos.webp height=28 width=150 alt="Autonomous Vehicle Detection and Classification in High Resolution Satellite Imagery" loading=lazy></a></div><div class=ml-3><div data-badge-popover=left data-badge-type=donut data-doi=10.1109/ACIT.2018.8672712 data-condensed=true class=altmetric-embed data-hide-no-mentions=true></div><div class=__dimensions_badge_embed__ data-doi=10.1109/ACIT.2018.8672712 data-legend=hover-left data-style=small_circle></div><a href="https://plu.mx/plum/a/?doi=10.1109/ACIT.2018.8672712" data-popup=bottom data-size=medium class="plumx-plum-print-popup plum-bigben-theme" data-site=plum data-hide-when-empty=true data-pass-hidden-categories=true></a></div></div><div class="media stream-item view-compac"><div class=media-body><div class="section-subheading article-title mb-0 mt-0"><a href=/publication/2018mdpi_buildings/>Autonomous Building Detection Using Edge Properties and Image Color Invariants</a></div><a href=/publication/2018mdpi_buildings/ class=summary-link><div class=article-style>Automated building extraction from high-resolution satellite imagery is a challenging research problem, and several issues remain with …</div></a><div class="stream-meta article-metadata"><div><span><a href=/author/ali-j.-ghandour/>Ali J. GHANDOUR</a></span>, <span><a href=/author/abdul-karim-gizzini/>Abdul Karim Gizzini</a></span></div></div><div class=btn-links><a class="btn btn-outline-primary btn-page-header btn-sm" href=/publication/2018mdpi_buildings/2018MDPI_Buildings.pdf target=_blank rel=noopener>PDF</a>
26<a href=# class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal" data-filename=/publication/2018mdpi_buildings/cite.bib>Cite</a>
27<a class="btn btn-outline-primary btn-page-header btn-sm" href=/project/geourbanai-mapping/>Project</a>
28<a class="btn btn-outline-primary btn-page-header btn-sm" href=https://doi.org/10.3390/buildings8050065 target=_blank rel=noopener>DOI</a></div></div><div class=ml-3><a href=/publication/2018mdpi_buildings/><img src=/publication/2018mdpi_buildings/featured_hu8c16a60b09d2bb4451fa3e1161b37599_79706_150x0_resize_q75_h2_lanczos.webp height=127 width=150 alt="Autonomous Building Detection Using Edge Properties and Image Color Invariants" loading=lazy></a></div><div class=ml-3><div data-badge-popover=left data-badge-type=donut data-doi=10.3390/buildings8050065 data-condensed=true class=altmetric-embed data-hide-no-mentions=true></div><div class=__dimensions_badge_embed__ data-doi=10.3390/buildings8050065 data-legend=hover-left data-style=small_circle></div><a href="https://plu.mx/plum/a/?doi=10.3390/buildings8050065" data-popup=bottom data-size=medium class="plumx-plum-print-popup plum-bigben-theme" data-site=plum data-hide-when-empty=true data-pass-hidden-categories=true></a></div></div><div class="media stream-item view-compac"><div class=media-body><div class="section-subheading article-title mb-0 mt-0"><a href=/publication/2018ecrs_cubesat/>Design of a Lebanese Cube Satellite</a></div><a href=/publication/2018ecrs_cubesat/ class=summary-link><div class=article-style>Nowadays, nanosatellites are widely used in space technology due to their small size, ease of deployment, and relatively short …</div></a><div class="stream-meta article-metadata"><div><span><a href=/author/ali-j.-ghandour/>Ali J. GHANDOUR</a></span>, <span><a href=/author/mohamad-abdallah/>Mohamad Abdallah</a></span></div></div><div class=btn-links><a class="btn btn-outline-primary btn-page-header btn-sm" href=/publication/2018ecrs_cubesat/2018ECRS_CubeSat.pdf target=_blank rel=noopener>PDF</a>
