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Monitoring and mapping complex urban features (e.g. roads and buildings) from remotely sensed data multispectral and hyperspectral has gained enormous research interest. Accurate ground truth allows for high quality assessment of classified images and to verify the produced map. Ground truth can be acquired from: field using the handheld Global Positioning System (GPS) device and from Images with high resolution extracted from Google Earth in additional to field. Ground truth or training samples could be achieved from VHR satellite images such as QuickBird, Ikonos, Geoeye-1 and Wordview images. Archived images are costly for researchers in developing countries. Images from GE with high spatial resolution are free for public and can be used directly producing large scale maps, in producing LULC mapping and training samples. Google Earth (GE) provides free access to high resolution satellite imagery, but is the quality good enough to map urban areas. Costal of the Red sea, Marsa Alam could be mapped using GE images. The main objective of this research is exploring the accuracy assessment of producing large scale maps from free Google Earth imagery and to collect ground truth or training samples in limited geographical extend. This research will be performed on Marsa Alam city or located on the western shore of the Red Sea, Red sea Governorate, Egypt. Marsa Alam is located 274 km south of Hurghada. The proposed methodology involves image collection taken into consideration the resolution of collected photographs which depend on the height of view. After that, image rectification using suitable rectification methods with different number and distributions of GCPs and CPs. Database and Geographic information systems (GIS) layers were created by on-screen vectorization based on the requirement of large scale maps. Attribute data have been collected from the field. The obtained results show that the planmetric accuracy of the produced map from Google Earth Images met map scale 10 000 according to (National Map Accuracy Standards).The collect ground truth or training samples from GE images and field help in accuracy assessment of classification process.
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