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Mapping individual abandoned houses across cities by integrating VHR remote sensing and street view imagery / Shengyuan Zou in International journal of applied Earth observation and geoinformation, vol 113 (September 2022)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 113 (September 2022) . - n° 103018 Titre : Mapping individual abandoned houses across cities by integrating VHR remote sensing and street view imagery Type de document : Article/Communication Auteurs : Shengyuan Zou, Auteur ; Le Wang, Auteur Année de publication : 2022 Article en page(s) : n° 103018 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] carte thématique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] détection d'objet [Termes IGN] image à très haute résolution [Termes IGN] image Streetview [Termes IGN] logement [Termes IGN] New York (Etats-Unis ; état) [Termes IGN] théorie de Dempster-Shafer [Termes IGN] zone urbaine Résumé : (auteur) Abandoned houses (AH) present an utmost challenge confronting the urban environment in contemporary U.S. shrinking cities. Data accessibility is a major hurdle that prevents the acquisition of large-scale AH information at the individual property level. To this end, the latest revolution of open-access remote sensing platforms has witnessed a plethora of multi-source, multi-perspective fine-spatial-resolution data for urban environments, among which very-high-resolution (VHR) top-down view remote sensing images and horizontal-perspective Google Street View (GSV) images are prominent exemplifiers. In this study, we aim to map individual-level abandoned houses across cities by developing a method that can effectively leverage VHR remote sensing and GSV images. The proposed method is composed of four steps. First, we explored the feasibility of the three most relevant and complementary remote sensing data for individual-level AH detection, i.e., daytime VHR images, nighttime light VHR images, and GSV images. Second, we extracted discriminative features that are indicative of housing abandonment conditions from the three disparate data sources. Third, we applied decision-level fusion with Dempster-Shafer Theory (DST) to better leverage the prior knowledge about data effectiveness. In the last step, a geographical random forests (GRF) model was first implemented to improve the predictions of where houses were occluded on GSV images. We mapped individual AH in two typical U.S. shrinking cities, Buffalo, NY, and Cleveland, OH, which allowed us to further explore the individual-property-level spatial characteristics of AH. Results revealed that the proposed DST fusion and GRF prediction consistently achieved promising performance across the two cities. Given the merits of incorporating open-access and multi-perspective data, our proposed method has the potential to be generalized to understanding regional and national-scale urban environments tackling housing abandonment challenges. Numéro de notice : A2022-788 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.103018 Date de publication en ligne : 18/09/2022 En ligne : https://doi.org/10.1016/j.jag.2022.103018 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101894 [article]

Registration of aerial imagery and lidar data in desert areas using the centroids of bushes as control information / Na Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 8 (August 2013)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 79 n° 8 (August 2013) . - pp 743 - 752 Titre : Registration of aerial imagery and lidar data in desert areas using the centroids of bushes as control information Type de document : Article/Communication Auteurs : Na Li, Auteur ; Xianfeng Huang, Auteur ; Fan Zhang, Auteur ; Le Wang, Auteur Année de publication : 2013 Article en page(s) : pp 743 - 752 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] appariement de données localisées [Termes IGN] brousse [Termes IGN] centroïde [Termes IGN] désert [Termes IGN] données lidar [Termes IGN] Gobi, désert du [Termes IGN] image aérienne [Termes IGN] Ransac (algorithme) [Termes IGN] semis de points Résumé : (Auteur) Geometric registration of multiple-source data is of great value for fusion processing and is very beneficial for the research of desert ecosystems. A lidar point cloud and optical image are two typical data that need to be integrated for data assimilation and information retrieval. This paper aims to solve the registration problem of aerial imagery and airborne lidar data in desert areas where traditional registration methods have difficulties in identifying registration primitives. In many deserts, such as the Sahara in Africa and Gobi in China, we observe that there are unevenly distributed desert bushes, which can be used as cues for registration. In this paper, we propose a registration approach using the centroids of bushes as registration primitives. This approach employs similar triangles created from both centroids as the evidence for matching and verifies the registration by the RANSAC algorithm. Experiments using data taken from the Dunhuang Gobi Desert in China show the registration surface model visually, and at the same time quantifies the deviation error, which corroborates that the proposed registration method is effective and feasible in desert areas. Numéro de notice : A2013-427 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.79.8.731 En ligne : https://doi.org/10.14358/PERS.79.8.731 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32565 [article]