Smart Land Record Application Using Web GIS and GPS

Bhatti, Muhammad Shahid; Ajmal, Muhammad; Saeed, Atif; Ahmed, Manzoor; Khalid, Rizwan; Arshad, Nouman

doi:10.1007/978-3-642-36981-0_83

Cited by 2 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Following our literature review, it was found that GPS was utilized in many applications, including both military and civilian ones. Examples worth highlighted here are logistic tracking (Banachowicz et al 2014;Liu 2016), navigation (Lim et al 2017), tracking of elderly person (Alem an et al 2015), and expedition (Xu 2012;Bhatti et al 2013). GPS also plays a crucial role in disaster management, particularly in locating the affected areas.…”

Section: Global Positioning Systemmentioning

confidence: 99%

Internetworking flood disaster mitigation system based on remote sensing and mobile GIS

Puttinaovarat

Horkaew

2020

Geomatics, Natural Hazards and Risk

View full text Add to dashboard Cite

Flood verification and mitigation based on ground survey has been impeded by many factors. For instance, damage assessment exclusively by onsite expedition could be delayed by restricted access, especially during heavy flooding. To devise appropriate mitigation measures, resources required to thoroughly investigate the incidents in response to disaster could be overwhelming. Moreover, inadequate equipment and non-standardized flood reporting process could also undermine the maneuver. As a consequent, rescue mission was inevitably deferred until the reported incidents had been confirmed. To address these issues, this paper presents an internetworking system for assisting flooding disaster mitigation. Its main contributions were two folds. Firstly, its data intelligent was driven by convergence of both user and official reports of flood incidents and those automatically detected from remotely sensed images. Secondly, the reliability of reported information was ensured by two-factor verification, i.e. by using a state-of-the-art deep learning strategy and by official investigations. The verified incidents were then presented in augmented graphical reports, which were practical in public inquiry and official uses in devising efficient and cost-effective disaster mitigation measures. The developed system was deployed in different cities, most affected by recent floods. Numerical and subjective assessments herein demonstrate the merits of the system.

show abstract