The establishment of a disaster data classification scheme has supported this analysis and has led to the development of standards in information production following any disaster.
In this paper we discuss the technique of disaster recovery (DR) and classification of disaster tolerance (DT).
Teaching resources for the SAILORS 2017 NLP research project: Tweet Classification for Disaster Relief.
The weight estimation and classification of disaster factors are based on the K-means model to build the hillslope prediction model.
The lack of a universal classification of disasters also means that recording of health data in disasters is not standardized.
As a result, crude classification schemes for disasters have been incorporated into the discipline of disaster epidemiology.
Typical application areas include environment monitoring, community healthcare, surveys with embedded geotagged photos [5], traffic monitoring and transportation planning, garbage classification, infrastructure management, disaster management, public safety and so on [1, 6, 7].
Information on type, extent, area, and other attributes, can be obtained for arable land, orchards, forests, and grasslands from the Second National Land Survey data, together with information on crop and forest types from the classification of pre-disaster high-resolution remote sensing images.
Underwater multimedia sensor networks enable several new applications, such as target tracking, advanced coastal multimedia surveillance, undersea explorations, image acquisition and classification, environmental monitoring, and disaster prevention.
In addition, the previously developed MEDTOC system is modified and enhanced to include location-aware features at the disaster site, as well as quick classification and assignment of patients to nearby hospitals.
