Mahmoud Salah

TL;DR: In this article, the authors developed automatic feature extraction from multispectral aerial images and lidar data for building detection using Grey Level Cooccurrence Matrix (GLCM), Normalized Difference Vegetation Indices (NDVI), and standard deviation of elevations and slopes.

TL;DR: De-solvated-β-Lg increased the heat-stability and bioavailability of AC, which could be further utilized in various food and pharmaceutical matrices, and recommend that β- Lg nanoparticles could be appropriated as delivery systems for anthocyanins.

TL;DR: The results show that using LiDAR data in the detection process improves the accuracy of feature detection by 14.9% compared with using aerial photography alone.

TL;DR: In this paper, the performance of three proposed empirical models for bathymetry calculations in three different areas: Alexandria port, Egypt, as an example of a low-turbidity deep water area with silt-sand bottom cover and a depth range of 10.5 m; the Lake Nubia entrance zone, Sudan, which is a highly turbid, unstable, clay bottom area with water depths to 6 m; and Shiraho, Ishigaki Island, Japan, a coral reef area with varied depths ranging up to 14 m.

TL;DR: In this paper, the authors developed automatic feature extraction from multispectral aerial images and lidar data based on test data from two different study areas with different characteristics, including buildings, trees, roads and grass.