Automatic landslide mapping is crucial for a fast response in a disaster scenario and improving landslide susceptibility models. Recent studies highlighted the potential of deep learning methods for automatic landslide segmentation. However, only a few works discuss the generalization capacity of these models to segment landslides in areas that differ from the ones used to train the models. In this study, we evaluated three different locations to assess the generalization capacity of these models in areas with similar and different environmental aspects. The model training consisted of three distinct datasets created with RapidEye satellite images, Normalized Vegetation Index (NDVI), and a digital elevation model (DEM). Here, we show that larger patch sizes (128 × 128 and 256 × 256 pixels) favor the detection of landslides in areas similar to the training area, while models trained with smaller patch sizes (32 × 32 and 64 × 64 pixels) are better for landslide detection in areas with different environmental aspects. In addition, we found that the NDVI layer helped to balance the model’s results and that morphological post-processing operations are efficient for improving the segmentation precision results. Our research highlights the potential of deep learning models for segmenting landslides in different areas and is a starting point for more sophisticated investigations that evaluate model generalization in images from various sensors and resolutions.
This work presents a 1:10,000 geomorphological mapping of an area in southeastern Brazil, based on morphometric analysis of Digital Elevation Models (DEMs), while classical methods focus on photo interpretation. Data derived from the DEM include elevation, slope gradient, slope aspect, vertical and horizontal curvatures, amplitude, elongation and wavelength of landforms. These parameters were used along with slope shape and drainage patterns to classify the landforms according to the Land Systems method, in which portions of the landscape that presents similar terrains attributes are grouped from regional (low detail) to local (high detail) scales, respectively, Land Systems, Land Units and Land Elements. The São Paulo State geomorphological map at 1:1,000,000 scale is considered the best reference source, and was compared with the results obtained in this project. Two Land Systems, four Land Units and twelve Land Elements were identified in the study area. In this area, karst terrains are common and easily identified due their characteristics drainage patterns, amplitude and slope gradient. Karst terrain boundaries defined in this study do overlap with those defined in the state map, however the morphometric analysis allowed a better description of the terrain attributes used to define them. The terrain attributes derived automatically from the DEM enabled an accurate geomorphological classification of the study area. The methodology presented in this paper is considered effective for mapping landforms at a detailed scale and can be employed in regional scale mapping using coarser resolution DEMs.
This work presents the development of a three-dimensional (3D) model of an outcrop of the Corumbataí Formation (Permian, Paraná Basin, Brazil) using Structure from Motion -Multi-View Stereo (SfM-MVS) technique in order to provide a structural analysis of clastic dikes cutting through siltstone layers. While traditional photogrammetry requires the user to input a series of parameters related to the camera orientation and its characteristics (such as focal distance), in SfM-MVS the scene geometry, camera position and orientations are automatically determined by a bundle adjustment, an iterative procedure based on a set of overlapping images. It is considered a low-cost technique in terms of hardware and software, also being able to provide point density and accuracy on par to the ones obtained withTerrestrial Laser Scanning. The results acquired on this research have good agreement with previous works, yielding a NNW main orientation for the dikes measured in the field and on the 3D model. The development of this work showed that SfM-MVS use and practice on geosciences still needs more studies on the optimization of the involved parameters (such as camera orientation, image overlap and angle of illumination), which, when accomplished, will result in less processing time and more accurate models.
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