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About:
Landslides are common geological phenomena in high-relief areas, occurring across a range of spatial and temporal scales, and are considered the main erosive process in the geomorphological evolution of such areas.
When occurring close to inhabited zones their destructive effects cannot be underestimated, as well as their capacity to modify the landscape and the huge amount of debris carried to streams and rivers, which may impact water distribution and the environment.
We have a continuous interest in landslides, with students working on their characterization from a morphological point of view with high-resolution DTMs, recognition and mapping with orbital mutispectral imagery, and evaluating the risk imposed by such events to urban settlements.
3D model of landslides in São Sebastião, SP
Photos (blue rectangles) takes with a drone and used to generate the 3D model. São Sebastião, SP
High-resolution LiDAR DEMs (DSM / DTM)
Midia Coverage:
Publications:
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Grohmann, C.H., Garcia, G.P.B., Viana, C.D., Dias, H.C., Gramani, M.F., Santos, L.F., Sousa, A.M., Soares, L.P., & Coelho, R.D., 2025. Landslide 3D reconstruction and monitoring using oblique and nadiral drone aerial imagery. In Earth Observation Applications to Landslide Mapping, Monitoring and Modeling (pp. 199–214). Elsevier. https://doi.org/10.1016/b978-0-12-823868-4.00010-6 (paywalled)
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Coelho R.D., Viana C.D., Dias V.C., Grohmann C.H. 2024. Landslides of the 2023 summer event of São Sebastião, southeastern Brazil: spatial dataset. Brazilian Journal of Geology, 54(2):e20240006. https://doi.org/10.1590/2317-4889202420240006 (open access)
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Alvioli, M., Loche, M., Jacobs, L., Grohmann, C. H., Abraham, M. T., Gupta, K., Satyam, N., Scaringi, G., Bornaetxea, T., Rossi, M., Marchesini, I., Lombardo, L., Moreno, M., Steger, S., Camera, C., Bajni, G., Samodra, G., Wahyudi, E. E., Susyanto, N., et al. 2024. A benchmark dataset and workflow for landslide susceptibility zonation. Earth-Science Reviews, 104927. https://doi.org/10.1016/j.earscirev.2024.104927
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Dias, H. C., Grohmann, C. H., 2024. Standards for shallow landslide identification in Brazil: Spatial trends and inventory mapping. Journal of South American Earth Sciences, 135:104805. http://dx.doi.org/10.1016/j.jsames.2024.104805 (paywalled)
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Dias, H. C., Hölbling, D., Dias, V. C., Grohmann, C. H. 2023. Application of Object-Based Image Analysis for Detecting and Differentiating between Shallow Landslides and Debris Flows. GI_Forum, 11: 34–44. https://doi.org/10.1553/giscience2023_01_s34 (open access)
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Dias, H. C., Hölbling, D., Grohmann, C.H., 2023. Rainfall-Induced Shallow Landslide Recognition and Transferability Using Object-Based Image Analysis in Brazil. Remote Sensing, 15: 5137. https://doi.org/10.3390/rs15215137 (open access)
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Dias, V. C. and Dias, H. C., Grohmann, C. H., 2023. Rainfall-induced debris flows and shallow landslides in Ribeira Valley, Brazil: Main characteristics and inventory mapping. 8th International Conference on Debris Flow Hazard Mitigation (DFHM8) http://dx.doi.org/10.1051/e3sconf/202341505003 (open access)
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Garcia, G.P.B., Soares, L.P., Espadoto, M., Grohmann, C.H., 2023. Relict landslide detection using deep-learning architectures for image segmentation in rainforest areas: a new framework. International Journal of Remote Sensing, 44(7): 2168–2195. http://dx.doi.org/10.1080/01431161.2023.2197130 (paywalled) / Preprint available at arXiv:2208.02693 (open access)
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Garcia, G.P.B., Gomes, E., Viana, C.D., Grohmann, C.H., 2022. Comparing Terrestrial Laser Scanner and RPA-based Photogrammetry to Generate a Landslide DEM. Bulletin of Geodetic Sciences, 28(03). https://doi.org/10.1590/s1982-21702022000300016 (open access)
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Sousa, A. M., Viana, C. D., Garcia, G. P. B., & Grohmann, C. H. 2023. Monitoring Geological Risk Areas in the City of São Paulo Based on Multi-Temporal High-Resolution 3D Models. Remote Sensing, 15(12). http://dx.doi.org/10.3390/rs15123028 (open access)
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Soares, L.P., Dias, H.C., Garcia, G.P.B., Grohmann, C.H., 2022 - Landslide Segmentation with Deep Learning: Evaluating Model Generalization in Rainfall-Induced Landslides in Brazil. Remote Sensing, 14(9):2237. https://doi.org/10.3390/rs14092237 (open access)
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Xu, G., Wang, Y., Wang, L., Soares, L.P., & Grohmann, C.H., 2022. Feature-based constraint deep CNN method for mapping rainfall-induced landslides in remote regions with mountainous terrain: An application to Brazil. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15. https://doi.org/10.1109/JSTARS.2022.3161383
