Towards an adaptable LiDAR data compression system / Ahmed Saad Ismail Kotb ; Supervised Hesham A. Hassan , Safaa M. Hassan
Material type: TextLanguage: English Publication details: Cairo : Ahmed Saad Ismail Kotb , 2019Description: 116 Leaves : charts , facsimiles ; 30cmOther title:- نحو نظام مكيف لضغط بيانات ماسح الليزر الضوئى [Added title page title]
- Issued also as CD
Item type | Current library | Home library | Call number | Copy number | Status | Date due | Barcode | |
---|---|---|---|---|---|---|---|---|
Thesis | قاعة الرسائل الجامعية - الدور الاول | المكتبة المركزبة الجديدة - جامعة القاهرة | Cai01.20.03.M.Sc.2019.Ah.T (Browse shelf(Opens below)) | Not for loan | 01010110080680000 | |||
CD - Rom | مخـــزن الرســائل الجـــامعية - البدروم | المكتبة المركزبة الجديدة - جامعة القاهرة | Cai01.20.03.M.Sc.2019.Ah.T (Browse shelf(Opens below)) | 80680.CD | Not for loan | 01020110080680000 |
Browsing المكتبة المركزبة الجديدة - جامعة القاهرة shelves Close shelf browser (Hides shelf browser)
No cover image available | No cover image available | No cover image available | No cover image available | No cover image available | No cover image available | No cover image available | ||
Cai01.20.03.M.Sc.2019.Ah.J Job execution framework on distributing environment / | Cai01.20.03.M.Sc.2019.Ah.J Job execution framework on distributing environment / | Cai01.20.03.M.Sc.2019.Ah.T Towards an adaptable LiDAR data compression system / | Cai01.20.03.M.Sc.2019.Ah.T Towards an adaptable LiDAR data compression system / | Cai01.20.03.M.Sc.2019.Ay.T A technique for semantic based mashup for internet of things (IoT) / | Cai01.20.03.M.Sc.2019.Ay.T A technique for semantic based mashup for internet of things (IoT) / | CaI01.20.03.M.Sc.2019.Ge.D Detection of mass panic using internet of things and machine learning / |
Thesis (M.Sc.) - Cairo University - Faculty of Computers and Artificial Intelligence - Department of Computer Science
Light Detection and Ranging (LiDAR) scanning system is an attractive active remote sensing system that uses a laser beam to obtain precise and directly geo-referenced spatial three-dimensional (3D) data about the shape and surface characteristics of the earth. LIDAR systems are capable of rapidly collecting a massive amount of spatial data from large geographical areas with high precision and high density within a limited time (i.e., it can collect up to 200,000 points per second). As a result, the obtained datasets can contain billions of points that consume more than a gigabyte per square kilometer (GB/km2). consequently, significant problems had been issued such as high storage capacity, difficult data distribution to the users and huge time-consumed for data processing and display. To overcome these problems, LiDAR data compression has become an important issue for managing, analyzing and using these huge amounts of LiDAR data. Although there are many LiDAR data compression methods, there is no any adaptable compression method can achieve the proper compression efficiency in all cases of the provided data nature
Issued also as CD
There are no comments on this title.