The 2015 Gorkha earthquake was one of the most devastating earthquakes in Nepal in this century. Postdisaster reconstruction is a complex and highly challenging process, especially concerning developing countries like Nepal, as it involves several well-coordinated plans and working activities. Due to the large scale of devastation, the Nepal government created a separate body, the National Reconstruction Authority (NRA), to manage and oversee recovery and reconstruction activities. This paper briefly discusses organizations typically involved in postdisaster reconstruction processes and the multifaceted roles they play. Document analysis on research papers, collected reports from the NRA, governmental departments, and many other organizations involved in the reconstruction was carried out. This study provides a comprehensive review of the reconstruction process after the 2015 Gorkha, Nepal, earthquake, whereas previous studies have covered a specific issue. The reconstruction works are presented structurally in detail. Similarly, the crucial steps and lesson learned for managing expectations and building back better are highlighted. This study may help to develop effective organization and policies in future postdisaster reconstruction.

The detection of landslide areas and surface characteristics is the prerequisite and basis of landslide hazard risk assessment. The traditional method relies mainly on manual field identification, and discrimination is based on the lack of unified quantitative standards. Thus, the use of neural networks for the quantitative identification and prediction of landslide surface deformation is explored. By constructing an integrated model based on YOLO X-CNN and Mask R-CNN, a deep learning-based feature detection method for landslide surface images is proposed. First, the method superimposes Unmanned Aerial Vehicle (UAV) oblique photography data (UOPD) and Internet heterosource image data (IHID) to construct a landslide surface image dataset and landslide surface deformation database. Second, an integrated model suitable for small- and medium-scale target detection and large-scale target edge extraction is constructed to automatically identify and extract landslide surface features and to achieve rapid detection of landslide surface features and accurate segmentation and deformation recognition of landslide areas. The results show that the detection accuracy for small rock targets is greater than 80% and that the speed is 57.04 FPS. The classification and mask segmentation accuracies of large slope targets are approximately 90%. A speed of 7.89 FPS can meet the needs of disaster emergency response; this provides a reference method for the accurate identification of landslide surface features.

Kathmandu Valley lies in an active tectonic zone, meaning that earthquakes are common in the region. The most recent was the Gorkha Nepal earthquake, measuring 7.8 M_w. Past earthquakes caused soil liquefaction in the valley with severe damages and destruction of existing critical infrastructures. As for such infrastructures, the road network, health facilities, schools and airports are considered. This paper presents a liquefaction susceptibility map. This map was obtained by computing the liquefaction potential index (LPI) for several boreholes with SPT measurements and clustering the areas with similar values of LPI. Moreover, the locations of existing critical infrastructures were reported on this risk map. Therefore, we noted that 42% of the road network and 16% of the airport area are in zones of very high liquefaction susceptibility, while 60%, 54%, and 64% of health facilities, schools and colleges are in very high liquefaction zones, respectively. This indicates that most of the critical facilities in the valley are at serious risk of liquefaction during a major earthquake and therefore should be retrofitted for their proper functioning during such disasters.