With the widespread use of SfM (Structure from Motion) in aerial surveys, it is often applied to images covering shallow water (e.g., rivers, coral reefs). The commonly used SfM softwares ignore the light refraction at the water surface (i.e., enforce one-media collinearity condition). The simplification affects not only the triangulation of submerged points but the estimation of camera parameters by using them. This study examined the latter effect through numerical experiments under simple conditions. As a result, both the intrinsic and extrinsic parameters were found to be affected, depending on water depth and its heterogeneity. The intrinsic parameters did not simply reflect the apparent “magnification” and “pin-cushion distortion” of the bottom image observed from the air. The errors generated in the extrinsic parameters were sometimes large enough to cause a systematic overestimation of water depth by more than 40%. Furthermore, the errors did not diminish even when the intrinsic parameters were fixed to true values. These results show the need for more attention to the effect.

Efficient road edge extraction from point clouds acquired by Mobile Mapping System (MMS) is an important task because the road edge is one of the main elements of high definition maps. In this paper, we present a scanline-based road edge extraction method using a bend angle of scanlines from MMS point clouds. Scanline-based methods generally have advantages in that computational cost is low, it is easy to extract accurate road edges, and they are independent of driving speed of MMS compared to methods using unorganized points. In contrast, the extraction accuracy becomes low at curb cuts, intersections, and small occlusion parts from weeds and fallen leaves. In order to resolve these problems, we present a scanline-based road edge extraction method by calculation of bend angle by using filtered point clouds and tracking based on the bend angle of scanlines and smoothness of road edges. In the experiments, our proposed methods achieved a completeness, a correctness, and a quality of 92.9% to 99.5%, and accuracy of 10.5 mm to 19.6 mm in total. In addition, our proposed methods resulted in makes robustness against slight occlusions around curbs and specifications of MMS.

For the Beirut explosion site in August 2020, we investigated whether damage could be identified from Sentinel1 data. We selected data from February 20 and June 7, 2020 as pre-explosion data, and data from August 6 as post-explosion data, and conducted interferometric SAR processing. We checked VH, VV and the phase changes values for the explosion site, areas heavily affected by the explosion, and areas almost unaffected by the explosion, and it was found that the VH and VV values for the explosion site showed clearer changes, while the phase changes value for the collapse of surrounding buildings were larger.