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Meena, S.R., Soares, L.P., Grohmann, C.H., Westen, C., Bhuyan, K., Singh, R.P. - Landslide detection in the Himalayas using machine learning algorithms and U-Net. Landslides https://doi.org/10.1007/s10346-022-01861-3 (open access)
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Dias, H.C., Sandre, L.H., Alarcón, D.A.S., Grohmann, C.H., Quintanilha, J.A. - Landslide recognition using SVM, Random Forest, and Maximum Likelihood classifiers on high-resolution satellite images: A case study of Itaóca, southeastern Brazil. Brazilian Journal of Geology, 51(4):839-852. http://dx.doi.org/10.1590/2317-4889202120200105 (open access)
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Dias, H.C., Hölbling, D., Grohmann, C.H., 2021 - Landslide Susceptibility Mapping in Brazil: A Review. Geosciences, 11(10):425. https://doi.org/10.3390/geosciences11100425 (open access)
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Dias, H.C., Gramani, M.F., Grohmann, C.H., Bateira, C., Vieira, B.C., 2021 - Statistical-based shallow landslide susceptibility assessment for a tropical environment: a case study in the southeastern coast of Brazil. Natural Hazards https://doi.org/10.1007/s11069-021-04676-y
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Dias, H.C., Hölbling, D., Grohmann C.H., 2021. Landslide inventory mapping in Brazil: Status and challenges. In: XIII International Symposium on Landslides, Cartagena, Colombia. (link)
[video ] - Short video of a field trip, using drone to map a landslide
Datasets:
A list of all our datasets is available in the Datasets Page
Spammers:
Post-Doc
Gabriella Labate Frugis (2025-??) - Surface analysis for geological risk monitoring on São Sebastião Island, Ilhabela – SP, using high-resolution laser altimetry (LiDAR) models
Vivian Cristina Dias (2022-2024) - Mapping and classification of watersheds affected by hydro geomorphological processes in the Serra do Mar Paulista: foundation for planning and mitigation actions
DSc
Lucas Pedrosa Soares (DSc 2025-??) - Generative Models in Remote Sensing: A Hybrid Approach with Synthetic and Real Images for Landslide Scar Segmentation
Rebeca Durço Coelho (DSc 2021-2025) - Multi-Scale Geomorphometric Analysis of Mass Movements in São Sebastião (SP, Brazil)
Helen Cristina Dias (DSc 2019-2025) - Analysis of manual and semi-automatic shallow landslides inventories and its suitability in predictive models
Guilherme Pereira Bento Garcia (DSc 2018-2023) - Detection and monitoring of landslides on natural slopes from integrated application of multispectral images and high-resolution elevation models
MSc
Amanda Mendes de Sousa (MSc 2019-2023) - Monitoring landslide susceptibility: using UAVs to generate 3D models for slope stability analysis in urban areas
Lucas Pedrosa Soares (MSc 2020-2022) - Automatic segmentation of landslide scars in remote sensing imagery with deep learning
Luiz Fernando dos Santos (MSc 2017-2020) - Application of High-resolution Digital Terrain Models in slope stability and mass movement analysis
Undergrad
Enzo Franceschi Genesi (2024) - Preparation of an inventory of the 1967 landslides in Caraguatatuba based on aerial photos and digital photogrammetry
Laura Selvati do Patrocinio Justiniano (2024) - Temporal analysis of landslides in São Sebastião - SP: Application of the Structure from Motion technique - Multi View Stereo
Laine Melo de Carvalho (2023) - Influence of slope and slope of the land on susceptibility to landslides in housing centers of RMSP
Francesco Barale (2019) - Modelling of a small landslide with Terrstrial Laser Scanner
Elton Barbosa Gomes (2018) - Stability analysis of mass movement-prone areas based on High-resolution Digital Terrain Models
Vitor Batista dos Santos (2018) - Analysis and 3D modelling of landslides by terrestrial LiDAR and Structure-from-Motion
Grants:
FAPESP #2023/11197-1 - Multi-Scale Geomorphometric Analysis of Mass Movements in São Sebastião (SP, Brazil).
FAPESP #2022/04233-9 - Mapping and classification of watersheds affected by hydro geomorphological processes in the Serra do Mar Paulista: foundation for planning and mitigation actions. (Post-Doc scholarship to Vivan C. Dias)
CNPq #311209/2021-1 - Artificial Intelligence and High-Resolution Remote Sensing applied to the study of mass movements, geological risks and coastal habitats.
FAPESP #2019/26568-0 - High-Resolution Remote Sensing, Deep Learning and Geomorphometry in Analyses of Mass Movements and Geological Risk.
FAPESP #2019/17261-8 - Analysis of manual and semi-automatic shallow landslides inventories and its suitability in predictive models. (DSc scholarship to Helen C. Dias)
FAPESP #2019/17555-1 - Automatic segmentation of landslide scars in remote sensing imagery with deep learning. (MSc scholarship to Lucas P. Soares)
CNPq #423481/2018-5 - UAV-based high-resolution imaging in geological, geomorphological and environmental analysis.
FAPESP #2016/06628-0 - Application of high-resolution digital elevation models in geology and geomorphology.
Collabs:
IPT – Institute for Technological Research
Z_GIS - Centre of Competence for Geoinformatics (Un. Salzburg)