29<a href=# class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal" data-filename=/publication/2018ecrs_cubesat/cite.bib>Cite</a>
30<a class="btn btn-outline-primary btn-page-header btn-sm" href=/project/geourbanai-mapping/>Project</a>
31<a class="btn btn-outline-primary btn-page-header btn-sm" href=https://doi.org/10.3390/ecrs-2-05135 target=_blank rel=noopener>DOI</a></div></div><div class=ml-3><a href=/publication/2018ecrs_cubesat/><img src=/publication/2018ecrs_cubesat/featured_hu688039fd8e03b35b41f4a129dae0e1b8_80340_150x0_resize_q75_h2_lanczos.webp height=64 width=150 alt="Design of a Lebanese Cube Satellite" loading=lazy></a></div><div class=ml-3><div data-badge-popover=left data-badge-type=donut data-doi=10.3390/ecrs-2-05135 data-condensed=true class=altmetric-embed data-hide-no-mentions=true></div><div class=__dimensions_badge_embed__ data-doi=10.3390/ecrs-2-05135 data-legend=hover-left data-style=small_circle></div><a href="https://plu.mx/plum/a/?doi=10.3390/ecrs-2-05135" data-popup=bottom data-size=medium class="plumx-plum-print-popup plum-bigben-theme" data-site=plum data-hide-when-empty=true data-pass-hidden-categories=true></a></div></div><div class="media stream-item view-compac"><div class=media-body><div class="section-subheading article-title mb-0 mt-0"><a href=/publication/2018ecrs_postwardamage/>Post-War Building Damage Detection</a></div><a href=/publication/2018ecrs_postwardamage/ class=summary-link><div class=article-style>Natural disasters and wars wreak havoc not only on individuals and critical infrastru
31cture, but also leave behind ruined residential …</div></a><div class="stream-meta article-metadata"><div><span><a href=/author/ali-j.-ghandour/>Ali J. GHANDOUR</a></span>, <span><a href=/author/abdul-karim-gizzini/>Abdul Karim Gizzini</a></span></div></div><div class=btn-links><a class="btn btn-outline-primary btn-page-header btn-sm" href=/publication/2018ecrs_postwardamage/2018ECRS_PostWarDamage.pdf target=_blank rel=noopener>PDF</a>
32<a href=# class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal" data-filename=/publication/2018ecrs_postwardamage/cite.bib>Cite</a>
33<a class="btn btn-outline-primary btn-page-header btn-sm" href=/project/geourbanai-mapping/>Project</a>
34<a class="btn btn-outline-primary btn-page-header btn-sm" href=https://doi.org/10.3390/ecrs-2-05172 target=_blank rel=noopener>DOI</a></div></div><div class=ml-3><a href=/publication/2018ecrs_postwardamage/><img src=/publication/2018ecrs_postwardamage/featured_hu2c9351c30bdc74ed4123013c4e013f8c_114709_150x0_resize_q75_h2_lanczos.webp height=79 width=150 alt="Post-War Building Damage Detection" loading=lazy></a></div><div class=ml-3><div data-badge-popover=left data-badge-type=donut data-doi=10.3390/ecrs-2-05172 data-condensed=true class=altmetric-embed data-hide-no-mentions=true></div><div class=__dimensions_badge_embed__ data-doi=10.3390/ecrs-2-05172 data-legend=hover-left data-style=small_circle></div><a href="https://plu.mx/plum/a/?doi=10.3390/ecrs-2-05172" data-popup=bottom data-size=medium class="plumx-plum-print-popup plum-bigben-theme" data-site=plum data-hide-when-empty=true data-pass-hidden-categories=true></a></div></div></div></div></article></div><div class=page-footer><div class=container><footer class=site-footer><p class="powered-by copyright-license-text">© 2026, GEOAI group - built using <a href=https://github.com/HugoBlox/hugo-blox-builder/tree/main/starters/research-group target=_blank>Hugo Blox Research Group Theme</a></p></footer></div></div>
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