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Near real-time building damage mapping is an indispensable prerequisite for governments to make decisions for disaster relief. With high-resolution synthetic aperture radar (SAR) systems, such as TerraSAR-X, the provision of such products in a fast and effective way becomes possible. In this letter, a deep learning-based framework for rapid regional tsunami damage recognition using post-event SAR imagery is proposed. To perform such a rapid damage mapping, a series of tile-based image split analysis is employed to generate the data set. Next, a selection algorithm with the SqueezeNet network is developed to swiftly distinguish between built-up (BU) and nonbuilt-up regions. Finally, a recognition algorithm with a modified wide residual network is developed to classify the BU regions into wash away, collapsed, and slightly damaged regions. Experiments performed on the TerraSAR-X data from the 2011 Tohoku earthquake and tsunami in Japan show a BU region extraction accuracy of 80.4% and a damage-level recognition accuracy of 74.8%, respectively. Our framework takes around 2 h to train on a new region, and only several minutes for prediction.

The integrated use of remote sensing imagery and hydro-geophysical field surveys is a well-established approach to map the hydrogeological framework, and thus explore and evaluate the groundwater potentiality of desert lands, where groundwater is considered as the main source of freshwater. This study uses such integrated approach to map the groundwater potentiality of the desert alluvial floodplain of the Nile Valley west of Qena, Egypt, as alternative water source to the River Nile. Typically ground gradient, faults and their stress field, lateral variation of rock permeability, drainage patterns, watersheds, rainfall, lithology, and soil types are the main factors believed to affect the groundwater recharge and storage from the infiltration of present-time and paleo-runoff. Following this generally accepted approach, different remote sensing data sets (SRTM DEM, Landsat-8, ALOS/PALSAR-1, Sentinel-1, and TRMM) as well as auxiliary maps (geological and soil maps) were used to identify and map these factors and prepare thematic maps portraying the different influences they exert on the groundwater recharge. These thematic maps were overlaid and integrated using weights in a GIS framework to generate the groundwater potentiality map which categorizes the different recharge capabilities into five zones. Moreover, the aeromagnetic data were processed to map the deep-seated structures and estimate the depth to basement rocks that may control the groundwater occurrence. In addition, the vertical electrical sounding (VES) measurements were applied and calibrated with the available borehole data to delineate the subsurface geological and hydrogeological setting as well as the groundwater aquifers. Different geoelectric cross-sections and hydro-geophysical maps were constructed using the borehole information and VES interpretation results to show the lateral extension of the different lithological units, groundwater-bearing zones, water table, and the saturated thickness of the aquifer. The GIS model and geophysical results show that the southwest part of Nag’a Hammadi-El-Ghoneimia stretch has very high recharge and storage potentiality and is characterized by the presence of two groundwater-bearing zones. The shallow groundwater aquifer is located at a depth of 30 m with a saturation thickness of more than 43 m. However, there are NW–SE faults crossing the study area and most likely serve as recharge conduits by connecting the shallow aquifer with the deeper ones. Such aquifers connection has been confirmed by investigating the chemical and isotopic composition of their groundwater.

The results of a comparative investigation of lineament mapping from stereoscopic Large Format Camera (LFC) photographs and from SIR-C L- and C-band Synthetic Aperture Radar (SAR) data are described. The lineament patterns are used to test a model developed by Koch (1993) of groundwater flow in the arid Red Sea Hills area of Sudan. Initial results show that the LFC imagery is most useful for mapping detailed fracture patterns while the combination of the L- and C-bands of the SIR-C Synthetic Aperture Radar is helpful in the location of major deep-seated fracture zones. The lineament patterns derived from LFC and SIR-C show strong N-S and E-W orientations, which correspond to the current tectonic configuration of the Red Sea rift system. The longer E-W trending features may be important from a regional hydro-geological point of view, whereas the shorter features are significant in that they interconnect the major faults.