The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 353–359, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-353-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 353–359, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-353-2021

  28 Jun 2021

28 Jun 2021

MAGO APPROACH FOR SEMANTIC SEGMENTATION: THE CASE STUDY OF UAVID BENCHMARK DATASET

S. Gagliolo and D. Sguerso S. Gagliolo and D. Sguerso
  • Geomatics Laboratory, Department of Civil, Chemical and Environmental Engineering (DICCA), University of Genoa, Italy

Keywords: Semantic segmentation, Machine learning, UAV photogrammetry, Point cloud

Abstract. The present work is focused on a semantic segmentation strategy implemented in the workflow of the tool MAGO (standing for “Adaptive Mesh for Orthophoto Generation”), considering the contribution of the 3D geometry and the colour information, both deriving from the point cloud of the scene. Moreover, the 2D source imagery, previously used to obtain the photogrammetric point cloud, is employed even to enhance the procedure with the recognition of moving objects, comparing the evolution of epochs.

The analysed context is an urban scene, deriving from the UAVid dataset proposed for the ISPRS benchmark. In particular, the so-called “seq18”, a set of high-resolution oblique images taken by UAV (Unmanned Aerial Vehicle), has been used to test the semantic segmentation. The workflow includes the production of two Digital Surface Models (DSMs), containing the geometric and radiometric information, respectively, and their processing by means of the Harris corner detector, allowing the understanding of the image variability. Then, starting from the source geometry and colour information and combining them with their variability mapping, a preliminary classification is performed. Further criteria allow the segmentation of the humans and cars present in the scene. In particular, static objects are identified according to the content of the neighbour pixels in a certain kernel, while the evolution in time of moving elements is recognized by means of the comparison of the projected images belonging to the different epochs. The presented preliminary achievements show some criticalities that require further attention and improvement. In particular, the strategy could be enriched getting more information from the source 2D images, which at the moment are directly used only for the comparison of consecutive epochs.