Published Paper Details:

Object detection has increasing attention within the field of remote sensing image analysis. Advance background, vertical views and variation in target kind and size in remote sensing images create object detection difficult task. Also, road is the important object for detecting and extracting using high resolution satellite image. As road plays important in geographic information system. In urban space road is essential infrastructure connecting different areas and also play important role in human civilization. Detection and extraction of road from aerial and satellite imaginary has gained and increasing attention. Road network has key role in development of transportation, road navigation, unnamed vehicle, geodata update, land use, land cover analysis and urban planning. Due to natural disaster like flood, earthquake it is necessary to check the quality of the road. It required large amount of time to check quality of road manually and it generate error. So, developing new method to road network from high resolution remote sensing images would be beneficial to geographical information system (GIS) and intelligent transportation system (ITS). With Development of Remote sensing technology, high resolution satellite image improve and obtain easily from which road can locate road accurately. Road recognition from remote sensing imaginary has two part: first is road detection and second is road extraction. This study aim to understand the deep learning and machine learning based method with the help of high resolution satellite image for detection and extraction of road.