摘要
Recent monitoring techniques employ multiple sources of information for the characterization of the phenomenon to be studied, being the coupling and adjustment of multi-source data one of the first challenges to consider and solve. The authors propose a new framework of the multi-source and multi-temporal data-oriented fusion for the characterization of landslide events. The main objective is to generate 3D virtual models(in the form of dense point clouds) and feed them back with the characteristic of soil and subsoil information. The scheme consists of three main steps. The first one is on-site data collection(geological characterization, geophysical measurements, GPS measurements, and UAV/drone mapping). The second step is generation of a high-resolution 3D virtual model(~1-inch spatial resolution) from the frames acquired through the UAV using the structure of motion(SfM) processing;the developed virtual model is optimized with GPS measurements to minimize geolocation error and eliminate distortions. The last step is assembling of the acquired data in the field and densified point cloud considering the different nature of the data, re-escalating procedure and the information stacking layer.
Recent monitoring techniques employ multiple sources of information for the characterization of the phenomenon to be studied, being the coupling and adjustment of multi-source data one of the first challenges to consider and solve. The authors propose a new framework of the multi-source and multi-temporal data-oriented fusion for the characterization of landslide events. The main objective is to generate 3D virtual models(in the form of dense point clouds) and feed them back with the characteristic of soil and subsoil information. The scheme consists of three main steps. The first one is on-site data collection(geological characterization, geophysical measurements, GPS measurements, and UAV/drone mapping). The second step is generation of a high-resolution 3D virtual model(～1-inch spatial resolution) from the frames acquired through the UAV using the structure of motion(SfM) processing; the developed virtual model is optimized with GPS measurements to minimize geolocation error and eliminate distortions. The last step is assembling of the acquired data in the field and densified point cloud considering the different nature of the data, re-escalating procedure and the information stacking layer.
基金
supported by the CONACYT Academic Fellowship(No.308896)
