In Israel, almost 30,000 recorded sites form a long record of human presence in the area, presenting one of the densest national archaeological records in the world. While some sites are still clearly visible on the surface, most ancient remains of various ages and origins occur in the subsurface layers at depths of 0.5-8 m (usually in multi-layered archaeological sites). Over the years, various efforts have been made to utilize surface geophysical techniques (e.g., GPR, ERT, magnetic, paleomagnetic, subsurface seismic, self-potential, thermal, VLF, induced polarization, piezoelectric, and microgravity) as rapid, efficient, and non-invasive alternatives to conventional archaeological survey methods for the identification of archaeological remains. Meanwhile, these attempts have not always been successful, mainly because of the environmental variability and complex physical-archaeological conditions. Remote Sensing (RS) is a low-cost tool used for detecting and monitoring the physical attributes of objects of interest on or below the Earth's surface from a considerable distance. The increasing interest of the archaeological community in RS can be attributed to the rapid data acquisition, extensive coverage, high resolution, and spectral sensitivity of RS in detecting anomalies associated with the surface, subsurface, buried, and underwater archaeological features. Our recent application of advanced Remote Sensing (RS) techniques in Israel has led to the identification of dozens of previously unknown archaeological targets in northern Israel: Wadi Asekt, Rujm el-Hiri, around the Lake Kinneret, and southern Israel: Biq'at Sayyarim. We examine the topological characteristics of these sites and explore the connections between the objects, shedding light on the inhabitants' lifestyle of these regions 3,000 to 6,000 years ago. Detailed surface geophysical surveys and archaeological investigations will be conducted in the selected areas in the next phase. Information theory approaches, and modern wavelet methodologies will be employed to numerically integrate the RS data with geophysical (potentially geochemical and geomorphological) methods.