• Mrs Jhansi Rani
• B Ashok

Previous applications of machine learning in remote sensing for the identification of damaged buildings in the aftermath of a large-scale disaster have been successful. However, standard methods do not consider the complexity and costs of compiling a training data set after a large-scale disaster. In this Project, study disaster events in which the intensity can be modeled via numerical simulation and/or instrumentation. For such cases, two fully automatic procedures for the detection of severely damaged buildings are introduced. The fundamental assumption is that samples that are located in areas with low disaster intensity mainly represent non damaged buildings. Furthermore, areas with moderate to strong disaster intensities likely contain damaged and non damaged buildings. Under this assumption, a procedure that is based on the automatic selection of training samples for learning and calibrating the standard support vector machine classifier is utilized. The second procedure is based on the use of two regularization parameters to define the support vectors. These frameworks avoid the collection of labeled building samples via field surveys and/or visual inspection of optical images, which requires a significant amount of time.

Building, Disaster, Support vector machine, parameters, calibration

"Disaster Intensity Based Selection of Training samples for Remote Sensing Building Damage Classification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 9, page no.g267-g274, September-2023, Available :http://www.jetir.org/papers/JETIR2309647.pdf

"Disaster Intensity Based Selection of Training samples for Remote Sensing Building Damage Classification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 9, page no. ppg267-g274, September-2023, Available at : http://www.jetir.org/papers/JETIR2309647.pdf